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Global Wheat Challenge 2021

Submit with WILDS

WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities

By  etienne_david

WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications, from tumor identification to wildlife monitoring to poverty mapping. It focuses on training robust algorithm. You can try to get you first model trained with the toolbox following the notebook !



Global Wheat Competition 2021 - Starting notebook using the WILDS library

image.png

  • WILDS (https://github.com/p-lambda/wilds) is a PyTorch-based library for distribution shifts. It contains data loaders and evaluation functions for the Global Wheat Competition, as well as some examples of algorithms and models that you can build on.
  • The goal of this notebook is to help you get started with using the WILDS library to train and submit your first model!
  • Using WILDS is not necessary for participating in the competition. However, you might find a lot of the utilities and infrastructure in the WILDS library useful.
  • Before starting, please check in Edit / Notebook settings that "GPU" is selected

Download Aicrowd-cli 📚

The Aicrowd CLI enables making submissions directly via the notebook.

In [ ]:
!pip install aicrowd-cli
Collecting aicrowd-cli
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ERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.
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In [ ]:
### Please enter your API Key from [here](https://www.aicrowd.com/participants/me).
API_KEY = "" 
!aicrowd login --api-key $API_KEY
API Key valid
Saved API Key successfully!

Download the WILDS library

For more detailed installation instructions, please refer to the WILDS documentation.

In [ ]:
!pip uninstall torch torchvision torchaudio -y
!pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
!pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu101.html
!git clone https://github.com/p-lambda/wilds/

!cd wilds && git checkout dev && pip install -e .
!pip install transformers
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WARNING: Skipping torchaudio as it is not installed.
Looking in links: https://download.pytorch.org/whl/torch_stable.html
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ERROR: torchtext 0.9.1 has requirement torch==1.8.1, but you'll have torch 1.7.1+cu101 which is incompatible.
Installing collected packages: torch, torchvision, torchaudio
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Looking in links: https://pytorch-geometric.com/whl/torch-1.7.0+cu101.html
Collecting torch-scatter
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Installing collected packages: torch-scatter
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Cloning into 'wilds'...
remote: Enumerating objects: 2835, done.
remote: Counting objects: 100% (517/517), done.
remote: Compressing objects: 100% (119/119), done.
remote: Total 2835 (delta 437), reused 435 (delta 396), pack-reused 2318
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Resolving deltas: 100% (2114/2114), done.
Branch 'dev' set up to track remote branch 'dev' from 'origin'.
Switched to a new branch 'dev'
Obtaining file:///content/wilds
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Building wheels for collected packages: littleutils
  Building wheel for littleutils (setup.py) ... done
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Successfully built littleutils
ERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.
ERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.
Installing collected packages: pillow, littleutils, outdated, ogb, pytz, wilds
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  Running setup.py develop for wilds
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Collecting transformers
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Installing collected packages: sacremoses, huggingface-hub, tokenizers, transformers
Successfully installed huggingface-hub-0.0.8 sacremoses-0.0.45 tokenizers-0.10.2 transformers-4.6.0

Run the WILDS package

This sample command will train a FasterRCNN model with the Group DRO algorithm that tries to minimize the loss of the worst-case training domain.

To train using a different algorithm like standard empirical risk minimization (ERM), simply replace --algorithm groupDRO with --algorithm ERM.

WILDS will automatically handle downloading the Global Wheat Competition dataset to the location specified in root_dir. For the purposes of this competition, both the val and the test sets in the WILDS library do not have labels provided, so the reported val and test accuracies are just dummy values.

In [ ]:
N_EPOCHS=10
SAVE_TO="gdro"
In [ ]:
!python3 wilds/examples/run_expt.py -d globalwheat --algorithm groupDRO --root_dir data --log_dir $SAVE_TO --download --progress_bar --save_step 1 --split_scheme official --n_epochs $N_EPOCHS
2021-05-17 06:49:23.673279: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
Dataset: gwhd
Algorithm: groupDRO
Root dir: data
Split scheme: official
Dataset kwargs: {}
Download: True
Frac: 1.0
Version: None
Loader kwargs: {'num_workers': 1, 'pin_memory': True}
Train loader: standard
Uniform over groups: True
Distinct groups: True
N groups per batch: 4
Batch size: 4
Eval loader: standard
Model: fasterrcnn
Model kwargs: {'n_classes': 1, 'pretrained': True, 'pretrained_model': True, 'pretrained_backbone': True, 'min_size': 1024, 'max_size': 1024}
Train transform: image_base
Eval transform: image_base
Target resolution: None
Resize scale: None
Max token length: None
Loss function: fasterrcnn_criterion
Loss kwargs: {}
Groupby fields: ['location_date_sensor']
Group dro step size: 0.01
Coral penalty weight: None
Irm lambda: None
Irm penalty anneal iters: None
Algo log metric: None
Val metric: detection_acc_avg
Val metric decreasing: False
N epochs: 10
Optimizer: Adam
Lr: 1e-05
Weight decay: 0.0001
Max grad norm: None
Optimizer kwargs: {}
Scheduler: None
Scheduler kwargs: {}
Scheduler metric split: val
Scheduler metric name: None
Process outputs function: None
Evaluate all splits: True
Eval splits: []
Eval only: False
Eval epoch: None
Device: cuda:0
Seed: 0
Log dir: gdro
Log every: 50
Save step: 1
Save best: True
Save last: True
Save pred: True
No group logging: False
Use wandb: False
Progress bar: True
Resume: False

Downloading dataset to data/gwhd_v0.9...
You can also download the dataset manually at https://wilds.stanford.edu/downloads.
Downloading https://worksheets.codalab.org/rest/bundles/0x8ba9122a41454997afdfb78762d390cf/contents/blob/ to data/gwhd_v0.9/archive.tar.gz
100% 10279141376/10280247296 [08:55<00:00, 18152342.55Byte/s]Extracting data/gwhd_v0.9/archive.tar.gz to data/gwhd_v0.9
10280386560Byte [09:43, 17628119.31Byte/s]
It took 13.81 minutes to download and uncompress the dataset.
Train data...
    location_date_sensor = 0: n = 29
    location_date_sensor = 1: n = 60
    location_date_sensor = 2: n = 176
    location_date_sensor = 3: n = 20
    location_date_sensor = 4: n = 24
    location_date_sensor = 5: n = 448
    location_date_sensor = 6: n = 160
    location_date_sensor = 7: n = 60
    location_date_sensor = 8: n = 32
    location_date_sensor = 9: n = 82
    location_date_sensor = 10: n = 204
    location_date_sensor = 11: n = 30
    location_date_sensor = 12: n = 747
    location_date_sensor = 13: n = 66
    location_date_sensor = 14: n = 401
    location_date_sensor = 15: n = 588
    location_date_sensor = 16: n = 98
    location_date_sensor = 17: n = 432
    location_date_sensor = 18: n = 0
    location_date_sensor = 19: n = 0
    location_date_sensor = 20: n = 0
    location_date_sensor = 21: n = 0
    location_date_sensor = 22: n = 0
    location_date_sensor = 23: n = 0
    location_date_sensor = 24: n = 0
    location_date_sensor = 25: n = 0
    location_date_sensor = 26: n = 0
    location_date_sensor = 27: n = 0
    location_date_sensor = 28: n = 0
    location_date_sensor = 29: n = 0
    location_date_sensor = 30: n = 0
    location_date_sensor = 31: n = 0
    location_date_sensor = 32: n = 0
    location_date_sensor = 33: n = 0
    location_date_sensor = 34: n = 0
    location_date_sensor = 35: n = 0
    location_date_sensor = 36: n = 0
    location_date_sensor = 37: n = 0
    location_date_sensor = 38: n = 0
    location_date_sensor = 39: n = 0
    location_date_sensor = 40: n = 0
    location_date_sensor = 41: n = 0
    location_date_sensor = 42: n = 0
    location_date_sensor = 43: n = 0
    location_date_sensor = 44: n = 0
    location_date_sensor = 45: n = 0
    location_date_sensor = 46: n = 0
Validation data...
    location_date_sensor = 0: n = 0
    location_date_sensor = 1: n = 0
    location_date_sensor = 2: n = 0
    location_date_sensor = 3: n = 0
    location_date_sensor = 4: n = 0
    location_date_sensor = 5: n = 0
    location_date_sensor = 6: n = 0
    location_date_sensor = 7: n = 0
    location_date_sensor = 8: n = 0
    location_date_sensor = 9: n = 0
    location_date_sensor = 10: n = 0
    location_date_sensor = 11: n = 0
    location_date_sensor = 12: n = 0
    location_date_sensor = 13: n = 0
    location_date_sensor = 14: n = 0
    location_date_sensor = 15: n = 0
    location_date_sensor = 16: n = 0
    location_date_sensor = 17: n = 0
    location_date_sensor = 18: n = 12
    location_date_sensor = 19: n = 49
    location_date_sensor = 20: n = 254
    location_date_sensor = 21: n = 216
    location_date_sensor = 22: n = 89
    location_date_sensor = 23: n = 11
    location_date_sensor = 24: n = 4
    location_date_sensor = 25: n = 7
    location_date_sensor = 26: n = 14
    location_date_sensor = 27: n = 17
    location_date_sensor = 28: n = 14
    location_date_sensor = 29: n = 8
    location_date_sensor = 30: n = 43
    location_date_sensor = 31: n = 55
    location_date_sensor = 32: n = 51
    location_date_sensor = 33: n = 50
    location_date_sensor = 34: n = 28
    location_date_sensor = 35: n = 75
    location_date_sensor = 36: n = 56
    location_date_sensor = 37: n = 34
    location_date_sensor = 38: n = 55
    location_date_sensor = 39: n = 13
    location_date_sensor = 40: n = 19
    location_date_sensor = 41: n = 19
    location_date_sensor = 42: n = 57
    location_date_sensor = 43: n = 39
    location_date_sensor = 44: n = 33
    location_date_sensor = 45: n = 39
    location_date_sensor = 46: n = 30
Test data...
    location_date_sensor = 0: n = 0
    location_date_sensor = 1: n = 0
    location_date_sensor = 2: n = 0
    location_date_sensor = 3: n = 0
    location_date_sensor = 4: n = 0
    location_date_sensor = 5: n = 0
    location_date_sensor = 6: n = 0
    location_date_sensor = 7: n = 0
    location_date_sensor = 8: n = 0
    location_date_sensor = 9: n = 0
    location_date_sensor = 10: n = 0
    location_date_sensor = 11: n = 0
    location_date_sensor = 12: n = 0
    location_date_sensor = 13: n = 0
    location_date_sensor = 14: n = 0
    location_date_sensor = 15: n = 0
    location_date_sensor = 16: n = 0
    location_date_sensor = 17: n = 0
    location_date_sensor = 18: n = 8
    location_date_sensor = 19: n = 71
    location_date_sensor = 20: n = 284
    location_date_sensor = 21: n = 240
    location_date_sensor = 22: n = 111
    location_date_sensor = 23: n = 11
    location_date_sensor = 24: n = 12
    location_date_sensor = 25: n = 7
    location_date_sensor = 26: n = 16
    location_date_sensor = 27: n = 13
    location_date_sensor = 28: n = 16
    location_date_sensor = 29: n = 14
    location_date_sensor = 30: n = 57
    location_date_sensor = 31: n = 45
    location_date_sensor = 32: n = 49
    location_date_sensor = 33: n = 45
    location_date_sensor = 34: n = 32
    location_date_sensor = 35: n = 69
    location_date_sensor = 36: n = 49
    location_date_sensor = 37: n = 26
    location_date_sensor = 38: n = 45
    location_date_sensor = 39: n = 4
    location_date_sensor = 40: n = 22
    location_date_sensor = 41: n = 14
    location_date_sensor = 42: n = 49
    location_date_sensor = 43: n = 45
    location_date_sensor = 44: n = 36
    location_date_sensor = 45: n = 38
    location_date_sensor = 46: n = 30
Downloading: "https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth" to /root/.cache/torch/hub/checkpoints/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
100% 160M/160M [00:00<00:00, 193MB/s]

Epoch [0]:

Train:
  5% 49/915 [01:11<21:08,  1.46s/it]objective: 0.363
loss_avg: 1.779
  location_date_sensor = 0  [n =     14]:	weight: 0.059	loss: 1.998	
  location_date_sensor = 1  [n =      6]:	weight: 0.054	loss: 1.548	
  location_date_sensor = 2  [n =     10]:	weight: 0.053	loss: 1.479	
  location_date_sensor = 3  [n =      5]:	weight: 0.056	loss: 2.450	
  location_date_sensor = 4  [n =     11]:	weight: 0.053	loss: 1.531	
  location_date_sensor = 5  [n =      7]:	weight: 0.054	loss: 1.603	
  location_date_sensor = 6  [n =      9]:	weight: 0.055	loss: 1.743	
  location_date_sensor = 7  [n =     14]:	weight: 0.058	loss: 2.052	
  location_date_sensor = 8  [n =     12]:	weight: 0.060	loss: 2.311	
  location_date_sensor = 9  [n =     14]:	weight: 0.063	loss: 2.453	
  location_date_sensor = 10  [n =     12]:	weight: 0.054	loss: 1.401	
  location_date_sensor = 11  [n =     11]:	weight: 0.059	loss: 2.345	
  location_date_sensor = 12  [n =     10]:	weight: 0.053	loss: 1.396	
  location_date_sensor = 13  [n =     12]:	weight: 0.055	loss: 1.569	
  location_date_sensor = 14  [n =     11]:	weight: 0.057	loss: 1.775	
  location_date_sensor = 15  [n =     14]:	weight: 0.054	loss: 1.516	
  location_date_sensor = 16  [n =     19]:	weight: 0.056	loss: 1.451	
  location_date_sensor = 17  [n =      9]:	weight: 0.055	loss: 1.474	
 11% 99/915 [02:27<20:39,  1.52s/it]objective: 0.263
loss_avg: 1.289
  location_date_sensor = 0  [n =      8]:	weight: 0.058	loss: 1.654	
  location_date_sensor = 1  [n =      6]:	weight: 0.048	loss: 0.929	
  location_date_sensor = 2  [n =     15]:	weight: 0.053	loss: 1.236	
  location_date_sensor = 3  [n =     16]:	weight: 0.055	loss: 1.333	
  location_date_sensor = 4  [n =     10]:	weight: 0.054	loss: 1.280	
  location_date_sensor = 5  [n =     10]:	weight: 0.051	loss: 0.900	
  location_date_sensor = 6  [n =     19]:	weight: 0.057	loss: 1.177	
  location_date_sensor = 7  [n =     15]:	weight: 0.065	loss: 1.693	
  location_date_sensor = 8  [n =     10]:	weight: 0.058	loss: 1.351	
  location_date_sensor = 9  [n =      8]:	weight: 0.063	loss: 1.397	
  location_date_sensor = 10  [n =      8]:	weight: 0.052	loss: 1.217	
  location_date_sensor = 11  [n =      7]:	weight: 0.058	loss: 1.600	
  location_date_sensor = 12  [n =     11]:	weight: 0.054	loss: 1.211	
  location_date_sensor = 13  [n =     10]:	weight: 0.055	loss: 1.325	
  location_date_sensor = 14  [n =     11]:	weight: 0.056	loss: 1.303	
  location_date_sensor = 15  [n =     18]:	weight: 0.058	loss: 1.326	
  location_date_sensor = 16  [n =     10]:	weight: 0.058	loss: 1.131	
  location_date_sensor = 17  [n =      8]:	weight: 0.050	loss: 0.989	
 16% 149/915 [03:43<19:30,  1.53s/it]objective: 0.237
loss_avg: 1.136
  location_date_sensor = 0  [n =     14]:	weight: 0.060	loss: 1.224	
  location_date_sensor = 1  [n =     13]:	weight: 0.047	loss: 0.782	
  location_date_sensor = 2  [n =     11]:	weight: 0.054	loss: 1.129	
  location_date_sensor = 3  [n =     10]:	weight: 0.055	loss: 1.096	
  location_date_sensor = 4  [n =     14]:	weight: 0.054	loss: 1.233	
  location_date_sensor = 5  [n =     12]:	weight: 0.049	loss: 0.940	
  location_date_sensor = 6  [n =     14]:	weight: 0.057	loss: 1.005	
  location_date_sensor = 7  [n =     14]:	weight: 0.070	loss: 1.451	
  location_date_sensor = 8  [n =     12]:	weight: 0.059	loss: 1.205	
  location_date_sensor = 9  [n =     12]:	weight: 0.062	loss: 1.305	
  location_date_sensor = 10  [n =     13]:	weight: 0.052	loss: 1.186	
  location_date_sensor = 11  [n =      9]:	weight: 0.059	loss: 1.461	
  location_date_sensor = 12  [n =      9]:	weight: 0.052	loss: 1.014	
  location_date_sensor = 13  [n =     10]:	weight: 0.056	loss: 1.107	
  location_date_sensor = 14  [n =      8]:	weight: 0.056	loss: 1.147	
  location_date_sensor = 15  [n =      8]:	weight: 0.060	loss: 1.214	
  location_date_sensor = 16  [n =      5]:	weight: 0.053	loss: 1.095	
  location_date_sensor = 17  [n =     12]:	weight: 0.049	loss: 0.864	
 22% 199/915 [05:00<18:10,  1.52s/it]objective: 0.225
loss_avg: 1.067
  location_date_sensor = 0  [n =     10]:	weight: 0.062	loss: 1.331	
  location_date_sensor = 1  [n =     10]:	weight: 0.045	loss: 0.749	
  location_date_sensor = 2  [n =     13]:	weight: 0.054	loss: 0.953	
  location_date_sensor = 3  [n =      9]:	weight: 0.055	loss: 1.025	
  location_date_sensor = 4  [n =     10]:	weight: 0.055	loss: 1.034	
  location_date_sensor = 5  [n =     11]:	weight: 0.048	loss: 0.922	
  location_date_sensor = 6  [n =      8]:	weight: 0.057	loss: 0.927	
  location_date_sensor = 7  [n =     10]:	weight: 0.070	loss: 1.337	
  location_date_sensor = 8  [n =     12]:	weight: 0.060	loss: 1.063	
  location_date_sensor = 9  [n =     19]:	weight: 0.065	loss: 1.125	
  location_date_sensor = 10  [n =     18]:	weight: 0.055	loss: 1.108	
  location_date_sensor = 11  [n =      8]:	weight: 0.058	loss: 1.340	
  location_date_sensor = 12  [n =     10]:	weight: 0.050	loss: 0.961	
  location_date_sensor = 13  [n =     10]:	weight: 0.056	loss: 1.198	
  location_date_sensor = 14  [n =     12]:	weight: 0.055	loss: 1.082	
  location_date_sensor = 15  [n =     16]:	weight: 0.059	loss: 1.057	
  location_date_sensor = 16  [n =      8]:	weight: 0.053	loss: 1.093	
  location_date_sensor = 17  [n =      6]:	weight: 0.047	loss: 0.767	
 27% 249/915 [06:16<16:53,  1.52s/it]objective: 0.220
loss_avg: 1.020
  location_date_sensor = 0  [n =      9]:	weight: 0.064	loss: 1.383	
  location_date_sensor = 1  [n =     15]:	weight: 0.043	loss: 0.725	
  location_date_sensor = 2  [n =     10]:	weight: 0.054	loss: 0.946	
  location_date_sensor = 3  [n =      7]:	weight: 0.053	loss: 1.022	
  location_date_sensor = 4  [n =      8]:	weight: 0.053	loss: 1.043	
  location_date_sensor = 5  [n =      7]:	weight: 0.046	loss: 0.776	
  location_date_sensor = 6  [n =     14]:	weight: 0.056	loss: 0.896	
  location_date_sensor = 7  [n =     22]:	weight: 0.077	loss: 1.249	
  location_date_sensor = 8  [n =      6]:	weight: 0.060	loss: 1.019	
  location_date_sensor = 9  [n =     15]:	weight: 0.068	loss: 1.094	
  location_date_sensor = 10  [n =     15]:	weight: 0.057	loss: 1.041	
  location_date_sensor = 11  [n =     11]:	weight: 0.057	loss: 1.247	
  location_date_sensor = 12  [n =      8]:	weight: 0.049	loss: 0.915	
  location_date_sensor = 13  [n =     11]:	weight: 0.054	loss: 1.056	
  location_date_sensor = 14  [n =      8]:	weight: 0.055	loss: 1.056	
  location_date_sensor = 15  [n =     10]:	weight: 0.060	loss: 0.996	
  location_date_sensor = 16  [n =     12]:	weight: 0.052	loss: 1.027	
  location_date_sensor = 17  [n =     12]:	weight: 0.044	loss: 0.737	
 33% 299/915 [07:32<15:40,  1.53s/it]objective: 0.195
loss_avg: 0.954
  location_date_sensor = 0  [n =      7]:	weight: 0.063	loss: 0.891	
  location_date_sensor = 1  [n =     11]:	weight: 0.042	loss: 0.624	
  location_date_sensor = 2  [n =     13]:	weight: 0.053	loss: 0.940	
  location_date_sensor = 3  [n =     10]:	weight: 0.052	loss: 0.962	
  location_date_sensor = 4  [n =      8]:	weight: 0.053	loss: 0.953	
  location_date_sensor = 5  [n =     10]:	weight: 0.045	loss: 0.859	
  location_date_sensor = 6  [n =     15]:	weight: 0.057	loss: 0.908	
  location_date_sensor = 7  [n =     12]:	weight: 0.080	loss: 1.122	
  location_date_sensor = 8  [n =     15]:	weight: 0.059	loss: 0.987	
  location_date_sensor = 9  [n =     17]:	weight: 0.071	loss: 1.023	
  location_date_sensor = 10  [n =      8]:	weight: 0.058	loss: 1.118	
  location_date_sensor = 11  [n =     10]:	weight: 0.058	loss: 1.186	
  location_date_sensor = 12  [n =      5]:	weight: 0.046	loss: 0.890	
  location_date_sensor = 13  [n =      8]:	weight: 0.053	loss: 1.130	
  location_date_sensor = 14  [n =     15]:	weight: 0.056	loss: 0.998	
  location_date_sensor = 15  [n =      8]:	weight: 0.061	loss: 1.001	
  location_date_sensor = 16  [n =     13]:	weight: 0.053	loss: 0.953	
  location_date_sensor = 17  [n =     15]:	weight: 0.044	loss: 0.713	
 38% 349/915 [08:49<14:24,  1.53s/it]objective: 0.201
loss_avg: 0.960
  location_date_sensor = 0  [n =     14]:	weight: 0.063	loss: 1.040	
  location_date_sensor = 1  [n =     12]:	weight: 0.041	loss: 0.695	
  location_date_sensor = 2  [n =     13]:	weight: 0.053	loss: 0.795	
  location_date_sensor = 3  [n =     10]:	weight: 0.050	loss: 0.870	
  location_date_sensor = 4  [n =      9]:	weight: 0.051	loss: 0.888	
  location_date_sensor = 5  [n =     13]:	weight: 0.045	loss: 0.728	
  location_date_sensor = 6  [n =      9]:	weight: 0.057	loss: 0.889	
  location_date_sensor = 7  [n =     14]:	weight: 0.085	loss: 1.135	
  location_date_sensor = 8  [n =      4]:	weight: 0.057	loss: 1.032	
  location_date_sensor = 9  [n =     12]:	weight: 0.074	loss: 1.271	
  location_date_sensor = 10  [n =      6]:	weight: 0.056	loss: 1.024	
  location_date_sensor = 11  [n =      6]:	weight: 0.057	loss: 1.185	
  location_date_sensor = 12  [n =     16]:	weight: 0.046	loss: 0.858	
  location_date_sensor = 13  [n =     12]:	weight: 0.054	loss: 1.057	
  location_date_sensor = 14  [n =     12]:	weight: 0.056	loss: 1.017	
  location_date_sensor = 15  [n =     14]:	weight: 0.060	loss: 1.019	
  location_date_sensor = 16  [n =     15]:	weight: 0.054	loss: 1.137	
  location_date_sensor = 17  [n =      9]:	weight: 0.043	loss: 0.680	
 44% 399/915 [10:05<13:06,  1.52s/it]objective: 0.196
loss_avg: 0.933
  location_date_sensor = 0  [n =     11]:	weight: 0.064	loss: 0.944	
  location_date_sensor = 1  [n =     10]:	weight: 0.040	loss: 0.650	
  location_date_sensor = 2  [n =     11]:	weight: 0.054	loss: 0.851	
  location_date_sensor = 3  [n =     10]:	weight: 0.050	loss: 0.900	
  location_date_sensor = 4  [n =     15]:	weight: 0.051	loss: 0.985	
  location_date_sensor = 5  [n =     18]:	weight: 0.045	loss: 0.849	
  location_date_sensor = 6  [n =     10]:	weight: 0.057	loss: 0.862	
  location_date_sensor = 7  [n =      8]:	weight: 0.083	loss: 1.043	
  location_date_sensor = 8  [n =      5]:	weight: 0.056	loss: 0.934	
  location_date_sensor = 9  [n =     12]:	weight: 0.077	loss: 1.046	
  location_date_sensor = 10  [n =     10]:	weight: 0.054	loss: 0.994	
  location_date_sensor = 11  [n =     12]:	weight: 0.056	loss: 1.110	
  location_date_sensor = 12  [n =      7]:	weight: 0.046	loss: 0.889	
  location_date_sensor = 13  [n =      8]:	weight: 0.054	loss: 1.070	
  location_date_sensor = 14  [n =     10]:	weight: 0.057	loss: 1.075	
  location_date_sensor = 15  [n =     17]:	weight: 0.063	loss: 0.998	
  location_date_sensor = 16  [n =     11]:	weight: 0.055	loss: 0.958	
  location_date_sensor = 17  [n =     15]:	weight: 0.042	loss: 0.724	
 49% 449/915 [11:21<11:51,  1.53s/it]objective: 0.193
loss_avg: 0.914
  location_date_sensor = 0  [n =      9]:	weight: 0.063	loss: 0.864	
  location_date_sensor = 1  [n =     11]:	weight: 0.038	loss: 0.622	
  location_date_sensor = 2  [n =      7]:	weight: 0.052	loss: 0.822	
  location_date_sensor = 3  [n =      7]:	weight: 0.049	loss: 0.846	
  location_date_sensor = 4  [n =     15]:	weight: 0.053	loss: 0.969	
  location_date_sensor = 5  [n =     12]:	weight: 0.046	loss: 0.614	
  location_date_sensor = 6  [n =      8]:	weight: 0.055	loss: 0.775	
  location_date_sensor = 7  [n =     13]:	weight: 0.087	loss: 1.213	
  location_date_sensor = 8  [n =     12]:	weight: 0.054	loss: 0.920	
  location_date_sensor = 9  [n =     17]:	weight: 0.080	loss: 1.058	
  location_date_sensor = 10  [n =     13]:	weight: 0.055	loss: 1.034	
  location_date_sensor = 11  [n =      9]:	weight: 0.057	loss: 1.149	
  location_date_sensor = 12  [n =      8]:	weight: 0.045	loss: 0.825	
  location_date_sensor = 13  [n =      8]:	weight: 0.053	loss: 1.016	
  location_date_sensor = 14  [n =     15]:	weight: 0.059	loss: 0.980	
  location_date_sensor = 15  [n =      9]:	weight: 0.064	loss: 0.882	
  location_date_sensor = 16  [n =     15]:	weight: 0.055	loss: 0.950	
  location_date_sensor = 17  [n =     12]:	weight: 0.041	loss: 0.680	
 55% 499/915 [12:38<10:35,  1.53s/it]objective: 0.200
loss_avg: 0.915
  location_date_sensor = 0  [n =     11]:	weight: 0.062	loss: 0.897	
  location_date_sensor = 1  [n =      9]:	weight: 0.037	loss: 0.590	
  location_date_sensor = 2  [n =     12]:	weight: 0.051	loss: 0.811	
  location_date_sensor = 3  [n =      9]:	weight: 0.048	loss: 0.887	
  location_date_sensor = 4  [n =     10]:	weight: 0.054	loss: 0.956	
  location_date_sensor = 5  [n =     18]:	weight: 0.045	loss: 0.818	
  location_date_sensor = 6  [n =     13]:	weight: 0.053	loss: 0.828	
  location_date_sensor = 7  [n =     15]:	weight: 0.091	loss: 1.093	
  location_date_sensor = 8  [n =     10]:	weight: 0.053	loss: 0.954	
  location_date_sensor = 9  [n =     11]:	weight: 0.083	loss: 1.046	
  location_date_sensor = 10  [n =     11]:	weight: 0.056	loss: 1.019	
  location_date_sensor = 11  [n =     11]:	weight: 0.057	loss: 1.099	
  location_date_sensor = 12  [n =      5]:	weight: 0.041	loss: 0.870	
  location_date_sensor = 13  [n =     15]:	weight: 0.054	loss: 1.053	
  location_date_sensor = 14  [n =      8]:	weight: 0.059	loss: 0.982	
  location_date_sensor = 15  [n =     12]:	weight: 0.064	loss: 0.903	
  location_date_sensor = 16  [n =      8]:	weight: 0.055	loss: 0.982	
  location_date_sensor = 17  [n =     12]:	weight: 0.040	loss: 0.641	
 60% 549/915 [13:54<09:17,  1.52s/it]objective: 0.189
loss_avg: 0.883
  location_date_sensor = 0  [n =     11]:	weight: 0.061	loss: 0.995	
  location_date_sensor = 1  [n =      7]:	weight: 0.035	loss: 0.625	
  location_date_sensor = 2  [n =     10]:	weight: 0.050	loss: 0.794	
  location_date_sensor = 3  [n =     12]:	weight: 0.047	loss: 0.762	
  location_date_sensor = 4  [n =     11]:	weight: 0.053	loss: 0.940	
  location_date_sensor = 5  [n =     12]:	weight: 0.046	loss: 0.744	
  location_date_sensor = 6  [n =     12]:	weight: 0.054	loss: 0.860	
  location_date_sensor = 7  [n =     12]:	weight: 0.095	loss: 1.077	
  location_date_sensor = 8  [n =     10]:	weight: 0.053	loss: 0.893	
  location_date_sensor = 9  [n =     15]:	weight: 0.086	loss: 0.917	
  location_date_sensor = 10  [n =     11]:	weight: 0.056	loss: 1.016	
  location_date_sensor = 11  [n =      8]:	weight: 0.057	loss: 0.994	
  location_date_sensor = 12  [n =     14]:	weight: 0.041	loss: 0.817	
  location_date_sensor = 13  [n =     11]:	weight: 0.055	loss: 1.052	
  location_date_sensor = 14  [n =      5]:	weight: 0.058	loss: 1.021	
  location_date_sensor = 15  [n =     13]:	weight: 0.064	loss: 0.906	
  location_date_sensor = 16  [n =     13]:	weight: 0.055	loss: 0.848	
  location_date_sensor = 17  [n =     13]:	weight: 0.039	loss: 0.683	
 65% 599/915 [15:10<08:03,  1.53s/it]objective: 0.200
loss_avg: 0.904
  location_date_sensor = 0  [n =      8]:	weight: 0.061	loss: 0.866	
  location_date_sensor = 1  [n =      4]:	weight: 0.033	loss: 0.682	
  location_date_sensor = 2  [n =     10]:	weight: 0.049	loss: 0.863	
  location_date_sensor = 3  [n =     13]:	weight: 0.047	loss: 0.752	
  location_date_sensor = 4  [n =     15]:	weight: 0.055	loss: 0.914	
  location_date_sensor = 5  [n =      9]:	weight: 0.045	loss: 0.829	
  location_date_sensor = 6  [n =     10]:	weight: 0.052	loss: 0.729	
  location_date_sensor = 7  [n =     12]:	weight: 0.097	loss: 1.025	
  location_date_sensor = 8  [n =      8]:	weight: 0.051	loss: 0.914	
  location_date_sensor = 9  [n =     14]:	weight: 0.088	loss: 0.974	
  location_date_sensor = 10  [n =     15]:	weight: 0.058	loss: 1.072	
  location_date_sensor = 11  [n =     12]:	weight: 0.056	loss: 1.044	
  location_date_sensor = 12  [n =     10]:	weight: 0.041	loss: 0.824	
  location_date_sensor = 13  [n =     18]:	weight: 0.059	loss: 0.951	
  location_date_sensor = 14  [n =     11]:	weight: 0.056	loss: 0.989	
  location_date_sensor = 15  [n =     12]:	weight: 0.065	loss: 0.952	
  location_date_sensor = 16  [n =      7]:	weight: 0.054	loss: 0.909	
  location_date_sensor = 17  [n =     12]:	weight: 0.038	loss: 0.698	
 71% 649/915 [16:26<06:45,  1.52s/it]objective: 0.182
loss_avg: 0.885
  location_date_sensor = 0  [n =      8]:	weight: 0.060	loss: 0.785	
  location_date_sensor = 1  [n =     11]:	weight: 0.031	loss: 0.628	
  location_date_sensor = 2  [n =     10]:	weight: 0.048	loss: 0.743	
  location_date_sensor = 3  [n =      4]:	weight: 0.044	loss: 0.873	
  location_date_sensor = 4  [n =      5]:	weight: 0.054	loss: 0.844	
  location_date_sensor = 5  [n =      7]:	weight: 0.043	loss: 0.752	
  location_date_sensor = 6  [n =     13]:	weight: 0.052	loss: 0.825	
  location_date_sensor = 7  [n =      7]:	weight: 0.096	loss: 1.145	
  location_date_sensor = 8  [n =     11]:	weight: 0.051	loss: 0.945	
  location_date_sensor = 9  [n =      7]:	weight: 0.089	loss: 0.951	
  location_date_sensor = 10  [n =     13]:	weight: 0.059	loss: 0.978	
  location_date_sensor = 11  [n =     15]:	weight: 0.058	loss: 1.037	
  location_date_sensor = 12  [n =     12]:	weight: 0.040	loss: 0.808	
  location_date_sensor = 13  [n =     10]:	weight: 0.061	loss: 1.016	
  location_date_sensor = 14  [n =     20]:	weight: 0.059	loss: 0.948	
  location_date_sensor = 15  [n =     19]:	weight: 0.067	loss: 0.956	
  location_date_sensor = 16  [n =     13]:	weight: 0.054	loss: 1.001	
  location_date_sensor = 17  [n =     15]:	weight: 0.037	loss: 0.621	
 76% 699/915 [17:43<05:30,  1.53s/it]objective: 0.184
loss_avg: 0.876
  location_date_sensor = 0  [n =     13]:	weight: 0.059	loss: 0.808	
  location_date_sensor = 1  [n =      6]:	weight: 0.030	loss: 0.648	
  location_date_sensor = 2  [n =     12]:	weight: 0.048	loss: 0.782	
  location_date_sensor = 3  [n =     11]:	weight: 0.043	loss: 0.828	
  location_date_sensor = 4  [n =      9]:	weight: 0.053	loss: 0.884	
  location_date_sensor = 5  [n =     13]:	weight: 0.043	loss: 0.768	
  location_date_sensor = 6  [n =     11]:	weight: 0.051	loss: 0.777	
  location_date_sensor = 7  [n =      9]:	weight: 0.096	loss: 1.061	
  location_date_sensor = 8  [n =     13]:	weight: 0.051	loss: 0.865	
  location_date_sensor = 9  [n =      8]:	weight: 0.086	loss: 0.931	
  location_date_sensor = 10  [n =     14]:	weight: 0.060	loss: 1.002	
  location_date_sensor = 11  [n =     10]:	weight: 0.060	loss: 0.981	
  location_date_sensor = 12  [n =     13]:	weight: 0.039	loss: 0.815	
  location_date_sensor = 13  [n =     16]:	weight: 0.062	loss: 0.997	
  location_date_sensor = 14  [n =     15]:	weight: 0.062	loss: 0.967	
  location_date_sensor = 15  [n =      8]:	weight: 0.068	loss: 0.928	
  location_date_sensor = 16  [n =     10]:	weight: 0.055	loss: 0.933	
  location_date_sensor = 17  [n =      9]:	weight: 0.036	loss: 0.661	
 82% 749/915 [18:59<04:13,  1.53s/it]objective: 0.180
loss_avg: 0.834
  location_date_sensor = 0  [n =     15]:	weight: 0.059	loss: 0.785	
  location_date_sensor = 1  [n =     10]:	weight: 0.029	loss: 0.581	
  location_date_sensor = 2  [n =     17]:	weight: 0.048	loss: 0.729	
  location_date_sensor = 3  [n =     11]:	weight: 0.043	loss: 0.770	
  location_date_sensor = 4  [n =     11]:	weight: 0.052	loss: 0.920	
  location_date_sensor = 5  [n =      7]:	weight: 0.041	loss: 0.869	
  location_date_sensor = 6  [n =      9]:	weight: 0.051	loss: 0.768	
  location_date_sensor = 7  [n =     12]:	weight: 0.097	loss: 0.973	
  location_date_sensor = 8  [n =      9]:	weight: 0.051	loss: 0.856	
  location_date_sensor = 9  [n =     14]:	weight: 0.088	loss: 0.953	
  location_date_sensor = 10  [n =      7]:	weight: 0.060	loss: 1.001	
  location_date_sensor = 11  [n =      6]:	weight: 0.059	loss: 1.069	
  location_date_sensor = 12  [n =     12]:	weight: 0.039	loss: 0.794	
  location_date_sensor = 13  [n =     14]:	weight: 0.066	loss: 0.929	
  location_date_sensor = 14  [n =     11]:	weight: 0.065	loss: 0.950	
  location_date_sensor = 15  [n =     11]:	weight: 0.067	loss: 0.874	
  location_date_sensor = 16  [n =      9]:	weight: 0.054	loss: 0.734	
  location_date_sensor = 17  [n =     15]:	weight: 0.036	loss: 0.660	
 87% 799/915 [20:15<02:57,  1.53s/it]objective: 0.179
loss_avg: 0.858
  location_date_sensor = 0  [n =     12]:	weight: 0.059	loss: 0.994	
  location_date_sensor = 1  [n =     12]:	weight: 0.028	loss: 0.590	
  location_date_sensor = 2  [n =      8]:	weight: 0.047	loss: 0.806	
  location_date_sensor = 3  [n =     15]:	weight: 0.044	loss: 0.838	
  location_date_sensor = 4  [n =     15]:	weight: 0.053	loss: 0.864	
  location_date_sensor = 5  [n =     10]:	weight: 0.040	loss: 0.783	
  location_date_sensor = 6  [n =      8]:	weight: 0.049	loss: 0.766	
  location_date_sensor = 7  [n =      7]:	weight: 0.096	loss: 0.838	
  location_date_sensor = 8  [n =     11]:	weight: 0.052	loss: 0.815	
  location_date_sensor = 9  [n =     13]:	weight: 0.092	loss: 0.924	
  location_date_sensor = 10  [n =     11]:	weight: 0.060	loss: 0.924	
  location_date_sensor = 11  [n =     16]:	weight: 0.058	loss: 0.982	
  location_date_sensor = 12  [n =     13]:	weight: 0.039	loss: 0.804	
  location_date_sensor = 13  [n =      9]:	weight: 0.066	loss: 1.053	
  location_date_sensor = 14  [n =     11]:	weight: 0.065	loss: 0.912	
  location_date_sensor = 15  [n =     12]:	weight: 0.068	loss: 0.938	
  location_date_sensor = 16  [n =      6]:	weight: 0.052	loss: 0.950	
  location_date_sensor = 17  [n =     11]:	weight: 0.036	loss: 0.641	
 93% 849/915 [21:32<01:40,  1.52s/it]objective: 0.172
loss_avg: 0.843
  location_date_sensor = 0  [n =      5]:	weight: 0.059	loss: 0.865	
  location_date_sensor = 1  [n =     13]:	weight: 0.028	loss: 0.568	
  location_date_sensor = 2  [n =     11]:	weight: 0.046	loss: 0.681	
  location_date_sensor = 3  [n =     14]:	weight: 0.046	loss: 0.781	
  location_date_sensor = 4  [n =     11]:	weight: 0.054	loss: 0.865	
  location_date_sensor = 5  [n =     15]:	weight: 0.041	loss: 0.797	
  location_date_sensor = 6  [n =     13]:	weight: 0.048	loss: 0.823	
  location_date_sensor = 7  [n =      9]:	weight: 0.093	loss: 0.918	
  location_date_sensor = 8  [n =     10]:	weight: 0.050	loss: 0.875	
  location_date_sensor = 9  [n =      9]:	weight: 0.091	loss: 0.903	
  location_date_sensor = 10  [n =      7]:	weight: 0.060	loss: 0.921	
  location_date_sensor = 11  [n =      9]:	weight: 0.059	loss: 0.956	
  location_date_sensor = 12  [n =     13]:	weight: 0.039	loss: 0.807	
  location_date_sensor = 13  [n =     19]:	weight: 0.069	loss: 0.963	
  location_date_sensor = 14  [n =     14]:	weight: 0.067	loss: 0.951	
  location_date_sensor = 15  [n =      7]:	weight: 0.067	loss: 0.790	
  location_date_sensor = 16  [n =     15]:	weight: 0.052	loss: 0.979	
  location_date_sensor = 17  [n =      6]:	weight: 0.034	loss: 0.647	
 98% 899/915 [22:48<00:24,  1.52s/it]objective: 0.180
loss_avg: 0.834
  location_date_sensor = 0  [n =     14]:	weight: 0.058	loss: 0.949	
  location_date_sensor = 1  [n =     10]:	weight: 0.027	loss: 0.588	
  location_date_sensor = 2  [n =      5]:	weight: 0.045	loss: 0.713	
  location_date_sensor = 3  [n =     14]:	weight: 0.046	loss: 0.719	
  location_date_sensor = 4  [n =      8]:	weight: 0.053	loss: 0.809	
  location_date_sensor = 5  [n =     10]:	weight: 0.041	loss: 0.683	
  location_date_sensor = 6  [n =     10]:	weight: 0.048	loss: 0.796	
  location_date_sensor = 7  [n =     14]:	weight: 0.095	loss: 0.940	
  location_date_sensor = 8  [n =     14]:	weight: 0.052	loss: 0.816	
  location_date_sensor = 9  [n =     16]:	weight: 0.093	loss: 0.876	
  location_date_sensor = 10  [n =     11]:	weight: 0.059	loss: 0.974	
  location_date_sensor = 11  [n =     10]:	weight: 0.060	loss: 0.950	
  location_date_sensor = 12  [n =     16]:	weight: 0.040	loss: 0.792	
  location_date_sensor = 13  [n =     13]:	weight: 0.072	loss: 0.906	
  location_date_sensor = 14  [n =      9]:	weight: 0.066	loss: 0.935	
  location_date_sensor = 15  [n =      6]:	weight: 0.063	loss: 0.931	
  location_date_sensor = 16  [n =     11]:	weight: 0.053	loss: 0.863	
  location_date_sensor = 17  [n =      9]:	weight: 0.033	loss: 0.671	
100% 915/915 [23:12<00:00,  1.52s/it]
objective: 0.171
loss_avg: 0.845
  location_date_sensor = 0  [n =      2]:	weight: 0.057	loss: 0.668	
  location_date_sensor = 1  [n =      1]:	weight: 0.026	loss: 0.738	
  location_date_sensor = 2  [n =      5]:	weight: 0.044	loss: 0.746	
  location_date_sensor = 3  [n =      7]:	weight: 0.047	loss: 0.770	
  location_date_sensor = 4  [n =      3]:	weight: 0.053	loss: 0.835	
  location_date_sensor = 5  [n =      3]:	weight: 0.040	loss: 0.900	
  location_date_sensor = 6  [n =      1]:	weight: 0.046	loss: 0.799	
  location_date_sensor = 7  [n =      7]:	weight: 0.098	loss: 0.921	
  location_date_sensor = 8  [n =      2]:	weight: 0.052	loss: 0.835	
  location_date_sensor = 9  [n =      2]:	weight: 0.095	loss: 1.050	
  location_date_sensor = 10  [n =      1]:	weight: 0.059	loss: 0.912	
  location_date_sensor = 11  [n =      5]:	weight: 0.060	loss: 0.983	
  location_date_sensor = 12  [n =      4]:	weight: 0.041	loss: 0.735	
  location_date_sensor = 13  [n =      4]:	weight: 0.072	loss: 0.912	
  location_date_sensor = 14  [n =      2]:	weight: 0.066	loss: 0.835	
  location_date_sensor = 15  [n =      3]:	weight: 0.064	loss: 0.800	
  location_date_sensor = 16  [n =      3]:	weight: 0.052	loss: 0.996	
  location_date_sensor = 17  [n =      2]:	weight: 0.032	loss: 0.625	
Epoch eval:
Average detection_acc: 0.617
  location_date_sensor = 0  [n =    195]:	detection_acc = 0.601
  location_date_sensor = 1  [n =    177]:	detection_acc = 0.799
  location_date_sensor = 2  [n =    203]:	detection_acc = 0.736
  location_date_sensor = 3  [n =    194]:	detection_acc = 0.666
  location_date_sensor = 4  [n =    198]:	detection_acc = 0.622
  location_date_sensor = 5  [n =    204]:	detection_acc = 0.666
  location_date_sensor = 6  [n =    206]:	detection_acc = 0.668
  location_date_sensor = 7  [n =    226]:	detection_acc = 0.474
  location_date_sensor = 8  [n =    186]:	detection_acc = 0.590
  location_date_sensor = 9  [n =    235]:	detection_acc = 0.481
  location_date_sensor = 10  [n =    204]:	detection_acc = 0.573
  location_date_sensor = 11  [n =    185]:	detection_acc = 0.457
  location_date_sensor = 12  [n =    196]:	detection_acc = 0.645
  location_date_sensor = 13  [n =    218]:	detection_acc = 0.598
  location_date_sensor = 14  [n =    208]:	detection_acc = 0.643
  location_date_sensor = 15  [n =    217]:	detection_acc = 0.648
  location_date_sensor = 16  [n =    203]:	detection_acc = 0.529
  location_date_sensor = 17  [n =    202]:	detection_acc = 0.763
Worst-group detection_acc: 0.457

Validation:
100% 348/348 [03:19<00:00,  1.75it/s]
objective: 0.000
loss_avg: 1.000
  location_date_sensor = 18  [n =     12]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 19  [n =     49]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 20  [n =    254]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 21  [n =    216]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 22  [n =     89]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 23  [n =     11]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 24  [n =      4]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 25  [n =      7]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 26  [n =     14]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 27  [n =     17]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 28  [n =     14]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 29  [n =      8]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 30  [n =     43]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 31  [n =     55]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 32  [n =     51]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 33  [n =     50]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 34  [n =     28]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 35  [n =     75]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 36  [n =     56]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 37  [n =     34]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 38  [n =     55]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 39  [n =     13]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 40  [n =     19]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 41  [n =     19]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 42  [n =     57]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 43  [n =     39]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 44  [n =     33]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 45  [n =     39]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 46  [n =     30]:	weight: 0.000	loss: 1.000	
Epoch eval:
Average detection_acc: 0.007
  location_date_sensor = 18  [n =     12]:	detection_acc = 0.000
  location_date_sensor = 19  [n =     49]:	detection_acc = 0.020
  location_date_sensor = 20  [n =    254]:	detection_acc = 0.000
  location_date_sensor = 21  [n =    216]:	detection_acc = 0.000
  location_date_sensor = 22  [n =     89]:	detection_acc = 0.000
  location_date_sensor = 23  [n =     11]:	detection_acc = 0.000
  location_date_sensor = 24  [n =      4]:	detection_acc = 0.750
  location_date_sensor = 25  [n =      7]:	detection_acc = 0.000
  location_date_sensor = 26  [n =     14]:	detection_acc = 0.000
  location_date_sensor = 27  [n =     17]:	detection_acc = 0.059
  location_date_sensor = 28  [n =     14]:	detection_acc = 0.000
  location_date_sensor = 29  [n =      8]:	detection_acc = 0.000
  location_date_sensor = 30  [n =     43]:	detection_acc = 0.000
  location_date_sensor = 31  [n =     55]:	detection_acc = 0.000
  location_date_sensor = 32  [n =     51]:	detection_acc = 0.000
  location_date_sensor = 33  [n =     50]:	detection_acc = 0.000
  location_date_sensor = 34  [n =     28]:	detection_acc = 0.000
  location_date_sensor = 35  [n =     75]:	detection_acc = 0.013
  location_date_sensor = 36  [n =     56]:	detection_acc = 0.018
  location_date_sensor = 37  [n =     34]:	detection_acc = 0.029
  location_date_sensor = 38  [n =     55]:	detection_acc = 0.000
  location_date_sensor = 39  [n =     13]:	detection_acc = 0.000
  location_date_sensor = 40  [n =     19]:	detection_acc = 0.000
  location_date_sensor = 41  [n =     19]:	detection_acc = 0.000
  location_date_sensor = 42  [n =     57]:	detection_acc = 0.000
  location_date_sensor = 43  [n =     39]:	detection_acc = 0.000
  location_date_sensor = 44  [n =     33]:	detection_acc = 0.030
  location_date_sensor = 45  [n =     39]:	detection_acc = 0.000
  location_date_sensor = 46  [n =     30]:	detection_acc = 0.033
Worst-group detection_acc: 0.000
Validation detection_acc_avg: 0.007
Epoch 0 has the best validation performance so far.
100% 365/365 [03:28<00:00,  1.75it/s]


Epoch [1]:

Train:
  5% 49/915 [01:15<22:09,  1.54s/it]objective: 0.174
loss_avg: 0.814
  location_date_sensor = 0  [n =     10]:	weight: 0.057	loss: 0.795	
  location_date_sensor = 1  [n =     11]:	weight: 0.025	loss: 0.587	
  location_date_sensor = 2  [n =      8]:	weight: 0.043	loss: 0.720	
  location_date_sensor = 3  [n =     15]:	weight: 0.047	loss: 0.730	
  location_date_sensor = 4  [n =     14]:	weight: 0.053	loss: 0.828	
  location_date_sensor = 5  [n =     12]:	weight: 0.040	loss: 0.766	
  location_date_sensor = 6  [n =      8]:	weight: 0.046	loss: 0.730	
  location_date_sensor = 7  [n =      9]:	weight: 0.099	loss: 0.997	
  location_date_sensor = 8  [n =     10]:	weight: 0.050	loss: 0.795	
  location_date_sensor = 9  [n =     17]:	weight: 0.096	loss: 0.900	
  location_date_sensor = 10  [n =      8]:	weight: 0.059	loss: 0.978	
  location_date_sensor = 11  [n =     15]:	weight: 0.061	loss: 0.956	
  location_date_sensor = 12  [n =     12]:	weight: 0.041	loss: 0.714	
  location_date_sensor = 13  [n =     13]:	weight: 0.074	loss: 0.947	
  location_date_sensor = 14  [n =     10]:	weight: 0.067	loss: 0.969	
  location_date_sensor = 15  [n =      7]:	weight: 0.063	loss: 0.866	
  location_date_sensor = 16  [n =      6]:	weight: 0.050	loss: 0.709	
  location_date_sensor = 17  [n =     15]:	weight: 0.031	loss: 0.652	
 11% 99/915 [02:31<20:43,  1.52s/it]objective: 0.175
loss_avg: 0.823
  location_date_sensor = 0  [n =     11]:	weight: 0.056	loss: 0.778	
  location_date_sensor = 1  [n =      8]:	weight: 0.025	loss: 0.569	
  location_date_sensor = 2  [n =     12]:	weight: 0.042	loss: 0.713	
  location_date_sensor = 3  [n =     10]:	weight: 0.047	loss: 0.775	
  location_date_sensor = 4  [n =     11]:	weight: 0.053	loss: 0.862	
  location_date_sensor = 5  [n =     10]:	weight: 0.039	loss: 0.616	
  location_date_sensor = 6  [n =      9]:	weight: 0.044	loss: 0.742	
  location_date_sensor = 7  [n =      8]:	weight: 0.098	loss: 1.004	
  location_date_sensor = 8  [n =      6]:	weight: 0.050	loss: 0.913	
  location_date_sensor = 9  [n =     12]:	weight: 0.099	loss: 0.864	
  location_date_sensor = 10  [n =     12]:	weight: 0.059	loss: 0.938	
  location_date_sensor = 11  [n =     12]:	weight: 0.063	loss: 0.878	
  location_date_sensor = 12  [n =     16]:	weight: 0.041	loss: 0.736	
  location_date_sensor = 13  [n =     18]:	weight: 0.076	loss: 0.930	
  location_date_sensor = 14  [n =     11]:	weight: 0.067	loss: 0.947	
  location_date_sensor = 15  [n =     12]:	weight: 0.062	loss: 0.961	
  location_date_sensor = 16  [n =     13]:	weight: 0.050	loss: 0.859	
  location_date_sensor = 17  [n =      9]:	weight: 0.031	loss: 0.623	
 16% 149/915 [03:48<19:29,  1.53s/it]objective: 0.165
loss_avg: 0.792
  location_date_sensor = 0  [n =      8]:	weight: 0.054	loss: 1.057	
  location_date_sensor = 1  [n =     11]:	weight: 0.023	loss: 0.469	
  location_date_sensor = 2  [n =     12]:	weight: 0.042	loss: 0.734	
  location_date_sensor = 3  [n =     11]:	weight: 0.047	loss: 0.798	
  location_date_sensor = 4  [n =      7]:	weight: 0.053	loss: 0.756	
  location_date_sensor = 5  [n =      9]:	weight: 0.038	loss: 0.541	
  location_date_sensor = 6  [n =     12]:	weight: 0.044	loss: 0.727	
  location_date_sensor = 7  [n =      7]:	weight: 0.096	loss: 0.879	
  location_date_sensor = 8  [n =     13]:	weight: 0.049	loss: 0.789	
  location_date_sensor = 9  [n =     11]:	weight: 0.098	loss: 0.842	
  location_date_sensor = 10  [n =      8]:	weight: 0.059	loss: 0.907	
  location_date_sensor = 11  [n =     20]:	weight: 0.066	loss: 0.910	
  location_date_sensor = 12  [n =     11]:	weight: 0.042	loss: 0.752	
  location_date_sensor = 13  [n =      7]:	weight: 0.077	loss: 1.037	
  location_date_sensor = 14  [n =      7]:	weight: 0.067	loss: 0.909	
  location_date_sensor = 15  [n =     12]:	weight: 0.064	loss: 0.926	
  location_date_sensor = 16  [n =     20]:	weight: 0.052	loss: 0.766	
  location_date_sensor = 17  [n =     14]:	weight: 0.031	loss: 0.631	
 22% 199/915 [05:04<18:12,  1.53s/it]objective: 0.182
loss_avg: 0.817
  location_date_sensor = 0  [n =      5]:	weight: 0.055	loss: 1.292	
  location_date_sensor = 1  [n =     13]:	weight: 0.023	loss: 0.464	
  location_date_sensor = 2  [n =      8]:	weight: 0.042	loss: 0.735	
  location_date_sensor = 3  [n =      8]:	weight: 0.047	loss: 0.763	
  location_date_sensor = 4  [n =     11]:	weight: 0.051	loss: 0.836	
  location_date_sensor = 5  [n =     14]:	weight: 0.038	loss: 0.612	
  location_date_sensor = 6  [n =      9]:	weight: 0.043	loss: 0.775	
  location_date_sensor = 7  [n =     12]:	weight: 0.096	loss: 0.934	
  location_date_sensor = 8  [n =     11]:	weight: 0.049	loss: 0.826	
  location_date_sensor = 9  [n =     13]:	weight: 0.099	loss: 0.821	
  location_date_sensor = 10  [n =     12]:	weight: 0.059	loss: 0.997	
  location_date_sensor = 11  [n =     10]:	weight: 0.068	loss: 0.931	
  location_date_sensor = 12  [n =      5]:	weight: 0.040	loss: 0.688	
  location_date_sensor = 13  [n =      9]:	weight: 0.077	loss: 0.881	
  location_date_sensor = 14  [n =     12]:	weight: 0.066	loss: 0.895	
  location_date_sensor = 15  [n =     19]:	weight: 0.067	loss: 0.879	
  location_date_sensor = 16  [n =     18]:	weight: 0.054	loss: 0.895	
  location_date_sensor = 17  [n =     11]:	weight: 0.030	loss: 0.629	
 27% 249/915 [06:20<16:55,  1.53s/it]objective: 0.160
loss_avg: 0.797
  location_date_sensor = 0  [n =      5]:	weight: 0.053	loss: 0.687	
  location_date_sensor = 1  [n =     12]:	weight: 0.022	loss: 0.536	
  location_date_sensor = 2  [n =     16]:	weight: 0.042	loss: 0.657	
  location_date_sensor = 3  [n =     13]:	weight: 0.046	loss: 0.813	
  location_date_sensor = 4  [n =     11]:	weight: 0.052	loss: 0.797	
  location_date_sensor = 5  [n =     13]:	weight: 0.038	loss: 0.735	
  location_date_sensor = 6  [n =      8]:	weight: 0.043	loss: 0.781	
  location_date_sensor = 7  [n =     12]:	weight: 0.096	loss: 0.973	
  location_date_sensor = 8  [n =     13]:	weight: 0.051	loss: 0.854	
  location_date_sensor = 9  [n =     10]:	weight: 0.101	loss: 0.893	
  location_date_sensor = 10  [n =      7]:	weight: 0.059	loss: 0.953	
  location_date_sensor = 11  [n =     10]:	weight: 0.067	loss: 0.925	
  location_date_sensor = 12  [n =     10]:	weight: 0.039	loss: 0.726	
  location_date_sensor = 13  [n =     12]:	weight: 0.076	loss: 0.909	
  location_date_sensor = 14  [n =      7]:	weight: 0.065	loss: 0.878	
  location_date_sensor = 15  [n =      9]:	weight: 0.067	loss: 0.939	
  location_date_sensor = 16  [n =     16]:	weight: 0.056	loss: 0.801	
  location_date_sensor = 17  [n =     16]:	weight: 0.031	loss: 0.664	
 33% 299/915 [07:37<15:40,  1.53s/it]objective: 0.181
loss_avg: 0.810
  location_date_sensor = 0  [n =     12]:	weight: 0.052	loss: 1.013	
  location_date_sensor = 1  [n =      7]:	weight: 0.022	loss: 0.584	
  location_date_sensor = 2  [n =      8]:	weight: 0.042	loss: 0.625	
  location_date_sensor = 3  [n =     13]:	weight: 0.046	loss: 0.723	
  location_date_sensor = 4  [n =     13]:	weight: 0.052	loss: 0.827	
  location_date_sensor = 5  [n =      9]:	weight: 0.037	loss: 0.555	
  location_date_sensor = 6  [n =      8]:	weight: 0.041	loss: 0.662	
  location_date_sensor = 7  [n =     11]:	weight: 0.097	loss: 0.978	
  location_date_sensor = 8  [n =     16]:	weight: 0.052	loss: 0.796	
  location_date_sensor = 9  [n =     10]:	weight: 0.100	loss: 0.824	
  location_date_sensor = 10  [n =     13]:	weight: 0.060	loss: 0.932	
  location_date_sensor = 11  [n =     15]:	weight: 0.068	loss: 0.889	
  location_date_sensor = 12  [n =     11]:	weight: 0.039	loss: 0.758	
  location_date_sensor = 13  [n =      9]:	weight: 0.077	loss: 0.932	
  location_date_sensor = 14  [n =     11]:	weight: 0.065	loss: 0.915	
  location_date_sensor = 15  [n =      8]:	weight: 0.066	loss: 0.926	
  location_date_sensor = 16  [n =     18]:	weight: 0.058	loss: 0.783	
  location_date_sensor = 17  [n =      8]:	weight: 0.030	loss: 0.606	
 38% 349/915 [08:53<14:24,  1.53s/it]objective: 0.174
loss_avg: 0.807
  location_date_sensor = 0  [n =     13]:	weight: 0.053	loss: 1.013	
  location_date_sensor = 1  [n =     16]:	weight: 0.020	loss: 0.523	
  location_date_sensor = 2  [n =      9]:	weight: 0.039	loss: 0.731	
  location_date_sensor = 3  [n =      7]:	weight: 0.045	loss: 0.698	
  location_date_sensor = 4  [n =     10]:	weight: 0.052	loss: 0.805	
  location_date_sensor = 5  [n =     11]:	weight: 0.036	loss: 0.607	
  location_date_sensor = 6  [n =      8]:	weight: 0.039	loss: 0.766	
  location_date_sensor = 7  [n =     15]:	weight: 0.101	loss: 0.902	
  location_date_sensor = 8  [n =     12]:	weight: 0.053	loss: 0.826	
  location_date_sensor = 9  [n =      9]:	weight: 0.101	loss: 0.943	
  location_date_sensor = 10  [n =      9]:	weight: 0.060	loss: 0.958	
  location_date_sensor = 11  [n =     11]:	weight: 0.070	loss: 0.865	
  location_date_sensor = 12  [n =     16]:	weight: 0.039	loss: 0.780	
  location_date_sensor = 13  [n =     12]:	weight: 0.077	loss: 0.998	
  location_date_sensor = 14  [n =     11]:	weight: 0.065	loss: 0.905	
  location_date_sensor = 15  [n =     11]:	weight: 0.067	loss: 0.865	
  location_date_sensor = 16  [n =      8]:	weight: 0.057	loss: 0.794	
  location_date_sensor = 17  [n =     12]:	weight: 0.029	loss: 0.597	
 44% 399/915 [10:09<13:08,  1.53s/it]objective: 0.180
loss_avg: 0.790
  location_date_sensor = 0  [n =     15]:	weight: 0.054	loss: 0.784	
  location_date_sensor = 1  [n =      4]:	weight: 0.019	loss: 0.588	
  location_date_sensor = 2  [n =     10]:	weight: 0.039	loss: 0.651	
  location_date_sensor = 3  [n =     12]:	weight: 0.044	loss: 0.684	
  location_date_sensor = 4  [n =      7]:	weight: 0.050	loss: 0.790	
  location_date_sensor = 5  [n =      7]:	weight: 0.034	loss: 0.703	
  location_date_sensor = 6  [n =     10]:	weight: 0.039	loss: 0.734	
  location_date_sensor = 7  [n =     19]:	weight: 0.105	loss: 0.808	
  location_date_sensor = 8  [n =      9]:	weight: 0.052	loss: 0.765	
  location_date_sensor = 9  [n =     12]:	weight: 0.100	loss: 0.808	
  location_date_sensor = 10  [n =     16]:	weight: 0.062	loss: 0.952	
  location_date_sensor = 11  [n =      9]:	weight: 0.069	loss: 0.840	
  location_date_sensor = 12  [n =     12]:	weight: 0.039	loss: 0.726	
  location_date_sensor = 13  [n =     16]:	weight: 0.079	loss: 0.848	
  location_date_sensor = 14  [n =      6]:	weight: 0.064	loss: 0.943	
  location_date_sensor = 15  [n =     10]:	weight: 0.067	loss: 0.871	
  location_date_sensor = 16  [n =     15]:	weight: 0.058	loss: 0.861	
  location_date_sensor = 17  [n =     11]:	weight: 0.028	loss: 0.647	
 49% 449/915 [11:26<11:52,  1.53s/it]objective: 0.153
loss_avg: 0.754
  location_date_sensor = 0  [n =      5]:	weight: 0.053	loss: 0.721	
  location_date_sensor = 1  [n =     13]:	weight: 0.019	loss: 0.550	
  location_date_sensor = 2  [n =     12]:	weight: 0.038	loss: 0.669	
  location_date_sensor = 3  [n =      8]:	weight: 0.043	loss: 0.666	
  location_date_sensor = 4  [n =     12]:	weight: 0.050	loss: 0.781	
  location_date_sensor = 5  [n =     13]:	weight: 0.034	loss: 0.659	
  location_date_sensor = 6  [n =      8]:	weight: 0.038	loss: 0.853	
  location_date_sensor = 7  [n =     10]:	weight: 0.108	loss: 0.752	
  location_date_sensor = 8  [n =     11]:	weight: 0.052	loss: 0.712	
  location_date_sensor = 9  [n =     12]:	weight: 0.100	loss: 0.892	
  location_date_sensor = 10  [n =      6]:	weight: 0.064	loss: 0.967	
  location_date_sensor = 11  [n =      9]:	weight: 0.069	loss: 0.856	
  location_date_sensor = 12  [n =     15]:	weight: 0.040	loss: 0.764	
  location_date_sensor = 13  [n =     11]:	weight: 0.080	loss: 0.879	
  location_date_sensor = 14  [n =     14]:	weight: 0.065	loss: 0.935	
  location_date_sensor = 15  [n =     10]:	weight: 0.065	loss: 0.854	
  location_date_sensor = 16  [n =     10]:	weight: 0.059	loss: 0.713	
  location_date_sensor = 17  [n =     21]:	weight: 0.029	loss: 0.590	
 55% 499/915 [12:42<10:33,  1.52s/it]objective: 0.165
loss_avg: 0.776
  location_date_sensor = 0  [n =     16]:	weight: 0.052	loss: 0.817	
  location_date_sensor = 1  [n =     11]:	weight: 0.019	loss: 0.520	
  location_date_sensor = 2  [n =      9]:	weight: 0.038	loss: 0.626	
  location_date_sensor = 3  [n =      9]:	weight: 0.041	loss: 0.662	
  location_date_sensor = 4  [n =     17]:	weight: 0.052	loss: 0.785	
  location_date_sensor = 5  [n =     12]:	weight: 0.034	loss: 0.593	
  location_date_sensor = 6  [n =     13]:	weight: 0.037	loss: 0.715	
  location_date_sensor = 7  [n =      6]:	weight: 0.105	loss: 0.873	
  location_date_sensor = 8  [n =      8]:	weight: 0.051	loss: 0.707	
  location_date_sensor = 9  [n =     15]:	weight: 0.103	loss: 0.826	
  location_date_sensor = 10  [n =     12]:	weight: 0.062	loss: 0.885	
  location_date_sensor = 11  [n =     11]:	weight: 0.069	loss: 0.860	
  location_date_sensor = 12  [n =      5]:	weight: 0.039	loss: 0.761	
  location_date_sensor = 13  [n =      9]:	weight: 0.081	loss: 0.882	
  location_date_sensor = 14  [n =     18]:	weight: 0.069	loss: 0.907	
  location_date_sensor = 15  [n =     13]:	weight: 0.065	loss: 0.907	
  location_date_sensor = 16  [n =      9]:	weight: 0.059	loss: 0.839	
  location_date_sensor = 17  [n =      7]:	weight: 0.028	loss: 0.601	
 60% 549/915 [13:58<09:18,  1.53s/it]objective: 0.177
loss_avg: 0.789
  location_date_sensor = 0  [n =     15]:	weight: 0.054	loss: 0.934	
  location_date_sensor = 1  [n =      8]:	weight: 0.018	loss: 0.592	
  location_date_sensor = 2  [n =     12]:	weight: 0.036	loss: 0.687	
  location_date_sensor = 3  [n =     12]:	weight: 0.040	loss: 0.677	
  location_date_sensor = 4  [n =     10]:	weight: 0.053	loss: 0.804	
  location_date_sensor = 5  [n =      8]:	weight: 0.033	loss: 0.585	
  location_date_sensor = 6  [n =     10]:	weight: 0.037	loss: 0.745	
  location_date_sensor = 7  [n =      8]:	weight: 0.102	loss: 0.862	
  location_date_sensor = 8  [n =      8]:	weight: 0.049	loss: 0.795	
  location_date_sensor = 9  [n =     12]:	weight: 0.103	loss: 0.827	
  location_date_sensor = 10  [n =     13]:	weight: 0.063	loss: 0.987	
  location_date_sensor = 11  [n =     13]:	weight: 0.070	loss: 0.789	
  location_date_sensor = 12  [n =     10]:	weight: 0.039	loss: 0.722	
  location_date_sensor = 13  [n =     17]:	weight: 0.084	loss: 0.855	
  location_date_sensor = 14  [n =     11]:	weight: 0.071	loss: 0.825	
  location_date_sensor = 15  [n =     10]:	weight: 0.065	loss: 0.905	
  location_date_sensor = 16  [n =      8]:	weight: 0.058	loss: 0.875	
  location_date_sensor = 17  [n =     15]:	weight: 0.028	loss: 0.626	
 65% 599/915 [15:15<08:00,  1.52s/it]objective: 0.165
loss_avg: 0.777
  location_date_sensor = 0  [n =     13]:	weight: 0.056	loss: 0.793	
  location_date_sensor = 1  [n =      9]:	weight: 0.017	loss: 0.570	
  location_date_sensor = 2  [n =      8]:	weight: 0.036	loss: 0.677	
  location_date_sensor = 3  [n =     10]:	weight: 0.040	loss: 0.706	
  location_date_sensor = 4  [n =     13]:	weight: 0.053	loss: 0.737	
  location_date_sensor = 5  [n =     13]:	weight: 0.032	loss: 0.818	
  location_date_sensor = 6  [n =     18]:	weight: 0.037	loss: 0.786	
  location_date_sensor = 7  [n =     14]:	weight: 0.104	loss: 0.854	
  location_date_sensor = 8  [n =      5]:	weight: 0.047	loss: 0.779	
  location_date_sensor = 9  [n =     12]:	weight: 0.104	loss: 0.762	
  location_date_sensor = 10  [n =      5]:	weight: 0.063	loss: 0.905	
  location_date_sensor = 11  [n =     13]:	weight: 0.071	loss: 0.786	
  location_date_sensor = 12  [n =     10]:	weight: 0.038	loss: 0.755	
  location_date_sensor = 13  [n =     16]:	weight: 0.087	loss: 0.877	
  location_date_sensor = 14  [n =      7]:	weight: 0.069	loss: 0.908	
  location_date_sensor = 15  [n =     11]:	weight: 0.065	loss: 0.935	
  location_date_sensor = 16  [n =     11]:	weight: 0.056	loss: 0.715	
  location_date_sensor = 17  [n =     12]:	weight: 0.028	loss: 0.614	
 71% 649/915 [16:31<06:46,  1.53s/it]objective: 0.167
loss_avg: 0.754
  location_date_sensor = 0  [n =     17]:	weight: 0.056	loss: 0.838	
  location_date_sensor = 1  [n =     13]:	weight: 0.017	loss: 0.555	
  location_date_sensor = 2  [n =     15]:	weight: 0.035	loss: 0.663	
  location_date_sensor = 3  [n =      9]:	weight: 0.039	loss: 0.707	
  location_date_sensor = 4  [n =     10]:	weight: 0.052	loss: 0.763	
  location_date_sensor = 5  [n =      7]:	weight: 0.032	loss: 0.542	
  location_date_sensor = 6  [n =     11]:	weight: 0.038	loss: 0.690	
  location_date_sensor = 7  [n =     14]:	weight: 0.106	loss: 0.853	
  location_date_sensor = 8  [n =     12]:	weight: 0.047	loss: 0.761	
  location_date_sensor = 9  [n =     12]:	weight: 0.104	loss: 0.800	
  location_date_sensor = 10  [n =     13]:	weight: 0.063	loss: 0.952	
  location_date_sensor = 11  [n =      3]:	weight: 0.070	loss: 0.708	
  location_date_sensor = 12  [n =     10]:	weight: 0.038	loss: 0.737	
  location_date_sensor = 13  [n =     11]:	weight: 0.089	loss: 0.864	
  location_date_sensor = 14  [n =      5]:	weight: 0.068	loss: 0.867	
  location_date_sensor = 15  [n =     11]:	weight: 0.066	loss: 0.836	
  location_date_sensor = 16  [n =     13]:	weight: 0.057	loss: 0.724	
  location_date_sensor = 17  [n =     14]:	weight: 0.028	loss: 0.643	
 76% 699/915 [17:47<05:29,  1.53s/it]objective: 0.169
loss_avg: 0.769
  location_date_sensor = 0  [n =     19]:	weight: 0.060	loss: 0.924	
  location_date_sensor = 1  [n =      9]:	weight: 0.016	loss: 0.560	
  location_date_sensor = 2  [n =     10]:	weight: 0.035	loss: 0.652	
  location_date_sensor = 3  [n =     11]:	weight: 0.039	loss: 0.662	
  location_date_sensor = 4  [n =     11]:	weight: 0.051	loss: 0.783	
  location_date_sensor = 5  [n =     10]:	weight: 0.031	loss: 0.558	
  location_date_sensor = 6  [n =      8]:	weight: 0.038	loss: 0.798	
  location_date_sensor = 7  [n =     14]:	weight: 0.108	loss: 0.820	
  location_date_sensor = 8  [n =     10]:	weight: 0.047	loss: 0.718	
  location_date_sensor = 9  [n =      7]:	weight: 0.102	loss: 0.804	
  location_date_sensor = 10  [n =     10]:	weight: 0.064	loss: 0.885	
  location_date_sensor = 11  [n =     10]:	weight: 0.066	loss: 0.809	
  location_date_sensor = 12  [n =     12]:	weight: 0.037	loss: 0.642	
  location_date_sensor = 13  [n =     15]:	weight: 0.093	loss: 0.885	
  location_date_sensor = 14  [n =      9]:	weight: 0.067	loss: 0.977	
  location_date_sensor = 15  [n =     10]:	weight: 0.065	loss: 0.821	
  location_date_sensor = 16  [n =     15]:	weight: 0.056	loss: 0.731	
  location_date_sensor = 17  [n =     10]:	weight: 0.027	loss: 0.659	
 82% 749/915 [19:04<04:14,  1.53s/it]objective: 0.172
loss_avg: 0.752
  location_date_sensor = 0  [n =      9]:	weight: 0.063	loss: 0.789	
  location_date_sensor = 1  [n =     11]:	weight: 0.016	loss: 0.559	
  location_date_sensor = 2  [n =     12]:	weight: 0.034	loss: 0.697	
  location_date_sensor = 3  [n =      5]:	weight: 0.036	loss: 0.713	
  location_date_sensor = 4  [n =     11]:	weight: 0.051	loss: 0.711	
  location_date_sensor = 5  [n =      9]:	weight: 0.029	loss: 0.600	
  location_date_sensor = 6  [n =     15]:	weight: 0.037	loss: 0.707	
  location_date_sensor = 7  [n =     12]:	weight: 0.112	loss: 0.799	
  location_date_sensor = 8  [n =     18]:	weight: 0.047	loss: 0.751	
  location_date_sensor = 9  [n =     12]:	weight: 0.102	loss: 0.773	
  location_date_sensor = 10  [n =     13]:	weight: 0.065	loss: 0.866	
  location_date_sensor = 11  [n =     12]:	weight: 0.066	loss: 0.805	
  location_date_sensor = 12  [n =      9]:	weight: 0.036	loss: 0.778	
  location_date_sensor = 13  [n =     16]:	weight: 0.096	loss: 0.860	
  location_date_sensor = 14  [n =      9]:	weight: 0.066	loss: 0.929	
  location_date_sensor = 15  [n =      7]:	weight: 0.064	loss: 0.880	
  location_date_sensor = 16  [n =     14]:	weight: 0.056	loss: 0.674	
  location_date_sensor = 17  [n =      6]:	weight: 0.026	loss: 0.575	
 87% 799/915 [20:20<02:57,  1.53s/it]objective: 0.152
loss_avg: 0.718
  location_date_sensor = 0  [n =     10]:	weight: 0.062	loss: 0.692	
  location_date_sensor = 1  [n =     12]:	weight: 0.016	loss: 0.485	
  location_date_sensor = 2  [n =      8]:	weight: 0.034	loss: 0.637	
  location_date_sensor = 3  [n =     12]:	weight: 0.036	loss: 0.616	
  location_date_sensor = 4  [n =     15]:	weight: 0.052	loss: 0.718	
  location_date_sensor = 5  [n =     11]:	weight: 0.028	loss: 0.580	
  location_date_sensor = 6  [n =     14]:	weight: 0.038	loss: 0.744	
  location_date_sensor = 7  [n =     10]:	weight: 0.112	loss: 0.700	
  location_date_sensor = 8  [n =      9]:	weight: 0.048	loss: 0.740	
  location_date_sensor = 9  [n =     14]:	weight: 0.102	loss: 0.761	
  location_date_sensor = 10  [n =     10]:	weight: 0.066	loss: 0.852	
  location_date_sensor = 11  [n =     11]:	weight: 0.066	loss: 0.739	
  location_date_sensor = 12  [n =     10]:	weight: 0.036	loss: 0.743	
  location_date_sensor = 13  [n =      8]:	weight: 0.096	loss: 0.844	
  location_date_sensor = 14  [n =     16]:	weight: 0.066	loss: 0.877	
  location_date_sensor = 15  [n =     13]:	weight: 0.064	loss: 0.842	
  location_date_sensor = 16  [n =      4]:	weight: 0.054	loss: 0.692	
  location_date_sensor = 17  [n =     13]:	weight: 0.026	loss: 0.613	
 93% 849/915 [21:36<01:40,  1.53s/it]objective: 0.171
loss_avg: 0.767
  location_date_sensor = 0  [n =     13]:	weight: 0.062	loss: 0.694	
  location_date_sensor = 1  [n =      9]:	weight: 0.015	loss: 0.506	
  location_date_sensor = 2  [n =      2]:	weight: 0.032	loss: 0.655	
  location_date_sensor = 3  [n =     11]:	weight: 0.035	loss: 0.643	
  location_date_sensor = 4  [n =     10]:	weight: 0.051	loss: 0.744	
  location_date_sensor = 5  [n =     12]:	weight: 0.028	loss: 0.611	
  location_date_sensor = 6  [n =     10]:	weight: 0.039	loss: 0.815	
  location_date_sensor = 7  [n =     11]:	weight: 0.110	loss: 0.772	
  location_date_sensor = 8  [n =     10]:	weight: 0.047	loss: 0.706	
  location_date_sensor = 9  [n =     10]:	weight: 0.103	loss: 0.827	
  location_date_sensor = 10  [n =     12]:	weight: 0.066	loss: 0.894	
  location_date_sensor = 11  [n =     15]:	weight: 0.066	loss: 0.764	
  location_date_sensor = 12  [n =     12]:	weight: 0.036	loss: 0.728	
  location_date_sensor = 13  [n =     13]:	weight: 0.098	loss: 0.926	
  location_date_sensor = 14  [n =     14]:	weight: 0.069	loss: 0.910	
  location_date_sensor = 15  [n =     17]:	weight: 0.067	loss: 0.914	
  location_date_sensor = 16  [n =      9]:	weight: 0.053	loss: 0.847	
  location_date_sensor = 17  [n =     10]:	weight: 0.025	loss: 0.592	
 98% 899/915 [22:53<00:24,  1.53s/it]objective: 0.171
loss_avg: 0.763
  location_date_sensor = 0  [n =     12]:	weight: 0.062	loss: 0.832	
  location_date_sensor = 1  [n =     11]:	weight: 0.014	loss: 0.469	
  location_date_sensor = 2  [n =     14]:	weight: 0.031	loss: 0.653	
  location_date_sensor = 3  [n =      8]:	weight: 0.035	loss: 0.690	
  location_date_sensor = 4  [n =     16]:	weight: 0.052	loss: 0.747	
  location_date_sensor = 5  [n =      6]:	weight: 0.028	loss: 0.608	
  location_date_sensor = 6  [n =     10]:	weight: 0.039	loss: 0.847	
  location_date_sensor = 7  [n =     18]:	weight: 0.113	loss: 0.805	
  location_date_sensor = 8  [n =      9]:	weight: 0.046	loss: 0.801	
  location_date_sensor = 9  [n =      9]:	weight: 0.100	loss: 0.812	
  location_date_sensor = 10  [n =     12]:	weight: 0.067	loss: 0.849	
  location_date_sensor = 11  [n =      9]:	weight: 0.065	loss: 0.732	
  location_date_sensor = 12  [n =     14]:	weight: 0.036	loss: 0.779	
  location_date_sensor = 13  [n =     12]:	weight: 0.101	loss: 0.955	
  location_date_sensor = 14  [n =      6]:	weight: 0.068	loss: 0.844	
  location_date_sensor = 15  [n =     12]:	weight: 0.070	loss: 0.904	
  location_date_sensor = 16  [n =     10]:	weight: 0.052	loss: 0.725	
  location_date_sensor = 17  [n =     12]:	weight: 0.025	loss: 0.611	
100% 915/915 [23:16<00:00,  1.53s/it]
objective: 0.123
loss_avg: 0.684
  location_date_sensor = 0  [n =      3]:	weight: 0.062	loss: 0.680	
  location_date_sensor = 1  [n =      6]:	weight: 0.014	loss: 0.499	
  location_date_sensor = 2  [n =      5]:	weight: 0.031	loss: 0.705	
  location_date_sensor = 3  [n =      4]:	weight: 0.034	loss: 0.653	
  location_date_sensor = 4  [n =      3]:	weight: 0.052	loss: 0.794	
  location_date_sensor = 5  [n =      6]:	weight: 0.027	loss: 0.547	
  location_date_sensor = 6  [n =      2]:	weight: 0.039	loss: 0.693	
  location_date_sensor = 7  [n =      1]:	weight: 0.116	loss: 0.649	
  location_date_sensor = 8  [n =      1]:	weight: 0.046	loss: 0.695	
  location_date_sensor = 9  [n =      4]:	weight: 0.099	loss: 0.760	
  location_date_sensor = 10  [n =      3]:	weight: 0.068	loss: 0.917	
  location_date_sensor = 11  [n =      3]:	weight: 0.065	loss: 0.774	
  location_date_sensor = 12  [n =      4]:	weight: 0.037	loss: 0.707	
  location_date_sensor = 13  [n =      1]:	weight: 0.101	loss: 0.844	
  location_date_sensor = 14  [n =      2]:	weight: 0.066	loss: 0.947	
  location_date_sensor = 15  [n =      3]:	weight: 0.071	loss: 0.788	
  location_date_sensor = 16  [n =      2]:	weight: 0.051	loss: 0.635	
  location_date_sensor = 17  [n =      4]:	weight: 0.024	loss: 0.533	
Epoch eval:
Average detection_acc: 0.722
  location_date_sensor = 0  [n =    211]:	detection_acc = 0.702
  location_date_sensor = 1  [n =    194]:	detection_acc = 0.878
  location_date_sensor = 2  [n =    190]:	detection_acc = 0.848
  location_date_sensor = 3  [n =    188]:	detection_acc = 0.768
  location_date_sensor = 4  [n =    212]:	detection_acc = 0.745
  location_date_sensor = 5  [n =    192]:	detection_acc = 0.771
  location_date_sensor = 6  [n =    191]:	detection_acc = 0.746
  location_date_sensor = 7  [n =    211]:	detection_acc = 0.657
  location_date_sensor = 8  [n =    191]:	detection_acc = 0.706
  location_date_sensor = 9  [n =    213]:	detection_acc = 0.593
  location_date_sensor = 10  [n =    194]:	detection_acc = 0.659
  location_date_sensor = 11  [n =    211]:	detection_acc = 0.608
  location_date_sensor = 12  [n =    204]:	detection_acc = 0.725
  location_date_sensor = 13  [n =    225]:	detection_acc = 0.707
  location_date_sensor = 14  [n =    186]:	detection_acc = 0.713
  location_date_sensor = 15  [n =    205]:	detection_acc = 0.754
  location_date_sensor = 16  [n =    219]:	detection_acc = 0.626
  location_date_sensor = 17  [n =    220]:	detection_acc = 0.818
Worst-group detection_acc: 0.593

Validation:
100% 348/348 [03:19<00:00,  1.75it/s]
objective: 0.000
loss_avg: 1.000
  location_date_sensor = 18  [n =     12]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 19  [n =     49]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 20  [n =    254]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 21  [n =    216]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 22  [n =     89]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 23  [n =     11]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 24  [n =      4]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 25  [n =      7]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 26  [n =     14]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 27  [n =     17]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 28  [n =     14]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 29  [n =      8]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 30  [n =     43]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 31  [n =     55]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 32  [n =     51]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 33  [n =     50]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 34  [n =     28]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 35  [n =     75]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 36  [n =     56]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 37  [n =     34]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 38  [n =     55]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 39  [n =     13]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 40  [n =     19]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 41  [n =     19]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 42  [n =     57]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 43  [n =     39]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 44  [n =     33]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 45  [n =     39]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 46  [n =     30]:	weight: 0.000	loss: 1.000	
Epoch eval:
Average detection_acc: 0.009
  location_date_sensor = 18  [n =     12]:	detection_acc = 0.000
  location_date_sensor = 19  [n =     49]:	detection_acc = 0.020
  location_date_sensor = 20  [n =    254]:	detection_acc = 0.000
  location_date_sensor = 21  [n =    216]:	detection_acc = 0.000
  location_date_sensor = 22  [n =     89]:	detection_acc = 0.000
  location_date_sensor = 23  [n =     11]:	detection_acc = 0.000
  location_date_sensor = 24  [n =      4]:	detection_acc = 0.500
  location_date_sensor = 25  [n =      7]:	detection_acc = 0.286
  location_date_sensor = 26  [n =     14]:	detection_acc = 0.000
  location_date_sensor = 27  [n =     17]:	detection_acc = 0.059
  location_date_sensor = 28  [n =     14]:	detection_acc = 0.000
  location_date_sensor = 29  [n =      8]:	detection_acc = 0.000
  location_date_sensor = 30  [n =     43]:	detection_acc = 0.000
  location_date_sensor = 31  [n =     55]:	detection_acc = 0.000
  location_date_sensor = 32  [n =     51]:	detection_acc = 0.000
  location_date_sensor = 33  [n =     50]:	detection_acc = 0.000
  location_date_sensor = 34  [n =     28]:	detection_acc = 0.000
  location_date_sensor = 35  [n =     75]:	detection_acc = 0.040
  location_date_sensor = 36  [n =     56]:	detection_acc = 0.000
  location_date_sensor = 37  [n =     34]:	detection_acc = 0.000
  location_date_sensor = 38  [n =     55]:	detection_acc = 0.000
  location_date_sensor = 39  [n =     13]:	detection_acc = 0.000
  location_date_sensor = 40  [n =     19]:	detection_acc = 0.000
  location_date_sensor = 41  [n =     19]:	detection_acc = 0.000
  location_date_sensor = 42  [n =     57]:	detection_acc = 0.000
  location_date_sensor = 43  [n =     39]:	detection_acc = 0.000
  location_date_sensor = 44  [n =     33]:	detection_acc = 0.000
  location_date_sensor = 45  [n =     39]:	detection_acc = 0.026
  location_date_sensor = 46  [n =     30]:	detection_acc = 0.100
Worst-group detection_acc: 0.000
Validation detection_acc_avg: 0.009
Epoch 1 has the best validation performance so far.
100% 365/365 [03:28<00:00,  1.75it/s]


Epoch [2]:

Train:
  5% 49/915 [01:15<22:02,  1.53s/it]objective: 0.159
loss_avg: 0.736
  location_date_sensor = 0  [n =      8]:	weight: 0.061	loss: 0.693	
  location_date_sensor = 1  [n =     10]:	weight: 0.014	loss: 0.549	
  location_date_sensor = 2  [n =     14]:	weight: 0.031	loss: 0.729	
  location_date_sensor = 3  [n =     12]:	weight: 0.034	loss: 0.600	
  location_date_sensor = 4  [n =     14]:	weight: 0.052	loss: 0.702	
  location_date_sensor = 5  [n =     10]:	weight: 0.026	loss: 0.470	
  location_date_sensor = 6  [n =      8]:	weight: 0.038	loss: 0.771	
  location_date_sensor = 7  [n =      8]:	weight: 0.113	loss: 0.781	
  location_date_sensor = 8  [n =     11]:	weight: 0.046	loss: 0.760	
  location_date_sensor = 9  [n =     12]:	weight: 0.100	loss: 0.770	
  location_date_sensor = 10  [n =     13]:	weight: 0.069	loss: 0.857	
  location_date_sensor = 11  [n =      8]:	weight: 0.064	loss: 0.767	
  location_date_sensor = 12  [n =     11]:	weight: 0.037	loss: 0.699	
  location_date_sensor = 13  [n =     13]:	weight: 0.103	loss: 0.874	
  location_date_sensor = 14  [n =     10]:	weight: 0.065	loss: 0.882	
  location_date_sensor = 15  [n =      8]:	weight: 0.071	loss: 0.927	
  location_date_sensor = 16  [n =     20]:	weight: 0.053	loss: 0.760	
  location_date_sensor = 17  [n =     10]:	weight: 0.024	loss: 0.644	
 11% 99/915 [02:31<20:46,  1.53s/it]objective: 0.154
loss_avg: 0.719
  location_date_sensor = 0  [n =     12]:	weight: 0.061	loss: 0.714	
  location_date_sensor = 1  [n =     18]:	weight: 0.014	loss: 0.534	
  location_date_sensor = 2  [n =      7]:	weight: 0.031	loss: 0.634	
  location_date_sensor = 3  [n =     17]:	weight: 0.034	loss: 0.623	
  location_date_sensor = 4  [n =      6]:	weight: 0.052	loss: 0.773	
  location_date_sensor = 5  [n =     11]:	weight: 0.026	loss: 0.551	
  location_date_sensor = 6  [n =      7]:	weight: 0.037	loss: 0.647	
  location_date_sensor = 7  [n =     12]:	weight: 0.113	loss: 0.759	
  location_date_sensor = 8  [n =     10]:	weight: 0.045	loss: 0.671	
  location_date_sensor = 9  [n =     10]:	weight: 0.102	loss: 0.786	
  location_date_sensor = 10  [n =     11]:	weight: 0.070	loss: 0.941	
  location_date_sensor = 11  [n =     19]:	weight: 0.064	loss: 0.686	
  location_date_sensor = 12  [n =      8]:	weight: 0.036	loss: 0.723	
  location_date_sensor = 13  [n =     11]:	weight: 0.104	loss: 0.875	
  location_date_sensor = 14  [n =      6]:	weight: 0.066	loss: 0.902	
  location_date_sensor = 15  [n =     12]:	weight: 0.071	loss: 0.859	
  location_date_sensor = 16  [n =     15]:	weight: 0.055	loss: 0.772	
  location_date_sensor = 17  [n =      8]:	weight: 0.024	loss: 0.692	
 16% 149/915 [03:47<19:29,  1.53s/it]objective: 0.160
loss_avg: 0.739
  location_date_sensor = 0  [n =     10]:	weight: 0.061	loss: 0.808	
  location_date_sensor = 1  [n =      9]:	weight: 0.014	loss: 0.543	
  location_date_sensor = 2  [n =     13]:	weight: 0.030	loss: 0.691	
  location_date_sensor = 3  [n =      8]:	weight: 0.033	loss: 0.668	
  location_date_sensor = 4  [n =     10]:	weight: 0.051	loss: 0.729	
  location_date_sensor = 5  [n =     13]:	weight: 0.026	loss: 0.582	
  location_date_sensor = 6  [n =      4]:	weight: 0.036	loss: 0.785	
  location_date_sensor = 7  [n =     11]:	weight: 0.111	loss: 0.711	
  location_date_sensor = 8  [n =     17]:	weight: 0.046	loss: 0.701	
  location_date_sensor = 9  [n =     17]:	weight: 0.104	loss: 0.787	
  location_date_sensor = 10  [n =     14]:	weight: 0.072	loss: 0.898	
  location_date_sensor = 11  [n =     15]:	weight: 0.066	loss: 0.677	
  location_date_sensor = 12  [n =      5]:	weight: 0.035	loss: 0.812	
  location_date_sensor = 13  [n =      9]:	weight: 0.103	loss: 0.802	
  location_date_sensor = 14  [n =     12]:	weight: 0.066	loss: 0.862	
  location_date_sensor = 15  [n =     15]:	weight: 0.072	loss: 0.887	
  location_date_sensor = 16  [n =     10]:	weight: 0.054	loss: 0.692	
  location_date_sensor = 17  [n =      8]:	weight: 0.023	loss: 0.613	
 22% 199/915 [05:04<18:11,  1.52s/it]objective: 0.164
loss_avg: 0.734
  location_date_sensor = 0  [n =      8]:	weight: 0.060	loss: 0.643	
  location_date_sensor = 1  [n =     11]:	weight: 0.014	loss: 0.587	
  location_date_sensor = 2  [n =     10]:	weight: 0.030	loss: 0.613	
  location_date_sensor = 3  [n =      8]:	weight: 0.032	loss: 0.584	
  location_date_sensor = 4  [n =     14]:	weight: 0.051	loss: 0.765	
  location_date_sensor = 5  [n =     12]:	weight: 0.025	loss: 0.656	
  location_date_sensor = 6  [n =      9]:	weight: 0.035	loss: 0.693	
  location_date_sensor = 7  [n =     16]:	weight: 0.113	loss: 0.844	
  location_date_sensor = 8  [n =      9]:	weight: 0.046	loss: 0.716	
  location_date_sensor = 9  [n =     10]:	weight: 0.107	loss: 0.756	
  location_date_sensor = 10  [n =     11]:	weight: 0.073	loss: 0.809	
  location_date_sensor = 11  [n =     12]:	weight: 0.066	loss: 0.708	
  location_date_sensor = 12  [n =     11]:	weight: 0.034	loss: 0.781	
  location_date_sensor = 13  [n =      9]:	weight: 0.102	loss: 0.851	
  location_date_sensor = 14  [n =     13]:	weight: 0.066	loss: 0.858	
  location_date_sensor = 15  [n =      8]:	weight: 0.074	loss: 0.824	
  location_date_sensor = 16  [n =     17]:	weight: 0.054	loss: 0.775	
  location_date_sensor = 17  [n =     12]:	weight: 0.022	loss: 0.624	
 27% 249/915 [06:20<17:00,  1.53s/it]objective: 0.161
loss_avg: 0.741
  location_date_sensor = 0  [n =     13]:	weight: 0.060	loss: 0.766	
  location_date_sensor = 1  [n =     12]:	weight: 0.013	loss: 0.561	
  location_date_sensor = 2  [n =     15]:	weight: 0.030	loss: 0.712	
  location_date_sensor = 3  [n =     12]:	weight: 0.032	loss: 0.642	
  location_date_sensor = 4  [n =     12]:	weight: 0.053	loss: 0.754	
  location_date_sensor = 5  [n =      7]:	weight: 0.025	loss: 0.724	
  location_date_sensor = 6  [n =     10]:	weight: 0.034	loss: 0.792	
  location_date_sensor = 7  [n =     12]:	weight: 0.117	loss: 0.706	
  location_date_sensor = 8  [n =     21]:	weight: 0.047	loss: 0.705	
  location_date_sensor = 9  [n =      7]:	weight: 0.102	loss: 0.720	
  location_date_sensor = 10  [n =     11]:	weight: 0.074	loss: 0.901	
  location_date_sensor = 11  [n =      4]:	weight: 0.064	loss: 0.777	
  location_date_sensor = 12  [n =      9]:	weight: 0.034	loss: 0.744	
  location_date_sensor = 13  [n =     11]:	weight: 0.102	loss: 0.845	
  location_date_sensor = 14  [n =     15]:	weight: 0.068	loss: 0.843	
  location_date_sensor = 15  [n =     10]:	weight: 0.074	loss: 0.936	
  location_date_sensor = 16  [n =      5]:	weight: 0.054	loss: 0.694	
  location_date_sensor = 17  [n =     14]:	weight: 0.022	loss: 0.598	
 33% 299/915 [07:36<15:40,  1.53s/it]objective: 0.159
loss_avg: 0.732
  location_date_sensor = 0  [n =     10]:	weight: 0.059	loss: 0.648	
  location_date_sensor = 1  [n =      9]:	weight: 0.013	loss: 0.550	
  location_date_sensor = 2  [n =     12]:	weight: 0.030	loss: 0.676	
  location_date_sensor = 3  [n =      8]:	weight: 0.031	loss: 0.629	
  location_date_sensor = 4  [n =     15]:	weight: 0.053	loss: 0.716	
  location_date_sensor = 5  [n =      8]:	weight: 0.024	loss: 0.810	
  location_date_sensor = 6  [n =      7]:	weight: 0.034	loss: 0.768	
  location_date_sensor = 7  [n =     16]:	weight: 0.117	loss: 0.770	
  location_date_sensor = 8  [n =      9]:	weight: 0.047	loss: 0.741	
  location_date_sensor = 9  [n =     13]:	weight: 0.103	loss: 0.716	
  location_date_sensor = 10  [n =     17]:	weight: 0.077	loss: 0.930	
  location_date_sensor = 11  [n =     14]:	weight: 0.063	loss: 0.659	
  location_date_sensor = 12  [n =     12]:	weight: 0.034	loss: 0.670	
  location_date_sensor = 13  [n =      6]:	weight: 0.100	loss: 0.807	
  location_date_sensor = 14  [n =      5]:	weight: 0.068	loss: 0.928	
  location_date_sensor = 15  [n =      8]:	weight: 0.073	loss: 0.864	
  location_date_sensor = 16  [n =     18]:	weight: 0.054	loss: 0.757	
  location_date_sensor = 17  [n =     13]:	weight: 0.022	loss: 0.597	
 38% 349/915 [08:53<14:20,  1.52s/it]objective: 0.150
loss_avg: 0.742
  location_date_sensor = 0  [n =     11]:	weight: 0.059	loss: 0.903	
  location_date_sensor = 1  [n =     13]:	weight: 0.013	loss: 0.568	
  location_date_sensor = 2  [n =     13]:	weight: 0.030	loss: 0.622	
  location_date_sensor = 3  [n =     17]:	weight: 0.031	loss: 0.623	
  location_date_sensor = 4  [n =     11]:	weight: 0.053	loss: 0.696	
  location_date_sensor = 5  [n =      9]:	weight: 0.024	loss: 0.508	
  location_date_sensor = 6  [n =     12]:	weight: 0.033	loss: 0.816	
  location_date_sensor = 7  [n =      6]:	weight: 0.117	loss: 0.763	
  location_date_sensor = 8  [n =      5]:	weight: 0.046	loss: 0.728	
  location_date_sensor = 9  [n =     11]:	weight: 0.103	loss: 0.751	
  location_date_sensor = 10  [n =      7]:	weight: 0.079	loss: 0.980	
  location_date_sensor = 11  [n =      7]:	weight: 0.063	loss: 0.687	
  location_date_sensor = 12  [n =     16]:	weight: 0.034	loss: 0.760	
  location_date_sensor = 13  [n =     12]:	weight: 0.100	loss: 0.855	
  location_date_sensor = 14  [n =     12]:	weight: 0.069	loss: 1.006	
  location_date_sensor = 15  [n =     13]:	weight: 0.074	loss: 0.884	
  location_date_sensor = 16  [n =     13]:	weight: 0.055	loss: 0.719	
  location_date_sensor = 17  [n =     12]:	weight: 0.022	loss: 0.581	
 44% 399/915 [10:09<13:06,  1.52s/it]objective: 0.157
loss_avg: 0.718
  location_date_sensor = 0  [n =     17]:	weight: 0.060	loss: 0.748	
  location_date_sensor = 1  [n =     11]:	weight: 0.013	loss: 0.578	
  location_date_sensor = 2  [n =     15]:	weight: 0.030	loss: 0.656	
  location_date_sensor = 3  [n =     10]:	weight: 0.031	loss: 0.547	
  location_date_sensor = 4  [n =      8]:	weight: 0.053	loss: 0.731	
  location_date_sensor = 5  [n =     10]:	weight: 0.023	loss: 0.733	
  location_date_sensor = 6  [n =     14]:	weight: 0.033	loss: 0.694	
  location_date_sensor = 7  [n =     12]:	weight: 0.116	loss: 0.715	
  location_date_sensor = 8  [n =      9]:	weight: 0.045	loss: 0.694	
  location_date_sensor = 9  [n =      8]:	weight: 0.102	loss: 0.742	
  location_date_sensor = 10  [n =     12]:	weight: 0.078	loss: 0.865	
  location_date_sensor = 11  [n =     17]:	weight: 0.063	loss: 0.712	
  location_date_sensor = 12  [n =      9]:	weight: 0.034	loss: 0.691	
  location_date_sensor = 13  [n =     15]:	weight: 0.101	loss: 0.817	
  location_date_sensor = 14  [n =      6]:	weight: 0.069	loss: 0.897	
  location_date_sensor = 15  [n =     13]:	weight: 0.076	loss: 0.826	
  location_date_sensor = 16  [n =      8]:	weight: 0.055	loss: 0.615	
  location_date_sensor = 17  [n =      6]:	weight: 0.021	loss: 0.605	
 49% 449/915 [11:26<11:53,  1.53s/it]objective: 0.142
loss_avg: 0.715
  location_date_sensor = 0  [n =      8]:	weight: 0.060	loss: 0.618	
  location_date_sensor = 1  [n =      7]:	weight: 0.012	loss: 0.550	
  location_date_sensor = 2  [n =     12]:	weight: 0.030	loss: 0.656	
  location_date_sensor = 3  [n =      8]:	weight: 0.030	loss: 0.606	
  location_date_sensor = 4  [n =     12]:	weight: 0.053	loss: 0.748	
  location_date_sensor = 5  [n =      9]:	weight: 0.023	loss: 0.606	
  location_date_sensor = 6  [n =      7]:	weight: 0.033	loss: 0.699	
  location_date_sensor = 7  [n =      2]:	weight: 0.113	loss: 0.841	
  location_date_sensor = 8  [n =     14]:	weight: 0.045	loss: 0.713	
  location_date_sensor = 9  [n =     13]:	weight: 0.103	loss: 0.720	
  location_date_sensor = 10  [n =     10]:	weight: 0.079	loss: 0.829	
  location_date_sensor = 11  [n =     16]:	weight: 0.064	loss: 0.682	
  location_date_sensor = 12  [n =     18]:	weight: 0.034	loss: 0.742	
  location_date_sensor = 13  [n =      9]:	weight: 0.101	loss: 0.687	
  location_date_sensor = 14  [n =     10]:	weight: 0.067	loss: 0.868	
  location_date_sensor = 15  [n =     12]:	weight: 0.078	loss: 0.867	
  location_date_sensor = 16  [n =     13]:	weight: 0.056	loss: 0.811	
  location_date_sensor = 17  [n =     20]:	weight: 0.021	loss: 0.640	
 55% 499/915 [12:42<10:35,  1.53s/it]objective: 0.141
loss_avg: 0.685
  location_date_sensor = 0  [n =     10]:	weight: 0.060	loss: 0.678	
  location_date_sensor = 1  [n =     23]:	weight: 0.012	loss: 0.530	
  location_date_sensor = 2  [n =      8]:	weight: 0.030	loss: 0.540	
  location_date_sensor = 3  [n =     11]:	weight: 0.029	loss: 0.637	
  location_date_sensor = 4  [n =     11]:	weight: 0.053	loss: 0.675	
  location_date_sensor = 5  [n =      6]:	weight: 0.022	loss: 0.657	
  location_date_sensor = 6  [n =     15]:	weight: 0.033	loss: 0.747	
  location_date_sensor = 7  [n =     10]:	weight: 0.110	loss: 0.651	
  location_date_sensor = 8  [n =      7]:	weight: 0.044	loss: 0.611	
  location_date_sensor = 9  [n =     10]:	weight: 0.102	loss: 0.702	
  location_date_sensor = 10  [n =     11]:	weight: 0.079	loss: 0.830	
  location_date_sensor = 11  [n =     15]:	weight: 0.065	loss: 0.677	
  location_date_sensor = 12  [n =      9]:	weight: 0.035	loss: 0.721	
  location_date_sensor = 13  [n =     14]:	weight: 0.102	loss: 0.775	
  location_date_sensor = 14  [n =      8]:	weight: 0.067	loss: 0.857	
  location_date_sensor = 15  [n =     10]:	weight: 0.078	loss: 0.843	
  location_date_sensor = 16  [n =     11]:	weight: 0.057	loss: 0.740	
  location_date_sensor = 17  [n =     11]:	weight: 0.022	loss: 0.567	
 60% 549/915 [13:58<09:20,  1.53s/it]objective: 0.160
loss_avg: 0.711
  location_date_sensor = 0  [n =      3]:	weight: 0.057	loss: 0.552	
  location_date_sensor = 1  [n =     11]:	weight: 0.012	loss: 0.481	
  location_date_sensor = 2  [n =     12]:	weight: 0.029	loss: 0.706	
  location_date_sensor = 3  [n =     10]:	weight: 0.030	loss: 0.652	
  location_date_sensor = 4  [n =     15]:	weight: 0.054	loss: 0.682	
  location_date_sensor = 5  [n =      6]:	weight: 0.021	loss: 0.515	
  location_date_sensor = 6  [n =     10]:	weight: 0.032	loss: 0.726	
  location_date_sensor = 7  [n =     14]:	weight: 0.110	loss: 0.720	
  location_date_sensor = 8  [n =     17]:	weight: 0.044	loss: 0.679	
  location_date_sensor = 9  [n =     13]:	weight: 0.103	loss: 0.737	
  location_date_sensor = 10  [n =     10]:	weight: 0.078	loss: 0.855	
  location_date_sensor = 11  [n =      9]:	weight: 0.066	loss: 0.716	
  location_date_sensor = 12  [n =     11]:	weight: 0.035	loss: 0.767	
  location_date_sensor = 13  [n =     10]:	weight: 0.104	loss: 0.798	
  location_date_sensor = 14  [n =     14]:	weight: 0.070	loss: 0.892	
  location_date_sensor = 15  [n =     10]:	weight: 0.079	loss: 0.897	
  location_date_sensor = 16  [n =     12]:	weight: 0.057	loss: 0.630	
  location_date_sensor = 17  [n =     13]:	weight: 0.021	loss: 0.617	
 65% 599/915 [15:14<08:01,  1.52s/it]objective: 0.145
loss_avg: 0.708
  location_date_sensor = 0  [n =      6]:	weight: 0.056	loss: 0.596	
  location_date_sensor = 1  [n =     16]:	weight: 0.012	loss: 0.512	
  location_date_sensor = 2  [n =     18]:	weight: 0.030	loss: 0.600	
  location_date_sensor = 3  [n =      8]:	weight: 0.029	loss: 0.630	
  location_date_sensor = 4  [n =      9]:	weight: 0.053	loss: 0.697	
  location_date_sensor = 5  [n =     15]:	weight: 0.021	loss: 0.690	
  location_date_sensor = 6  [n =     12]:	weight: 0.032	loss: 0.734	
  location_date_sensor = 7  [n =     14]:	weight: 0.113	loss: 0.758	
  location_date_sensor = 8  [n =      6]:	weight: 0.044	loss: 0.624	
  location_date_sensor = 9  [n =      8]:	weight: 0.103	loss: 0.694	
  location_date_sensor = 10  [n =     13]:	weight: 0.081	loss: 0.839	
  location_date_sensor = 11  [n =     16]:	weight: 0.066	loss: 0.644	
  location_date_sensor = 12  [n =      9]:	weight: 0.035	loss: 0.740	
  location_date_sensor = 13  [n =      8]:	weight: 0.101	loss: 0.773	
  location_date_sensor = 14  [n =     10]:	weight: 0.071	loss: 0.822	
  location_date_sensor = 15  [n =     15]:	weight: 0.081	loss: 0.889	
  location_date_sensor = 16  [n =      7]:	weight: 0.054	loss: 0.829	
  location_date_sensor = 17  [n =     10]:	weight: 0.021	loss: 0.721	
 71% 649/915 [16:31<06:46,  1.53s/it]objective: 0.145
loss_avg: 0.705
  location_date_sensor = 0  [n =     10]:	weight: 0.054	loss: 0.736	
  location_date_sensor = 1  [n =     11]:	weight: 0.012	loss: 0.580	
  location_date_sensor = 2  [n =     13]:	weight: 0.030	loss: 0.614	
  location_date_sensor = 3  [n =      6]:	weight: 0.028	loss: 0.665	
  location_date_sensor = 4  [n =     11]:	weight: 0.053	loss: 0.712	
  location_date_sensor = 5  [n =     14]:	weight: 0.021	loss: 0.667	
  location_date_sensor = 6  [n =     12]:	weight: 0.032	loss: 0.655	
  location_date_sensor = 7  [n =     13]:	weight: 0.115	loss: 0.684	
  location_date_sensor = 8  [n =     13]:	weight: 0.043	loss: 0.704	
  location_date_sensor = 9  [n =      9]:	weight: 0.103	loss: 0.776	
  location_date_sensor = 10  [n =     11]:	weight: 0.082	loss: 0.854	
  location_date_sensor = 11  [n =      9]:	weight: 0.065	loss: 0.646	
  location_date_sensor = 12  [n =      6]:	weight: 0.034	loss: 0.804	
  location_date_sensor = 13  [n =     11]:	weight: 0.100	loss: 0.835	
  location_date_sensor = 14  [n =     14]:	weight: 0.073	loss: 0.853	
  location_date_sensor = 15  [n =      9]:	weight: 0.082	loss: 0.841	
  location_date_sensor = 16  [n =     10]:	weight: 0.054	loss: 0.630	
  location_date_sensor = 17  [n =     18]:	weight: 0.021	loss: 0.564	
 76% 699/915 [17:47<05:29,  1.52s/it]objective: 0.140
loss_avg: 0.703
  location_date_sensor = 0  [n =      9]:	weight: 0.053	loss: 0.695	
  location_date_sensor = 1  [n =     10]:	weight: 0.012	loss: 0.536	
  location_date_sensor = 2  [n =     11]:	weight: 0.030	loss: 0.606	
  location_date_sensor = 3  [n =     17]:	weight: 0.028	loss: 0.657	
  location_date_sensor = 4  [n =      8]:	weight: 0.053	loss: 0.645	
  location_date_sensor = 5  [n =     13]:	weight: 0.021	loss: 0.752	
  location_date_sensor = 6  [n =      9]:	weight: 0.032	loss: 0.685	
  location_date_sensor = 7  [n =      8]:	weight: 0.114	loss: 0.629	
  location_date_sensor = 8  [n =      8]:	weight: 0.042	loss: 0.694	
  location_date_sensor = 9  [n =      8]:	weight: 0.102	loss: 0.798	
  location_date_sensor = 10  [n =      9]:	weight: 0.082	loss: 0.859	
  location_date_sensor = 11  [n =      9]:	weight: 0.064	loss: 0.614	
  location_date_sensor = 12  [n =     12]:	weight: 0.034	loss: 0.695	
  location_date_sensor = 13  [n =     11]:	weight: 0.102	loss: 0.784	
  location_date_sensor = 14  [n =      8]:	weight: 0.073	loss: 0.892	
  location_date_sensor = 15  [n =     17]:	weight: 0.085	loss: 0.867	
  location_date_sensor = 16  [n =     14]:	weight: 0.054	loss: 0.671	
  location_date_sensor = 17  [n =     19]:	weight: 0.022	loss: 0.614	
 82% 749/915 [19:04<04:12,  1.52s/it]objective: 0.143
loss_avg: 0.705
  location_date_sensor = 0  [n =     14]:	weight: 0.054	loss: 0.800	
  location_date_sensor = 1  [n =     10]:	weight: 0.012	loss: 0.472	
  location_date_sensor = 2  [n =      9]:	weight: 0.029	loss: 0.642	
  location_date_sensor = 3  [n =     25]:	weight: 0.029	loss: 0.668	
  location_date_sensor = 4  [n =     13]:	weight: 0.053	loss: 0.704	
  location_date_sensor = 5  [n =     16]:	weight: 0.022	loss: 0.583	
  location_date_sensor = 6  [n =     11]:	weight: 0.032	loss: 0.713	
  location_date_sensor = 7  [n =     13]:	weight: 0.114	loss: 0.660	
  location_date_sensor = 8  [n =      6]:	weight: 0.042	loss: 0.664	
  location_date_sensor = 9  [n =      6]:	weight: 0.099	loss: 0.720	
  location_date_sensor = 10  [n =     12]:	weight: 0.082	loss: 0.845	
  location_date_sensor = 11  [n =     13]:	weight: 0.063	loss: 0.668	
  location_date_sensor = 12  [n =      7]:	weight: 0.034	loss: 0.690	
  location_date_sensor = 13  [n =     10]:	weight: 0.103	loss: 0.785	
  location_date_sensor = 14  [n =     13]:	weight: 0.074	loss: 0.881	
  location_date_sensor = 15  [n =      8]:	weight: 0.087	loss: 0.844	
  location_date_sensor = 16  [n =      4]:	weight: 0.053	loss: 0.830	
  location_date_sensor = 17  [n =     10]:	weight: 0.022	loss: 0.636	
 87% 799/915 [20:20<02:57,  1.53s/it]objective: 0.146
loss_avg: 0.695
  location_date_sensor = 0  [n =     14]:	weight: 0.056	loss: 0.681	
  location_date_sensor = 1  [n =      7]:	weight: 0.011	loss: 0.521	
  location_date_sensor = 2  [n =     15]:	weight: 0.029	loss: 0.637	
  location_date_sensor = 3  [n =      9]:	weight: 0.030	loss: 0.592	
  location_date_sensor = 4  [n =      5]:	weight: 0.052	loss: 0.602	
  location_date_sensor = 5  [n =     11]:	weight: 0.021	loss: 0.437	
  location_date_sensor = 6  [n =     11]:	weight: 0.031	loss: 0.739	
  location_date_sensor = 7  [n =     12]:	weight: 0.113	loss: 0.688	
  location_date_sensor = 8  [n =     14]:	weight: 0.041	loss: 0.683	
  location_date_sensor = 9  [n =     14]:	weight: 0.099	loss: 0.829	
  location_date_sensor = 10  [n =     12]:	weight: 0.084	loss: 0.802	
  location_date_sensor = 11  [n =     10]:	weight: 0.063	loss: 0.616	
  location_date_sensor = 12  [n =     14]:	weight: 0.033	loss: 0.702	
  location_date_sensor = 13  [n =      9]:	weight: 0.103	loss: 0.825	
  location_date_sensor = 14  [n =     11]:	weight: 0.076	loss: 0.843	
  location_date_sensor = 15  [n =     11]:	weight: 0.087	loss: 0.876	
  location_date_sensor = 16  [n =     10]:	weight: 0.052	loss: 0.711	
  location_date_sensor = 17  [n =     11]:	weight: 0.022	loss: 0.596	
 93% 849/915 [21:37<01:40,  1.53s/it]objective: 0.151
loss_avg: 0.693
  location_date_sensor = 0  [n =     14]:	weight: 0.056	loss: 0.607	
  location_date_sensor = 1  [n =     10]:	weight: 0.011	loss: 0.551	
  location_date_sensor = 2  [n =      8]:	weight: 0.029	loss: 0.659	
  location_date_sensor = 3  [n =     11]:	weight: 0.030	loss: 0.585	
  location_date_sensor = 4  [n =     12]:	weight: 0.052	loss: 0.641	
  location_date_sensor = 5  [n =     12]:	weight: 0.021	loss: 0.655	
  location_date_sensor = 6  [n =     13]:	weight: 0.032	loss: 0.770	
  location_date_sensor = 7  [n =     11]:	weight: 0.110	loss: 0.712	
  location_date_sensor = 8  [n =     12]:	weight: 0.041	loss: 0.642	
  location_date_sensor = 9  [n =      9]:	weight: 0.097	loss: 0.679	
  location_date_sensor = 10  [n =     15]:	weight: 0.087	loss: 0.812	
  location_date_sensor = 11  [n =      6]:	weight: 0.060	loss: 0.625	
  location_date_sensor = 12  [n =      9]:	weight: 0.033	loss: 0.706	
  location_date_sensor = 13  [n =     15]:	weight: 0.103	loss: 0.795	
  location_date_sensor = 14  [n =     13]:	weight: 0.077	loss: 0.832	
  location_date_sensor = 15  [n =     10]:	weight: 0.088	loss: 0.811	
  location_date_sensor = 16  [n =     10]:	weight: 0.052	loss: 0.681	
  location_date_sensor = 17  [n =     10]:	weight: 0.021	loss: 0.580	
 98% 899/915 [22:53<00:24,  1.52s/it]objective: 0.148
loss_avg: 0.682
  location_date_sensor = 0  [n =      8]:	weight: 0.056	loss: 0.623	
  location_date_sensor = 1  [n =     11]:	weight: 0.011	loss: 0.477	
  location_date_sensor = 2  [n =     12]:	weight: 0.029	loss: 0.603	
  location_date_sensor = 3  [n =     16]:	weight: 0.029	loss: 0.555	
  location_date_sensor = 4  [n =     12]:	weight: 0.051	loss: 0.599	
  location_date_sensor = 5  [n =      9]:	weight: 0.021	loss: 0.685	
  location_date_sensor = 6  [n =     11]:	weight: 0.032	loss: 0.726	
  location_date_sensor = 7  [n =      5]:	weight: 0.107	loss: 0.631	
  location_date_sensor = 8  [n =     10]:	weight: 0.041	loss: 0.687	
  location_date_sensor = 9  [n =     11]:	weight: 0.097	loss: 0.700	
  location_date_sensor = 10  [n =     14]:	weight: 0.090	loss: 0.877	
  location_date_sensor = 11  [n =     15]:	weight: 0.060	loss: 0.613	
  location_date_sensor = 12  [n =      9]:	weight: 0.032	loss: 0.756	
  location_date_sensor = 13  [n =      9]:	weight: 0.103	loss: 0.782	
  location_date_sensor = 14  [n =     10]:	weight: 0.080	loss: 0.875	
  location_date_sensor = 15  [n =     13]:	weight: 0.091	loss: 0.829	
  location_date_sensor = 16  [n =     14]:	weight: 0.052	loss: 0.702	
  location_date_sensor = 17  [n =     11]:	weight: 0.021	loss: 0.582	
100% 915/915 [23:16<00:00,  1.53s/it]
objective: 0.164
loss_avg: 0.704
  location_date_sensor = 0  [n =      1]:	weight: 0.054	loss: 0.649	
  location_date_sensor = 1  [n =      3]:	weight: 0.010	loss: 0.561	
  location_date_sensor = 2  [n =      1]:	weight: 0.029	loss: 0.662	
  location_date_sensor = 3  [n =      4]:	weight: 0.029	loss: 0.598	
  location_date_sensor = 4  [n =      2]:	weight: 0.051	loss: 0.646	
  location_date_sensor = 5  [n =      3]:	weight: 0.021	loss: 0.672	
  location_date_sensor = 6  [n =      4]:	weight: 0.032	loss: 0.791	
  location_date_sensor = 7  [n =      8]:	weight: 0.107	loss: 0.645	
  location_date_sensor = 8  [n =      5]:	weight: 0.041	loss: 0.647	
  location_date_sensor = 9  [n =      5]:	weight: 0.097	loss: 0.697	
  location_date_sensor = 10  [n =      2]:	weight: 0.093	loss: 1.099	
  location_date_sensor = 11  [n =      2]:	weight: 0.059	loss: 0.755	
  location_date_sensor = 12  [n =      4]:	weight: 0.032	loss: 0.692	
  location_date_sensor = 13  [n =      2]:	weight: 0.103	loss: 0.696	
  location_date_sensor = 14  [n =      5]:	weight: 0.079	loss: 0.883	
  location_date_sensor = 15  [n =      1]:	weight: 0.091	loss: 0.797	
  location_date_sensor = 16  [n =      3]:	weight: 0.053	loss: 0.685	
  location_date_sensor = 17  [n =      2]:	weight: 0.021	loss: 0.635	
Epoch eval:
Average detection_acc: 0.753
  location_date_sensor = 0  [n =    186]:	detection_acc = 0.747
  location_date_sensor = 1  [n =    212]:	detection_acc = 0.864
  location_date_sensor = 2  [n =    218]:	detection_acc = 0.859
  location_date_sensor = 3  [n =    217]:	detection_acc = 0.804
  location_date_sensor = 4  [n =    200]:	detection_acc = 0.784
  location_date_sensor = 5  [n =    194]:	detection_acc = 0.756
  location_date_sensor = 6  [n =    186]:	detection_acc = 0.752
  location_date_sensor = 7  [n =    203]:	detection_acc = 0.726
  location_date_sensor = 8  [n =    203]:	detection_acc = 0.743
  location_date_sensor = 9  [n =    194]:	detection_acc = 0.623
  location_date_sensor = 10  [n =    215]:	detection_acc = 0.717
  location_date_sensor = 11  [n =    216]:	detection_acc = 0.688
  location_date_sensor = 12  [n =    189]:	detection_acc = 0.745
  location_date_sensor = 13  [n =    194]:	detection_acc = 0.748
  location_date_sensor = 14  [n =    195]:	detection_acc = 0.736
  location_date_sensor = 15  [n =    203]:	detection_acc = 0.775
  location_date_sensor = 16  [n =    214]:	detection_acc = 0.668
  location_date_sensor = 17  [n =    218]:	detection_acc = 0.814
Worst-group detection_acc: 0.623

Validation:
100% 348/348 [03:19<00:00,  1.74it/s]
objective: 0.000
loss_avg: 1.000
  location_date_sensor = 18  [n =     12]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 19  [n =     49]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 20  [n =    254]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 21  [n =    216]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 22  [n =     89]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 23  [n =     11]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 24  [n =      4]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 25  [n =      7]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 26  [n =     14]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 27  [n =     17]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 28  [n =     14]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 29  [n =      8]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 30  [n =     43]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 31  [n =     55]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 32  [n =     51]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 33  [n =     50]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 34  [n =     28]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 35  [n =     75]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 36  [n =     56]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 37  [n =     34]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 38  [n =     55]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 39  [n =     13]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 40  [n =     19]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 41  [n =     19]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 42  [n =     57]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 43  [n =     39]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 44  [n =     33]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 45  [n =     39]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 46  [n =     30]:	weight: 0.000	loss: 1.000	
Epoch eval:
Average detection_acc: 0.014
  location_date_sensor = 18  [n =     12]:	detection_acc = 0.000
  location_date_sensor = 19  [n =     49]:	detection_acc = 0.020
  location_date_sensor = 20  [n =    254]:	detection_acc = 0.000
  location_date_sensor = 21  [n =    216]:	detection_acc = 0.000
  location_date_sensor = 22  [n =     89]:	detection_acc = 0.000
  location_date_sensor = 23  [n =     11]:	detection_acc = 0.000
  location_date_sensor = 24  [n =      4]:	detection_acc = 0.500
  location_date_sensor = 25  [n =      7]:	detection_acc = 0.143
  location_date_sensor = 26  [n =     14]:	detection_acc = 0.071
  location_date_sensor = 27  [n =     17]:	detection_acc = 0.059
  location_date_sensor = 28  [n =     14]:	detection_acc = 0.000
  location_date_sensor = 29  [n =      8]:	detection_acc = 0.000
  location_date_sensor = 30  [n =     43]:	detection_acc = 0.000
  location_date_sensor = 31  [n =     55]:	detection_acc = 0.000
  location_date_sensor = 32  [n =     51]:	detection_acc = 0.000
  location_date_sensor = 33  [n =     50]:	detection_acc = 0.000
  location_date_sensor = 34  [n =     28]:	detection_acc = 0.000
  location_date_sensor = 35  [n =     75]:	detection_acc = 0.040
  location_date_sensor = 36  [n =     56]:	detection_acc = 0.054
  location_date_sensor = 37  [n =     34]:	detection_acc = 0.029
  location_date_sensor = 38  [n =     55]:	detection_acc = 0.000
  location_date_sensor = 39  [n =     13]:	detection_acc = 0.000
  location_date_sensor = 40  [n =     19]:	detection_acc = 0.000
  location_date_sensor = 41  [n =     19]:	detection_acc = 0.000
  location_date_sensor = 42  [n =     57]:	detection_acc = 0.000
  location_date_sensor = 43  [n =     39]:	detection_acc = 0.000
  location_date_sensor = 44  [n =     33]:	detection_acc = 0.030
  location_date_sensor = 45  [n =     39]:	detection_acc = 0.026
  location_date_sensor = 46  [n =     30]:	detection_acc = 0.133
Worst-group detection_acc: 0.000
Validation detection_acc_avg: 0.014
Epoch 2 has the best validation performance so far.
100% 365/365 [03:29<00:00,  1.74it/s]


Epoch [3]:

Train:
  5% 49/915 [01:15<22:04,  1.53s/it]objective: 0.146
loss_avg: 0.694
  location_date_sensor = 0  [n =     14]:	weight: 0.054	loss: 0.587	
  location_date_sensor = 1  [n =      5]:	weight: 0.010	loss: 0.587	
  location_date_sensor = 2  [n =     13]:	weight: 0.028	loss: 0.587	
  location_date_sensor = 3  [n =      7]:	weight: 0.029	loss: 0.577	
  location_date_sensor = 4  [n =     14]:	weight: 0.051	loss: 0.679	
  location_date_sensor = 5  [n =     11]:	weight: 0.021	loss: 0.887	
  location_date_sensor = 6  [n =     17]:	weight: 0.032	loss: 0.692	
  location_date_sensor = 7  [n =      7]:	weight: 0.106	loss: 0.617	
  location_date_sensor = 8  [n =     11]:	weight: 0.041	loss: 0.679	
  location_date_sensor = 9  [n =      9]:	weight: 0.096	loss: 0.681	
  location_date_sensor = 10  [n =     14]:	weight: 0.093	loss: 0.814	
  location_date_sensor = 11  [n =      4]:	weight: 0.058	loss: 0.596	
  location_date_sensor = 12  [n =     12]:	weight: 0.032	loss: 0.708	
  location_date_sensor = 13  [n =     14]:	weight: 0.104	loss: 0.718	
  location_date_sensor = 14  [n =     16]:	weight: 0.082	loss: 0.813	
  location_date_sensor = 15  [n =      7]:	weight: 0.091	loss: 0.910	
  location_date_sensor = 16  [n =      9]:	weight: 0.053	loss: 0.605	
  location_date_sensor = 17  [n =     16]:	weight: 0.021	loss: 0.620	
 11% 99/915 [02:31<20:52,  1.54s/it]objective: 0.147
loss_avg: 0.704
  location_date_sensor = 0  [n =      8]:	weight: 0.054	loss: 0.628	
  location_date_sensor = 1  [n =     11]:	weight: 0.010	loss: 0.537	
  location_date_sensor = 2  [n =     11]:	weight: 0.028	loss: 0.649	
  location_date_sensor = 3  [n =     20]:	weight: 0.029	loss: 0.631	
  location_date_sensor = 4  [n =     13]:	weight: 0.052	loss: 0.631	
  location_date_sensor = 5  [n =     11]:	weight: 0.021	loss: 0.822	
  location_date_sensor = 6  [n =     11]:	weight: 0.033	loss: 0.733	
  location_date_sensor = 7  [n =     11]:	weight: 0.105	loss: 0.728	
  location_date_sensor = 8  [n =      8]:	weight: 0.041	loss: 0.635	
  location_date_sensor = 9  [n =     12]:	weight: 0.095	loss: 0.727	
  location_date_sensor = 10  [n =     10]:	weight: 0.095	loss: 0.784	
  location_date_sensor = 11  [n =      9]:	weight: 0.056	loss: 0.612	
  location_date_sensor = 12  [n =     18]:	weight: 0.033	loss: 0.700	
  location_date_sensor = 13  [n =      8]:	weight: 0.103	loss: 0.707	
  location_date_sensor = 14  [n =      9]:	weight: 0.085	loss: 0.949	
  location_date_sensor = 15  [n =     12]:	weight: 0.091	loss: 0.900	
  location_date_sensor = 16  [n =      9]:	weight: 0.051	loss: 0.692	
  location_date_sensor = 17  [n =      9]:	weight: 0.021	loss: 0.638	
 16% 149/915 [03:48<19:29,  1.53s/it]objective: 0.147
loss_avg: 0.698
  location_date_sensor = 0  [n =     16]:	weight: 0.053	loss: 0.621	
  location_date_sensor = 1  [n =     13]:	weight: 0.010	loss: 0.542	
  location_date_sensor = 2  [n =     11]:	weight: 0.028	loss: 0.620	
  location_date_sensor = 3  [n =     13]:	weight: 0.029	loss: 0.575	
  location_date_sensor = 4  [n =      6]:	weight: 0.051	loss: 0.637	
  location_date_sensor = 5  [n =      9]:	weight: 0.021	loss: 0.678	
  location_date_sensor = 6  [n =     12]:	weight: 0.033	loss: 0.779	
  location_date_sensor = 7  [n =      6]:	weight: 0.105	loss: 0.693	
  location_date_sensor = 8  [n =     11]:	weight: 0.040	loss: 0.669	
  location_date_sensor = 9  [n =      9]:	weight: 0.094	loss: 0.686	
  location_date_sensor = 10  [n =     16]:	weight: 0.096	loss: 0.876	
  location_date_sensor = 11  [n =     17]:	weight: 0.056	loss: 0.612	
  location_date_sensor = 12  [n =     12]:	weight: 0.034	loss: 0.721	
  location_date_sensor = 13  [n =     13]:	weight: 0.105	loss: 0.753	
  location_date_sensor = 14  [n =     12]:	weight: 0.086	loss: 1.014	
  location_date_sensor = 15  [n =      8]:	weight: 0.091	loss: 0.793	
  location_date_sensor = 16  [n =      6]:	weight: 0.051	loss: 0.630	
  location_date_sensor = 17  [n =     10]:	weight: 0.021	loss: 0.627	
 22% 199/915 [05:04<18:10,  1.52s/it]objective: 0.148
loss_avg: 0.712
  location_date_sensor = 0  [n =      5]:	weight: 0.053	loss: 0.708	
  location_date_sensor = 1  [n =     14]:	weight: 0.010	loss: 0.504	
  location_date_sensor = 2  [n =     10]:	weight: 0.028	loss: 0.606	
  location_date_sensor = 3  [n =     12]:	weight: 0.029	loss: 0.628	
  location_date_sensor = 4  [n =     19]:	weight: 0.050	loss: 0.623	
  location_date_sensor = 5  [n =      8]:	weight: 0.020	loss: 0.650	
  location_date_sensor = 6  [n =     13]:	weight: 0.033	loss: 0.730	
  location_date_sensor = 7  [n =     10]:	weight: 0.103	loss: 0.779	
  location_date_sensor = 8  [n =      4]:	weight: 0.039	loss: 0.662	
  location_date_sensor = 9  [n =      9]:	weight: 0.093	loss: 0.696	
  location_date_sensor = 10  [n =     14]:	weight: 0.099	loss: 0.920	
  location_date_sensor = 11  [n =      6]:	weight: 0.055	loss: 0.680	
  location_date_sensor = 12  [n =     14]:	weight: 0.034	loss: 0.742	
  location_date_sensor = 13  [n =     10]:	weight: 0.106	loss: 0.816	
  location_date_sensor = 14  [n =     10]:	weight: 0.087	loss: 0.931	
  location_date_sensor = 15  [n =     10]:	weight: 0.092	loss: 0.888	
  location_date_sensor = 16  [n =     18]:	weight: 0.051	loss: 0.705	
  location_date_sensor = 17  [n =     14]:	weight: 0.021	loss: 0.609	
 27% 249/915 [06:21<17:00,  1.53s/it]objective: 0.158
loss_avg: 0.683
  location_date_sensor = 0  [n =     15]:	weight: 0.051	loss: 0.611	
  location_date_sensor = 1  [n =      8]:	weight: 0.009	loss: 0.520	
  location_date_sensor = 2  [n =     16]:	weight: 0.028	loss: 0.568	
  location_date_sensor = 3  [n =     11]:	weight: 0.029	loss: 0.569	
  location_date_sensor = 4  [n =      6]:	weight: 0.051	loss: 0.623	
  location_date_sensor = 5  [n =      6]:	weight: 0.020	loss: 0.731	
  location_date_sensor = 6  [n =     16]:	weight: 0.034	loss: 0.722	
  location_date_sensor = 7  [n =     13]:	weight: 0.103	loss: 0.635	
  location_date_sensor = 8  [n =      9]:	weight: 0.038	loss: 0.674	
  location_date_sensor = 9  [n =     15]:	weight: 0.094	loss: 0.701	
  location_date_sensor = 10  [n =     13]:	weight: 0.101	loss: 0.844	
  location_date_sensor = 11  [n =      6]:	weight: 0.053	loss: 0.626	
  location_date_sensor = 12  [n =     10]:	weight: 0.034	loss: 0.703	
  location_date_sensor = 13  [n =     14]:	weight: 0.107	loss: 0.739	
  location_date_sensor = 14  [n =     13]:	weight: 0.089	loss: 0.868	
  location_date_sensor = 15  [n =      9]:	weight: 0.091	loss: 0.799	
  location_date_sensor = 16  [n =      9]:	weight: 0.051	loss: 0.652	
  location_date_sensor = 17  [n =     11]:	weight: 0.020	loss: 0.627	
 33% 299/915 [07:37<15:39,  1.53s/it]objective: 0.148
loss_avg: 0.677
  location_date_sensor = 0  [n =     19]:	weight: 0.052	loss: 0.628	
  location_date_sensor = 1  [n =      7]:	weight: 0.009	loss: 0.504	
  location_date_sensor = 2  [n =     15]:	weight: 0.028	loss: 0.575	
  location_date_sensor = 3  [n =     10]:	weight: 0.028	loss: 0.591	
  location_date_sensor = 4  [n =      9]:	weight: 0.049	loss: 0.613	
  location_date_sensor = 5  [n =      9]:	weight: 0.019	loss: 0.561	
  location_date_sensor = 6  [n =     23]:	weight: 0.035	loss: 0.727	
  location_date_sensor = 7  [n =     12]:	weight: 0.103	loss: 0.661	
  location_date_sensor = 8  [n =      7]:	weight: 0.037	loss: 0.637	
  location_date_sensor = 9  [n =     10]:	weight: 0.095	loss: 0.658	
  location_date_sensor = 10  [n =      6]:	weight: 0.101	loss: 0.878	
  location_date_sensor = 11  [n =      4]:	weight: 0.051	loss: 0.588	
  location_date_sensor = 12  [n =      8]:	weight: 0.034	loss: 0.746	
  location_date_sensor = 13  [n =     10]:	weight: 0.107	loss: 0.744	
  location_date_sensor = 14  [n =     14]:	weight: 0.094	loss: 0.907	
  location_date_sensor = 15  [n =     17]:	weight: 0.092	loss: 0.811	
  location_date_sensor = 16  [n =     12]:	weight: 0.050	loss: 0.618	
  location_date_sensor = 17  [n =      8]:	weight: 0.020	loss: 0.535	
 38% 349/915 [08:54<14:24,  1.53s/it]objective: 0.150
loss_avg: 0.677
  location_date_sensor = 0  [n =      6]:	weight: 0.053	loss: 0.786	
  location_date_sensor = 1  [n =      9]:	weight: 0.009	loss: 0.542	
  location_date_sensor = 2  [n =     10]:	weight: 0.027	loss: 0.641	
  location_date_sensor = 3  [n =      3]:	weight: 0.027	loss: 0.652	
  location_date_sensor = 4  [n =     11]:	weight: 0.048	loss: 0.622	
  location_date_sensor = 5  [n =      7]:	weight: 0.018	loss: 0.633	
  location_date_sensor = 6  [n =     13]:	weight: 0.036	loss: 0.672	
  location_date_sensor = 7  [n =     13]:	weight: 0.103	loss: 0.639	
  location_date_sensor = 8  [n =     16]:	weight: 0.036	loss: 0.597	
  location_date_sensor = 9  [n =     16]:	weight: 0.094	loss: 0.704	
  location_date_sensor = 10  [n =     11]:	weight: 0.101	loss: 0.876	
  location_date_sensor = 11  [n =     15]:	weight: 0.050	loss: 0.592	
  location_date_sensor = 12  [n =     15]:	weight: 0.033	loss: 0.698	
  location_date_sensor = 13  [n =     14]:	weight: 0.109	loss: 0.723	
  location_date_sensor = 14  [n =     11]:	weight: 0.096	loss: 0.786	
  location_date_sensor = 15  [n =      8]:	weight: 0.093	loss: 0.870	
  location_date_sensor = 16  [n =     10]:	weight: 0.049	loss: 0.624	
  location_date_sensor = 17  [n =     12]:	weight: 0.020	loss: 0.617	
 44% 399/915 [10:10<13:06,  1.52s/it]objective: 0.148
loss_avg: 0.662
  location_date_sensor = 0  [n =     10]:	weight: 0.051	loss: 0.559	
  location_date_sensor = 1  [n =      7]:	weight: 0.008	loss: 0.481	
  location_date_sensor = 2  [n =     15]:	weight: 0.027	loss: 0.598	
  location_date_sensor = 3  [n =     12]:	weight: 0.027	loss: 0.606	
  location_date_sensor = 4  [n =     11]:	weight: 0.048	loss: 0.605	
  location_date_sensor = 5  [n =     10]:	weight: 0.018	loss: 0.565	
  location_date_sensor = 6  [n =      9]:	weight: 0.036	loss: 0.619	
  location_date_sensor = 7  [n =     14]:	weight: 0.102	loss: 0.623	
  location_date_sensor = 8  [n =      5]:	weight: 0.036	loss: 0.668	
  location_date_sensor = 9  [n =     14]:	weight: 0.095	loss: 0.714	
  location_date_sensor = 10  [n =     12]:	weight: 0.102	loss: 0.844	
  location_date_sensor = 11  [n =     10]:	weight: 0.048	loss: 0.624	
  location_date_sensor = 12  [n =     13]:	weight: 0.034	loss: 0.716	
  location_date_sensor = 13  [n =     12]:	weight: 0.111	loss: 0.645	
  location_date_sensor = 14  [n =      6]:	weight: 0.094	loss: 0.874	
  location_date_sensor = 15  [n =     18]:	weight: 0.097	loss: 0.891	
  location_date_sensor = 16  [n =      9]:	weight: 0.049	loss: 0.574	
  location_date_sensor = 17  [n =     13]:	weight: 0.019	loss: 0.563	
 49% 449/915 [11:27<11:51,  1.53s/it]objective: 0.137
loss_avg: 0.656
  location_date_sensor = 0  [n =      5]:	weight: 0.049	loss: 0.581	
  location_date_sensor = 1  [n =     13]:	weight: 0.008	loss: 0.509	
  location_date_sensor = 2  [n =     11]:	weight: 0.027	loss: 0.568	
  location_date_sensor = 3  [n =     20]:	weight: 0.027	loss: 0.514	
  location_date_sensor = 4  [n =      4]:	weight: 0.046	loss: 0.648	
  location_date_sensor = 5  [n =     15]:	weight: 0.018	loss: 0.539	
  location_date_sensor = 6  [n =     17]:	weight: 0.035	loss: 0.642	
  location_date_sensor = 7  [n =      7]:	weight: 0.101	loss: 0.589	
  location_date_sensor = 8  [n =     11]:	weight: 0.036	loss: 0.638	
  location_date_sensor = 9  [n =     14]:	weight: 0.096	loss: 0.709	
  location_date_sensor = 10  [n =     15]:	weight: 0.105	loss: 0.820	
  location_date_sensor = 11  [n =      7]:	weight: 0.048	loss: 0.647	
  location_date_sensor = 12  [n =     11]:	weight: 0.034	loss: 0.761	
  location_date_sensor = 13  [n =     12]:	weight: 0.112	loss: 0.775	
  location_date_sensor = 14  [n =      6]:	weight: 0.093	loss: 0.761	
  location_date_sensor = 15  [n =     11]:	weight: 0.101	loss: 0.901	
  location_date_sensor = 16  [n =      7]:	weight: 0.047	loss: 0.804	
  location_date_sensor = 17  [n =     14]:	weight: 0.019	loss: 0.568	
 55% 499/915 [12:43<10:37,  1.53s/it]objective: 0.152
loss_avg: 0.663
  location_date_sensor = 0  [n =     19]:	weight: 0.049	loss: 0.581	
  location_date_sensor = 1  [n =     13]:	weight: 0.008	loss: 0.499	
  location_date_sensor = 2  [n =      7]:	weight: 0.027	loss: 0.523	
  location_date_sensor = 3  [n =     12]:	weight: 0.027	loss: 0.622	
  location_date_sensor = 4  [n =      7]:	weight: 0.044	loss: 0.620	
  location_date_sensor = 5  [n =     10]:	weight: 0.017	loss: 0.502	
  location_date_sensor = 6  [n =      9]:	weight: 0.036	loss: 0.721	
  location_date_sensor = 7  [n =     11]:	weight: 0.099	loss: 0.662	
  location_date_sensor = 8  [n =      6]:	weight: 0.035	loss: 0.667	
  location_date_sensor = 9  [n =     12]:	weight: 0.096	loss: 0.653	
  location_date_sensor = 10  [n =      9]:	weight: 0.109	loss: 0.813	
  location_date_sensor = 11  [n =     12]:	weight: 0.047	loss: 0.582	
  location_date_sensor = 12  [n =     14]:	weight: 0.034	loss: 0.695	
  location_date_sensor = 13  [n =     11]:	weight: 0.111	loss: 0.762	
  location_date_sensor = 14  [n =      7]:	weight: 0.092	loss: 0.928	
  location_date_sensor = 15  [n =     18]:	weight: 0.105	loss: 0.836	
  location_date_sensor = 16  [n =     12]:	weight: 0.048	loss: 0.696	
  location_date_sensor = 17  [n =     11]:	weight: 0.019	loss: 0.611	
 60% 549/915 [14:00<09:18,  1.53s/it]objective: 0.135
loss_avg: 0.659
  location_date_sensor = 0  [n =     11]:	weight: 0.049	loss: 0.542	
  location_date_sensor = 1  [n =     13]:	weight: 0.008	loss: 0.544	
  location_date_sensor = 2  [n =     12]:	weight: 0.026	loss: 0.591	
  location_date_sensor = 3  [n =     14]:	weight: 0.027	loss: 0.581	
  location_date_sensor = 4  [n =     13]:	weight: 0.045	loss: 0.622	
  location_date_sensor = 5  [n =      9]:	weight: 0.018	loss: 0.713	
  location_date_sensor = 6  [n =     13]:	weight: 0.036	loss: 0.673	
  location_date_sensor = 7  [n =     12]:	weight: 0.098	loss: 0.612	
  location_date_sensor = 8  [n =      7]:	weight: 0.034	loss: 0.659	
  location_date_sensor = 9  [n =     12]:	weight: 0.096	loss: 0.640	
  location_date_sensor = 10  [n =      7]:	weight: 0.108	loss: 0.768	
  location_date_sensor = 11  [n =      9]:	weight: 0.046	loss: 0.567	
  location_date_sensor = 12  [n =     10]:	weight: 0.035	loss: 0.680	
  location_date_sensor = 13  [n =      6]:	weight: 0.108	loss: 0.886	
  location_date_sensor = 14  [n =      6]:	weight: 0.090	loss: 0.887	
  location_date_sensor = 15  [n =     14]:	weight: 0.113	loss: 0.816	
  location_date_sensor = 16  [n =     18]:	weight: 0.048	loss: 0.717	
  location_date_sensor = 17  [n =     14]:	weight: 0.019	loss: 0.601	
 65% 599/915 [15:16<08:04,  1.53s/it]objective: 0.152
loss_avg: 0.661
  location_date_sensor = 0  [n =      6]:	weight: 0.049	loss: 0.572	
  location_date_sensor = 1  [n =     17]:	weight: 0.008	loss: 0.498	
  location_date_sensor = 2  [n =     10]:	weight: 0.026	loss: 0.641	
  location_date_sensor = 3  [n =     10]:	weight: 0.027	loss: 0.553	
  location_date_sensor = 4  [n =     14]:	weight: 0.044	loss: 0.610	
  location_date_sensor = 5  [n =     11]:	weight: 0.017	loss: 0.603	
  location_date_sensor = 6  [n =     10]:	weight: 0.036	loss: 0.653	
  location_date_sensor = 7  [n =     10]:	weight: 0.096	loss: 0.658	
  location_date_sensor = 8  [n =      8]:	weight: 0.033	loss: 0.660	
  location_date_sensor = 9  [n =      7]:	weight: 0.095	loss: 0.595	
  location_date_sensor = 10  [n =     10]:	weight: 0.107	loss: 0.852	
  location_date_sensor = 11  [n =      8]:	weight: 0.045	loss: 0.572	
  location_date_sensor = 12  [n =      9]:	weight: 0.034	loss: 0.634	
  location_date_sensor = 13  [n =     14]:	weight: 0.109	loss: 0.666	
  location_date_sensor = 14  [n =     17]:	weight: 0.091	loss: 0.822	
  location_date_sensor = 15  [n =     18]:	weight: 0.118	loss: 0.828	
  location_date_sensor = 16  [n =     11]:	weight: 0.049	loss: 0.684	
  location_date_sensor = 17  [n =     10]:	weight: 0.019	loss: 0.609	
 71% 649/915 [16:33<06:44,  1.52s/it]objective: 0.142
loss_avg: 0.673
  location_date_sensor = 0  [n =     14]:	weight: 0.048	loss: 0.650	
  location_date_sensor = 1  [n =      7]:	weight: 0.008	loss: 0.489	
  location_date_sensor = 2  [n =     16]:	weight: 0.026	loss: 0.627	
  location_date_sensor = 3  [n =      5]:	weight: 0.026	loss: 0.526	
  location_date_sensor = 4  [n =      5]:	weight: 0.043	loss: 0.594	
  location_date_sensor = 5  [n =     12]:	weight: 0.017	loss: 0.715	
  location_date_sensor = 6  [n =     14]:	weight: 0.036	loss: 0.691	
  location_date_sensor = 7  [n =     13]:	weight: 0.096	loss: 0.591	
  location_date_sensor = 8  [n =     13]:	weight: 0.033	loss: 0.623	
  location_date_sensor = 9  [n =      9]:	weight: 0.093	loss: 0.663	
  location_date_sensor = 10  [n =     11]:	weight: 0.107	loss: 0.838	
  location_date_sensor = 11  [n =     10]:	weight: 0.044	loss: 0.603	
  location_date_sensor = 12  [n =     13]:	weight: 0.035	loss: 0.762	
  location_date_sensor = 13  [n =     16]:	weight: 0.110	loss: 0.655	
  location_date_sensor = 14  [n =      7]:	weight: 0.092	loss: 0.891	
  location_date_sensor = 15  [n =     15]:	weight: 0.123	loss: 0.812	
  location_date_sensor = 16  [n =      9]:	weight: 0.048	loss: 0.602	
  location_date_sensor = 17  [n =     11]:	weight: 0.019	loss: 0.636	
 76% 699/915 [17:49<05:30,  1.53s/it]objective: 0.151
loss_avg: 0.664
  location_date_sensor = 0  [n =     10]:	weight: 0.048	loss: 0.589	
  location_date_sensor = 1  [n =     14]:	weight: 0.007	loss: 0.516	
  location_date_sensor = 2  [n =      8]:	weight: 0.026	loss: 0.575	
  location_date_sensor = 3  [n =     15]:	weight: 0.025	loss: 0.502	
  location_date_sensor = 4  [n =     11]:	weight: 0.042	loss: 0.594	
  location_date_sensor = 5  [n =     10]:	weight: 0.017	loss: 0.654	
  location_date_sensor = 6  [n =      5]:	weight: 0.037	loss: 0.710	
  location_date_sensor = 7  [n =      8]:	weight: 0.094	loss: 0.629	
  location_date_sensor = 8  [n =     13]:	weight: 0.033	loss: 0.653	
  location_date_sensor = 9  [n =      4]:	weight: 0.088	loss: 0.674	
  location_date_sensor = 10  [n =     15]:	weight: 0.110	loss: 0.848	
  location_date_sensor = 11  [n =     10]:	weight: 0.043	loss: 0.554	
  location_date_sensor = 12  [n =     11]:	weight: 0.035	loss: 0.701	
  location_date_sensor = 13  [n =      9]:	weight: 0.109	loss: 0.695	
  location_date_sensor = 14  [n =     17]:	weight: 0.093	loss: 0.833	
  location_date_sensor = 15  [n =     14]:	weight: 0.128	loss: 0.866	
  location_date_sensor = 16  [n =     14]:	weight: 0.048	loss: 0.634	
  location_date_sensor = 17  [n =     12]:	weight: 0.019	loss: 0.602	
 82% 749/915 [19:06<04:14,  1.53s/it]objective: 0.131
loss_avg: 0.635
  location_date_sensor = 0  [n =     15]:	weight: 0.048	loss: 0.589	
  location_date_sensor = 1  [n =     15]:	weight: 0.007	loss: 0.467	
  location_date_sensor = 2  [n =     11]:	weight: 0.025	loss: 0.570	
  location_date_sensor = 3  [n =     17]:	weight: 0.025	loss: 0.543	
  location_date_sensor = 4  [n =      3]:	weight: 0.040	loss: 0.556	
  location_date_sensor = 5  [n =     13]:	weight: 0.017	loss: 0.667	
  location_date_sensor = 6  [n =     10]:	weight: 0.036	loss: 0.683	
  location_date_sensor = 7  [n =      9]:	weight: 0.092	loss: 0.559	
  location_date_sensor = 8  [n =     10]:	weight: 0.033	loss: 0.616	
  location_date_sensor = 9  [n =     13]:	weight: 0.088	loss: 0.665	
  location_date_sensor = 10  [n =     10]:	weight: 0.112	loss: 0.768	
  location_date_sensor = 11  [n =     12]:	weight: 0.043	loss: 0.561	
  location_date_sensor = 12  [n =     17]:	weight: 0.035	loss: 0.705	
  location_date_sensor = 13  [n =      8]:	weight: 0.107	loss: 0.645	
  location_date_sensor = 14  [n =      8]:	weight: 0.095	loss: 0.807	
  location_date_sensor = 15  [n =     14]:	weight: 0.131	loss: 0.816	
  location_date_sensor = 16  [n =      8]:	weight: 0.048	loss: 0.662	
  location_date_sensor = 17  [n =      7]:	weight: 0.019	loss: 0.562	
 87% 799/915 [20:22<02:57,  1.53s/it]objective: 0.160
loss_avg: 0.660
  location_date_sensor = 0  [n =     10]:	weight: 0.047	loss: 0.524	
  location_date_sensor = 1  [n =     13]:	weight: 0.007	loss: 0.589	
  location_date_sensor = 2  [n =     11]:	weight: 0.025	loss: 0.552	
  location_date_sensor = 3  [n =      6]:	weight: 0.026	loss: 0.552	
  location_date_sensor = 4  [n =     10]:	weight: 0.039	loss: 0.652	
  location_date_sensor = 5  [n =      9]:	weight: 0.017	loss: 0.603	
  location_date_sensor = 6  [n =      5]:	weight: 0.035	loss: 0.750	
  location_date_sensor = 7  [n =     12]:	weight: 0.090	loss: 0.647	
  location_date_sensor = 8  [n =     10]:	weight: 0.033	loss: 0.619	
  location_date_sensor = 9  [n =      8]:	weight: 0.088	loss: 0.616	
  location_date_sensor = 10  [n =     11]:	weight: 0.112	loss: 0.824	
  location_date_sensor = 11  [n =     14]:	weight: 0.043	loss: 0.570	
  location_date_sensor = 12  [n =      7]:	weight: 0.035	loss: 0.820	
  location_date_sensor = 13  [n =     20]:	weight: 0.109	loss: 0.711	
  location_date_sensor = 14  [n =     10]:	weight: 0.097	loss: 0.857	
  location_date_sensor = 15  [n =     13]:	weight: 0.135	loss: 0.861	
  location_date_sensor = 16  [n =     10]:	weight: 0.047	loss: 0.607	
  location_date_sensor = 17  [n =     21]:	weight: 0.019	loss: 0.582	
 93% 849/915 [21:39<01:40,  1.53s/it]objective: 0.137
loss_avg: 0.634
  location_date_sensor = 0  [n =     12]:	weight: 0.047	loss: 0.537	
  location_date_sensor = 1  [n =     11]:	weight: 0.007	loss: 0.531	
  location_date_sensor = 2  [n =     13]:	weight: 0.025	loss: 0.577	
  location_date_sensor = 3  [n =     15]:	weight: 0.025	loss: 0.525	
  location_date_sensor = 4  [n =     10]:	weight: 0.038	loss: 0.572	
  location_date_sensor = 5  [n =     13]:	weight: 0.017	loss: 0.689	
  location_date_sensor = 6  [n =      7]:	weight: 0.034	loss: 0.670	
  location_date_sensor = 7  [n =     10]:	weight: 0.089	loss: 0.571	
  location_date_sensor = 8  [n =     15]:	weight: 0.032	loss: 0.615	
  location_date_sensor = 9  [n =     12]:	weight: 0.086	loss: 0.709	
  location_date_sensor = 10  [n =     11]:	weight: 0.113	loss: 0.748	
  location_date_sensor = 11  [n =     15]:	weight: 0.042	loss: 0.522	
  location_date_sensor = 12  [n =     11]:	weight: 0.036	loss: 0.696	
  location_date_sensor = 13  [n =      6]:	weight: 0.111	loss: 0.632	
  location_date_sensor = 14  [n =     11]:	weight: 0.098	loss: 0.798	
  location_date_sensor = 15  [n =     14]:	weight: 0.139	loss: 0.824	
  location_date_sensor = 16  [n =      5]:	weight: 0.045	loss: 0.686	
  location_date_sensor = 17  [n =      9]:	weight: 0.018	loss: 0.575	
 98% 899/915 [22:55<00:24,  1.53s/it]objective: 0.163
loss_avg: 0.675
  location_date_sensor = 0  [n =      8]:	weight: 0.045	loss: 0.546	
  location_date_sensor = 1  [n =     13]:	weight: 0.007	loss: 0.530	
  location_date_sensor = 2  [n =     13]:	weight: 0.025	loss: 0.593	
  location_date_sensor = 3  [n =      9]:	weight: 0.024	loss: 0.535	
  location_date_sensor = 4  [n =     10]:	weight: 0.038	loss: 0.618	
  location_date_sensor = 5  [n =     11]:	weight: 0.017	loss: 0.639	
  location_date_sensor = 6  [n =     15]:	weight: 0.033	loss: 0.710	
  location_date_sensor = 7  [n =     11]:	weight: 0.087	loss: 0.620	
  location_date_sensor = 8  [n =      9]:	weight: 0.032	loss: 0.643	
  location_date_sensor = 9  [n =      7]:	weight: 0.083	loss: 0.700	
  location_date_sensor = 10  [n =     16]:	weight: 0.116	loss: 0.790	
  location_date_sensor = 11  [n =     11]:	weight: 0.042	loss: 0.578	
  location_date_sensor = 12  [n =     14]:	weight: 0.036	loss: 0.664	
  location_date_sensor = 13  [n =     14]:	weight: 0.111	loss: 0.757	
  location_date_sensor = 14  [n =     14]:	weight: 0.099	loss: 0.820	
  location_date_sensor = 15  [n =     12]:	weight: 0.144	loss: 0.882	
  location_date_sensor = 16  [n =      6]:	weight: 0.043	loss: 0.679	
  location_date_sensor = 17  [n =      7]:	weight: 0.018	loss: 0.676	
100% 915/915 [23:19<00:00,  1.53s/it]
objective: 0.139
loss_avg: 0.667
  location_date_sensor = 0  [n =      3]:	weight: 0.045	loss: 0.672	
  location_date_sensor = 1  [n =      2]:	weight: 0.007	loss: 0.653	
  location_date_sensor = 2  [n =      6]:	weight: 0.024	loss: 0.551	
  location_date_sensor = 3  [n =      2]:	weight: 0.024	loss: 0.618	
  location_date_sensor = 4  [n =      1]:	weight: 0.037	loss: 0.758	
  location_date_sensor = 6  [n =      1]:	weight: 0.034	loss: 0.675	
  location_date_sensor = 7  [n =      3]:	weight: 0.088	loss: 0.658	
  location_date_sensor = 8  [n =      4]:	weight: 0.032	loss: 0.608	
  location_date_sensor = 9  [n =      4]:	weight: 0.083	loss: 0.681	
  location_date_sensor = 10  [n =      1]:	weight: 0.117	loss: 0.775	
  location_date_sensor = 11  [n =      3]:	weight: 0.042	loss: 0.551	
  location_date_sensor = 12  [n =      7]:	weight: 0.036	loss: 0.720	
  location_date_sensor = 13  [n =      3]:	weight: 0.111	loss: 0.686	
  location_date_sensor = 14  [n =      4]:	weight: 0.101	loss: 0.834	
  location_date_sensor = 15  [n =      3]:	weight: 0.144	loss: 0.877	
  location_date_sensor = 16  [n =      4]:	weight: 0.043	loss: 0.623	
  location_date_sensor = 17  [n =      6]:	weight: 0.018	loss: 0.597	
Epoch eval:
Average detection_acc: 0.782
  location_date_sensor = 0  [n =    206]:	detection_acc = 0.823
  location_date_sensor = 1  [n =    205]:	detection_acc = 0.874
  location_date_sensor = 2  [n =    219]:	detection_acc = 0.880
  location_date_sensor = 3  [n =    213]:	detection_acc = 0.827
  location_date_sensor = 4  [n =    177]:	detection_acc = 0.815
  location_date_sensor = 5  [n =    184]:	detection_acc = 0.792
  location_date_sensor = 6  [n =    220]:	detection_acc = 0.767
  location_date_sensor = 7  [n =    192]:	detection_acc = 0.773
  location_date_sensor = 8  [n =    177]:	detection_acc = 0.770
  location_date_sensor = 9  [n =    196]:	detection_acc = 0.641
  location_date_sensor = 10  [n =    212]:	detection_acc = 0.741
  location_date_sensor = 11  [n =    182]:	detection_acc = 0.733
  location_date_sensor = 12  [n =    226]:	detection_acc = 0.745
  location_date_sensor = 13  [n =    214]:	detection_acc = 0.808
  location_date_sensor = 14  [n =    198]:	detection_acc = 0.752
  location_date_sensor = 15  [n =    235]:	detection_acc = 0.772
  location_date_sensor = 16  [n =    186]:	detection_acc = 0.722
  location_date_sensor = 17  [n =    215]:	detection_acc = 0.820
Worst-group detection_acc: 0.641

Validation:
100% 348/348 [03:19<00:00,  1.74it/s]
objective: 0.000
loss_avg: 1.000
  location_date_sensor = 18  [n =     12]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 19  [n =     49]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 20  [n =    254]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 21  [n =    216]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 22  [n =     89]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 23  [n =     11]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 24  [n =      4]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 25  [n =      7]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 26  [n =     14]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 27  [n =     17]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 28  [n =     14]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 29  [n =      8]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 30  [n =     43]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 31  [n =     55]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 32  [n =     51]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 33  [n =     50]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 34  [n =     28]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 35  [n =     75]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 36  [n =     56]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 37  [n =     34]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 38  [n =     55]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 39  [n =     13]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 40  [n =     19]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 41  [n =     19]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 42  [n =     57]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 43  [n =     39]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 44  [n =     33]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 45  [n =     39]:	weight: 0.000	loss: 1.000	
  location_date_sensor = 46  [n =     30]:	weight: 0.000	loss: 1.000	
Epoch eval:
Average detection_acc: 0.016
  location_date_sensor = 18  [n =     12]:	detection_acc = 0.000
  location_date_sensor = 19  [n =     49]:	detection_acc = 0.020
  location_date_sensor = 20  [n =    254]:	detection_acc = 0.000
  location_date_sensor = 21  [n =    216]:	detection_acc = 0.000
  location_date_sensor = 22  [n =     89]:	detection_acc = 0.000
  location_date_sensor = 23  [n =     11]:	detection_acc = 0.091
  location_date_sensor = 24  [n =      4]:	detection_acc = 0.750
  location_date_sensor = 25  [n =      7]:	detection_acc = 0.286
  location_date_sensor = 26  [n =     14]:	detection_acc = 0.071
  location_date_sensor = 27  [n =     17]:	detection_acc = 0.059
  location_date_sensor = 28  [n =     14]:	detection_acc = 0.000
  location_date_sensor = 29  [n =      8]:	detection_acc = 0.000
  location_date_sensor = 30  [n =     43]:	detection_acc = 0.000
  location_date_sensor = 31  [n =     55]:	detection_acc = 0.000
  location_date_sensor = 32  [n =     51]:	detection_acc = 0.000
  location_date_sensor = 33  [n =     50]:	detection_acc = 0.000
  location_date_sensor = 34  [n =     28]:	detection_acc = 0.000
  location_date_sensor = 35  [n =     75]:	detection_acc = 0.040
  location_date_sensor = 36  [n =     56]:	detection_acc = 0.071
  location_date_sensor = 37  [n =     34]:	detection_acc = 0.000
  location_date_sensor = 38  [n =     55]:	detection_acc = 0.000
  location_date_sensor = 39  [n =     13]:	detection_acc = 0.000
  location_date_sensor = 40  [n =     19]:	detection_acc = 0.000
  location_date_sensor = 41  [n =     19]:	detection_acc = 0.000
  location_date_sensor = 42  [n =     57]:	detection_acc = 0.000
  location_date_sensor = 43  [n =     39]:	detection_acc = 0.000
  location_date_sensor = 44  [n =     33]:	detection_acc = 0.030
  location_date_sensor = 45  [n =     39]:	detection_acc = 0.026
  location_date_sensor = 46  [n =     30]:	detection_acc = 0.133
Worst-group detection_acc: 0.000
Validation detection_acc_avg: 0.016
Epoch 3 has the best validation performance so far.
100% 365/365 [03:29<00:00,  1.74it/s]


Epoch [4]:

Train:
  5% 49/915 [01:15<22:04,  1.53s/it]objective: 0.134
loss_avg: 0.634
  location_date_sensor = 0  [n =     12]:	weight: 0.045	loss: 0.524	
  location_date_sensor = 1  [n =     10]:	weight: 0.007	loss: 0.519	
  location_date_sensor = 2  [n =     16]:	weight: 0.025	loss: 0.598	
  location_date_sensor = 3  [n =      7]:	weight: 0.023	loss: 0.516	
  location_date_sensor = 4  [n =      9]:	weight: 0.036	loss: 0.557	
  location_date_sensor = 5  [n =      7]:	weight: 0.016	loss: 0.615	
  location_date_sensor = 6  [n =     11]:	weight: 0.034	loss: 0.752	
  location_date_sensor = 7  [n =     14]:	weight: 0.088	loss: 0.575	
  location_date_sensor = 8  [n =     15]:	weight: 0.032	loss: 0.618	
  location_date_sensor = 9  [n =     11]:	weight: 0.083	loss: 0.685	
  location_date_sensor = 10  [n =      9]:	weight: 0.116	loss: 0.749	
  location_date_sensor = 11  [n =     12]:	weight: 0.042	loss: 0.575	
  location_date_sensor = 12  [n =     10]:	weight: 0.036	loss: 0.732	
  location_date_sensor = 13  [n =     16]:	weight: 0.112	loss: 0.633	
  location_date_sensor = 14  [n =      7]:	weight: 0.100	loss: 0.877	
  location_date_sensor = 15  [n =      9]:	weight: 0.147	loss: 0.844	
  location_date_sensor = 16  [n =      8]:	weight: 0.043	loss: 0.635	
  location_date_sensor = 17  [n =     17]:	weight: 0.018	loss: 0.573	
 11% 99/915 [02:31<20:45,  1.53s/it]objective: 0.135
loss_avg: 0.640
  location_date_sensor = 0  [n =     16]:	weight: 0.045	loss: 0.666	
  location_date_sensor = 1  [n =     17]:	weight: 0.007	loss: 0.508	
  location_date_sensor = 2  [n =     13]:	weight: 0.025	loss: 0.535	
  location_date_sensor = 3  [n =      5]:	weight: 0.022	loss: 0.532	
  location_date_sensor = 4  [n =     10]:	weight: 0.036	loss: 0.556	
  location_date_sensor = 5  [n =     13]:	weight: 0.016	loss: 0.633	
  location_date_sensor = 6  [n =      3]:	weight: 0.034	loss: 0.673	
  location_date_sensor = 7  [n =      9]:	weight: 0.088	loss: 0.590	
  location_date_sensor = 8  [n =     16]:	weight: 0.032	loss: 0.588	
  location_date_sensor = 9  [n =     12]:	weight: 0.083	loss: 0.683	
  location_date_sensor = 10  [n =     15]:	weight: 0.118	loss: 0.886	
  location_date_sensor = 11  [n =      9]:	weight: 0.041	loss: 0.583	
  location_date_sensor = 12  [n =      9]:	weight: 0.036	loss: 0.655	
  location_date_sensor = 13  [n =      9]:	weight: 0.111	loss: 0.586	
  location_date_sensor = 14  [n =     12]:	weight: 0.101	loss: 0.776	
  location_date_sensor = 15  [n =     10]:	weight: 0.147	loss: 0.816	
  location_date_sensor = 16  [n =     13]:	weight: 0.043	loss: 0.628	
  location_date_sensor = 17  [n =      9]:	weight: 0.018	loss: 0.560	
 16% 149/915 [03:48<19:35,  1.53s/it]objective: 0.133
loss_avg: 0.643
  location_date_sensor = 0  [n =     16]:	weight: 0.045	loss: 0.554	
  location_date_sensor = 1  [n =     12]:	weight: 0.007	loss: 0.546	
  location_date_sensor = 2  [n =     13]:	weight: 0.025	loss: 0.566	
  location_date_sensor = 3  [n =     10]:	weight: 0.022	loss: 0.540	
  location_date_sensor = 4  [n =      9]:	weight: 0.035	loss: 0.619	
  location_date_sensor = 5  [n =     13]:	weight: 0.016	loss: 0.588	
  location_date_sensor = 6  [n =     14]:	weight: 0.032	loss: 0.687	
  location_date_sensor = 7  [n =      7]:	weight: 0.084	loss: 0.549	
  location_date_sensor = 8  [n =      9]:	weight: 0.033	loss: 0.599	
  location_date_sensor = 9  [n =      6]:	weight: 0.082	loss: 0.680	
  location_date_sensor = 10  [n =     10]:	weight: 0.121	loss: 0.809	
  location_date_sensor = 11  [n =     16]:	weight: 0.040	loss: 0.520	
  location_date_sensor = 12  [n =     14]:	weight: 0.036	loss: 0.716	
  location_date_sensor = 13  [n =      7]:	weight: 0.109	loss: 0.779	
  location_date_sensor = 14  [n =     15]:	weight: 0.106	loss: 0.853	
  location_date_sensor = 15  [n =     10]:	weight: 0.147	loss: 0.778	
  location_date_sensor = 16  [n =      9]:	weight: 0.042	loss: 0.697	
  location_date_sensor = 17  [n =     10]:	weight: 0.018	loss: 0.557	
 22% 199/915 [05:04<18:16,  1.53s/it]objective: 0.139
loss_avg: 0.637
  location_date_sensor = 0  [n =     10]:	weight: 0.045	loss: 0.488	
  location_date_sensor = 1  [n =     14]:	weight: 0.007	loss: 0.498	
  location_date_sensor = 2  [n =     11]:	weight: 0.025	loss: 0.614	
  location_date_sensor = 3  [n =      8]:	weight: 0.021	loss: 0.580	
  location_date_sensor = 4  [n =     12]:	weight: 0.035	loss: 0.594	
  location_date_sensor = 5  [n =     11]:	weight: 0.016	loss: 0.668	
  location_date_sensor = 6  [n =      8]:	weight: 0.032	loss: 0.669	
  location_date_sensor = 7  [n =     11]:	weight: 0.082	loss: 0.550	
  location_date_sensor = 8  [n =     13]:	weight: 0.032	loss: 0.603	
  location_date_sensor = 9  [n =      6]:	weight: 0.078	loss: 0.659	
  location_date_sensor = 10  [n =      8]:	weight: 0.120	loss: 0.719	
  location_date_sensor = 11  [n =     11]:	weight: 0.040	loss: 0.515	
  location_date_sensor = 12  [n =     13]:	weight: 0.037	loss: 0.678	
  location_date_sensor = 13  [n =     17]:	weight: 0.112	loss: 0.641	
  location_date_sensor = 14  [n =     13]:	weight: 0.109	loss: 0.838	
  location_date_sensor = 15  [n =     14]:	weight: 0.150	loss: 0.798	
  location_date_sensor = 16  [n =      7]:	weight: 0.042	loss: 0.807	
  location_date_sensor = 17  [n =     13]:	weight: 0.018	loss: 0.581	
 27% 249/915 [06:21<17:00,  1.53s/it]objective: 0.155
loss_avg: 0.647
  location_date_sensor = 0  [n =      9]:	weight: 0.045	loss: 0.515	
  location_date_sensor = 1  [n =      4]:	weight: 0.007	loss: 0.534	
  location_date_sensor = 2  [n =     11]:	weight: 0.024	loss: 0.615	
  location_date_sensor = 3  [n =     10]:	weight: 0.021	loss: 0.579	
  location_date_sensor = 4  [n =     13]:	weight: 0.035	loss: 0.615	
  location_date_sensor = 5  [n =     13]:	weight: 0.016	loss: 0.541	
  location_date_sensor = 6  [n =     10]:	weight: 0.031	loss: 0.636	
  location_date_sensor = 7  [n =     12]:	weight: 0.082	loss: 0.614	
  location_date_sensor = 8  [n =     14]:	weight: 0.032	loss: 0.593	
  location_date_sensor = 9  [n =      9]:	weight: 0.078	loss: 0.703	
  location_date_sensor = 10  [n =     15]:	weight: 0.122	loss: 0.787	
  location_date_sensor = 11  [n =     14]:	weight: 0.040	loss: 0.561	
  location_date_sensor = 12  [n =      8]:	weight: 0.036	loss: 0.699	
  location_date_sensor = 13  [n =     10]:	weight: 0.111	loss: 0.604	
  location_date_sensor = 14  [n =     16]:	weight: 0.113	loss: 0.852	
  location_date_sensor = 15  [n =     12]:	weight: 0.150	loss: 0.760	
  location_date_sensor = 16  [n =      7]:	weight: 0.041	loss: 0.725	
  location_date_sensor = 17  [n =     13]:	weight: 0.018	loss: 0.577	
 33% 299/915 [07:37<15:38,  1.52s/it]objective: 0.141
loss_avg: 0.636
  location_date_sensor = 0  [n =     15]:	weight: 0.044	loss: 0.511	
  location_date_sensor = 1  [n =     11]:	weight: 0.006	loss: 0.504	
  location_date_sensor = 2  [n =     12]:	weight: 0.024	loss: 0.590	
  location_date_sensor = 3  [n =     11]:	weight: 0.020	loss: 0.516	
  location_date_sensor = 4  [n =      9]:	weight: 0.034	loss: 0.581	
  location_date_sensor = 5  [n =     14]:	weight: 0.016	loss: 0.751	
  location_date_sensor = 6  [n =     11]:	weight: 0.031	loss: 0.625	
  location_date_sensor = 7  [n =     14]:	weight: 0.081	loss: 0.576	
  location_date_sensor = 8  [n =      4]:	weight: 0.031	loss: 0.634	
  location_date_sensor = 9  [n =     12]:	weight: 0.077	loss: 0.618	
  location_date_sensor = 10  [n =     13]:	weight: 0.124	loss: 0.749	
  location_date_sensor = 11  [n =     10]:	weight: 0.040	loss: 0.553	
  location_date_sensor = 12  [n =     15]:	weight: 0.036	loss: 0.701	
  location_date_sensor = 13  [n =     11]:	weight: 0.110	loss: 0.624	
  location_date_sensor = 14  [n =     11]:	weight: 0.117	loss: 0.861	
  location_date_sensor = 15  [n =     10]:	weight: 0.152	loss: 0.759	
  location_date_sensor = 16  [n =      7]:	weight: 0.040	loss: 0.721	
  location_date_sensor = 17  [n =     10]:	weight: 0.018	loss: 0.587	
 38% 349/915 [08:54<14:28,  1.53s/it]objective: 0.126
loss_avg: 0.632
  location_date_sensor = 0  [n =     13]:	weight: 0.044	loss: 0.529	
  location_date_sensor = 1  [n =     11]:	weight: 0.006	loss: 0.514	
  location_date_sensor = 2  [n =     14]:	weight: 0.024	loss: 0.593	
  location_date_sensor = 3  [n =     15]:	weight: 0.020	loss: 0.553	
  location_date_sensor = 4  [n =     11]:	weight: 0.034	loss: 0.574	
  location_date_sensor = 5  [n =     11]:	weight: 0.016	loss: 0.658	
  location_date_sensor = 6  [n =     13]:	weight: 0.032	loss: 0.679	
  location_date_sensor = 7  [n =      9]:	weight: 0.082	loss: 0.625	
  location_date_sensor = 8  [n =      9]:	weight: 0.031	loss: 0.628	
  location_date_sensor = 9  [n =     12]:	weight: 0.078	loss: 0.626	
  location_date_sensor = 10  [n =      8]:	weight: 0.125	loss: 0.764	
  location_date_sensor = 11  [n =     11]:	weight: 0.039	loss: 0.512	
  location_date_sensor = 12  [n =     16]:	weight: 0.038	loss: 0.705	
  location_date_sensor = 13  [n =      8]:	weight: 0.108	loss: 0.606	
  location_date_sensor = 14  [n =      9]:	weight: 0.119	loss: 0.918	
  location_date_sensor = 15  [n =      6]:	weight: 0.150	loss: 0.816	
  location_date_sensor = 16  [n =     11]:	weight: 0.040	loss: 0.647	
  location_date_sensor = 17  [n =     13]:	weight: 0.017	loss: 0.607	
 44% 399/915 [10:10<13:09,  1.53s/it]objective: 0.147
loss_avg: 0.660
  location_date_sensor = 0  [n =     13]:	weight: 0.044	loss: 0.515	
  location_date_sensor = 1  [n =     10]:	weight: 0.006	loss: 0.551	
  location_date_sensor = 2  [n =     12]:	weight: 0.024	loss: 0.558	
  location_date_sensor = 3  [n =      9]:	weight: 0.020	loss: 0.560	
  location_date_sensor = 4  [n =     10]:	weight: 0.033	loss: 0.573	
  location_date_sensor = 5  [n =     15]:	weight: 0.017	loss: 0.794	
  location_date_sensor = 6  [n =      8]:	weight: 0.032	loss: 0.640	
  location_date_sensor = 7  [n =     10]:	weight: 0.081	loss: 0.538	
  location_date_sensor = 8  [n =     10]:	weight: 0.030	loss: 0.633	
  location_date_sensor = 9  [n =      7]:	weight: 0.077	loss: 0.704	
  location_date_sensor = 10  [n =      9]:	weight: 0.124	loss: 0.813	
  location_date_sensor = 11  [n =      9]:	weight: 0.038	loss: 0.533	
  location_date_sensor = 12  [n =     17]:	weight: 0.040	loss: 0.746	
  location_date_sensor = 13  [n =     14]:	weight: 0.109	loss: 0.686	
  location_date_sensor = 14  [n =     15]:	weight: 0.121	loss: 0.805	
  location_date_sensor = 15  [n =     14]:	weight: 0.150	loss: 0.800	
  location_date_sensor = 16  [n =      8]:	weight: 0.039	loss: 0.546	
  location_date_sensor = 17  [n =     10]:	weight: 0.018	loss: 0.657	
 49% 449/915 [11:26<11:51,  1.53s/it]objective: 0.139
loss_avg: 0.652
  location_date_sensor = 0  [n =      9]:	weight: 0.044	loss: 0.550	
  location_date_sensor = 1  [n =      6]:	weight: 0.006	loss: 0.551	
  location_date_sensor = 2  [n =     12]:	weight: 0.024	loss: 0.660	
  location_date_sensor = 3  [n =     10]:	weight: 0.020	loss: 0.553	
  location_date_sensor = 4  [n =     10]:	weight: 0.032	loss: 0.577	
  location_date_sensor = 5  [n =     12]:	weight: 0.017	loss: 0.701	
  location_date_sensor = 6  [n =     17]:	weight: 0.032	loss: 0.762	
  location_date_sensor = 7  [n =     15]:	weight: 0.080	loss: 0.548	
  location_date_sensor = 8  [n =     11]:	weight: 0.030	loss: 0.598	
  location_date_sensor = 9  [n =      9]:	weight: 0.077	loss: 0.645	
  location_date_sensor = 10  [n =     13]:	weight: 0.125	loss: 0.753	
  location_date_sensor = 11  [n =     12]:	weight: 0.038	loss: 0.505	
  location_date_sensor = 12  [n =     10]:	weight: 0.040	loss: 0.684	
  location_date_sensor = 13  [n =     12]:	weight: 0.111	loss: 0.643	
  location_date_sensor = 14  [n =      8]:	weight: 0.120	loss: 0.896	
  location_date_sensor = 15  [n =      7]:	weight: 0.152	loss: 0.853	
  location_date_sensor = 16  [n =     12]:	weight: 0.039	loss: 0.623	
  location_date_sensor = 17  [n =     15]:	weight: 0.017	loss: 0.659	
 55% 499/915 [12:43<10:35,  1.53s/it]objective: 0.147
loss_avg: 0.636
  location_date_sensor = 0  [n =      8]:	weight: 0.042	loss: 0.556	
  location_date_sensor = 1  [n =     13]:	weight: 0.006	loss: 0.510	
  location_date_sensor = 2  [n =     12]:	weight: 0.024	loss: 0.546	
  location_date_sensor = 3  [n =      9]:	weight: 0.019	loss: 0.570	
  location_date_sensor = 4  [n =     11]:	weight: 0.032	loss: 0.590	
  location_date_sensor = 5  [n =     11]:	weight: 0.017	loss: 0.688	
  location_date_sensor = 6  [n =      7]:	weight: 0.033	loss: 0.739	
  location_date_sensor = 7  [n =      6]:	weight: 0.079	loss: 0.659	
  location_date_sensor = 8  [n =     14]:	weight: 0.030	loss: 0.617	
  location_date_sensor = 9  [n =     12]:	weight: 0.076	loss: 0.668	
  location_date_sensor = 10  [n =      8]:	weight: 0.126	loss: 0.777	
  location_date_sensor = 11  [n =     14]:	weight: 0.037	loss: 0.545	
  location_date_sensor = 12  [n =     10]:	weight: 0.040	loss: 0.688	
  location_date_sensor = 13  [n =     14]:	weight: 0.110	loss: 0.611	
  location_date_sensor = 14  [n =      6]:	weight: 0.118	loss: 0.805	
  location_date_sensor = 15  [n =     18]:	weight: 0.156	loss: 0.816	
  location_date_sensor = 16  [n =     14]:	weight: 0.039	loss: 0.610	
  location_date_sensor = 17  [n =     13]:	weight: 0.017	loss: 0.564	
 60% 549/915 [13:59<09:20,  1.53s/it]objective: 0.141
loss_avg: 0.647
  location_date_sensor = 0  [n =     11]:	weight: 0.041	loss: 0.535	
  location_date_sensor = 1  [n =      9]:	weight: 0.006	loss: 0.514	
  location_date_sensor = 2  [n =     11]:	weight: 0.023	loss: 0.614	
  location_date_sensor = 3  [n =     10]:	weight: 0.019	loss: 0.545	
  location_date_sensor = 4  [n =      6]:	weight: 0.031	loss: 0.587	
  location_date_sensor = 5  [n =     12]:	weight: 0.017	loss: 0.705	
  location_date_sensor = 6  [n =     14]:	weight: 0.032	loss: 0.700	
  location_date_sensor = 7  [n =     12]:	weight: 0.078	loss: 0.633	
  location_date_sensor = 8  [n =     15]:	weight: 0.030	loss: 0.586	
  location_date_sensor = 9  [n =      8]:	weight: 0.074	loss: 0.645	
  location_date_sensor = 10  [n =      7]:	weight: 0.125	loss: 0.689	
  location_date_sensor = 11  [n =      9]:	weight: 0.037	loss: 0.540	
  location_date_sensor = 12  [n =     14]:	weight: 0.040	loss: 0.672	
  location_date_sensor = 13  [n =     12]:	weight: 0.111	loss: 0.633	
  location_date_sensor = 14  [n =     16]:	weight: 0.121	loss: 0.815	
  location_date_sensor = 15  [n =      8]:	weight: 0.162	loss: 0.861	
  location_date_sensor = 16  [n =     12]:	weight: 0.038	loss: 0.690	
  location_date_sensor = 17  [n =     14]:	weight: 0.017	loss: 0.602	
 65% 599/915 [15:16<08:02,  1.53s/it]objective: 0.141
loss_avg: 0.629
  location_date_sensor = 0  [n =      9]:	weight: 0.041	loss: 0.464	
  location_date_sensor = 1  [n =     14]:	weight: 0.006	loss: 0.504	
  location_date_sensor = 2  [n =      7]:	weight: 0.023	loss: 0.562	
  location_date_sensor = 3  [n =      7]:	weight: 0.018	loss: 0.552	
  location_date_sensor = 4  [n =     13]:	weight: 0.031	loss: 0.559	
  location_date_sensor = 5  [n =     14]:	weight: 0.017	loss: 0.613	
  location_date_sensor = 6  [n =     10]:	weight: 0.032	loss: 0.670	
  location_date_sensor = 7  [n =     11]:	weight: 0.077	loss: 0.541	
  location_date_sensor = 8  [n =     10]:	weight: 0.030	loss: 0.574	
  location_date_sensor = 9  [n =      4]:	weight: 0.071	loss: 0.570	
  location_date_sensor = 10  [n =     14]:	weight: 0.123	loss: 0.682	
  location_date_sensor = 11  [n =     12]:	weight: 0.036	loss: 0.548	
  location_date_sensor = 12  [n =     12]:	weight: 0.039	loss: 0.697	
  location_date_sensor = 13  [n =     11]:	weight: 0.110	loss: 0.626	
  location_date_sensor = 14  [n =     10]:	weight: 0.126	loss: 0.868	
  location_date_sensor = 15  [n =     13]:	weight: 0.164	loss: 0.892	
  location_date_sensor = 16  [n =     13]:	weight: 0.039	loss: 0.651	
  location_date_sensor = 17  [n =     16]:	weight: 0.018	loss: 0.637	
 67% 612/915 [15:36<07:43,  1.53s/it]

Preparing a submission

The WILDS library saves predictions in *_pred.pth files. To prepare them for submission to our competition, we have to transform them into the appropriate format.

In [ ]:
import torch
import pandas as pd

pred_false_val = torch.load(f"{SAVE_TO}/gwhd_split:val_seed:{N_EPOCHS-1}_epoch:{N_EPOCHS-1}_pred.pth")
pred_false_test = torch.load(f"{SAVE_TO}/gwhd_split:test_seed:{N_EPOCHS-1}_epoch:{N_EPOCHS-1}_pred.pth")

false_val = pd.read_csv("data/gwhd_v0.9/official_val.csv") 
false_test = pd.read_csv("data/gwhd_v0.9/official_test.csv")
In [ ]:
assert len(false_val) == len(pred_false_val)
assert len(pred_false_test) == len(pred_false_test)
In [ ]:
def encodeBoxes(tensor_boxes,score_thr=0.5):
  boxes = tensor_boxes["boxes"].numpy()
  scores = tensor_boxes["scores"].numpy()
  boxes = boxes[scores > score_thr]

  strboxes = ";".join([" ".join([str(int(i)) for i in box]) for box in boxes])

  if len(strboxes) == 0:
    strboxes = "no_box"
  
  return strboxes
In [ ]:
encoded_boxes = [encodeBoxes(tensor_boxes) for tensor_boxes in pred_false_val]
false_val["PredString"] = encoded_boxes
encoded_boxes = [encodeBoxes(tensor_boxes) for tensor_boxes in pred_false_test]
false_test["PredString"] = encoded_boxes

Sanity check before submission

We can check few predictions to get an idea of how well the training went

In [ ]:
test_img = "data/gwhd_v0.9/images/"+ false_test["image_name"].values[1]
print(test_img)
import cv2
import matplotlib.pyplot as plt
test_img = cv2.imread(test_img)
boxes = pred_false_test[1]["boxes"].numpy()
scores = pred_false_test[1]["scores"].numpy()

boxes = boxes[scores >0.5] # we set a naive threshold here 

for (x,y,xx,yy) in boxes:
  cv2.rectangle(test_img,(int(x),int(y)),(int(xx),int(yy)),(255,0,0),5)

plt.imshow(test_img[...,::-1])

Writing submission file

In [ ]:
results = pd.concat([false_test,false_val]) #Val and test does not correspond to the true split
del results["BoxesString"] # We need to remove this column or it causes damage :)
results["image_name"] = results["image_name"].apply(lambda x: x.replace(".png","")) # we need to remove the extension
results.to_csv("submission_final.csv",index=False)

Making Direct Submission thought Aicrowd CLI

In [ ]:
!aicrowd submission create -c global-wheat-challenge-2021 -f submission_final.csv
In [ ]:

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