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leocd
Leo Cahya Dinendra

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Challenges Entered

Understand semantic segmentation and monocular depth estimation from downward-facing drone images

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A benchmark for image-based food recognition

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What data should you label to get the most value for your money?

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graded 179064
graded 179053
graded 179052

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graded 190340
graded 190316
graded 189982

ASCII-rendered single-player dungeon crawl game

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Machine Learning for detection of early onset of Alzheimers

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graded 140851

3D Seismic Image Interpretation by Machine Learning

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graded 157061
graded 156573
graded 156572

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Play in a realistic insurance market, compete for profit!

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graded 110896
graded 110895
graded 110894

5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles 21 Days. Can you solve it all?

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5 Puzzles, 3 Weeks. Can you solve them all? 😉

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Predicting smell of molecular compounds

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5 PROBLEMS 3 WEEKS. CAN YOU SOLVE THEM ALL?

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graded 157061
graded 156573
graded 156572
Participant Rating
saeful_ghofar_zamianie_putra 0
shivam 136
vrv 0
Participant Rating
shivam 136

Data Purchasing Challenge 2022

I need to say this

11 months ago

Wow. What a clickbait-y title. But that got your attention :stuck_out_tongue:

I haven’t properly said it before, but Thank you Zew and Aicrowd for organizing this competition. Thank you, fellow participants. I learn lots of new stuff from this, especially from the top LB solutions & other participants’ notebooks. I think I already got in my mind the best practice when facing this kind of problem in my work in the near future (sooner or later I think I’ll be facing this too, and labeling will be more expensive because of engineer/scientist level labeler needed for the data).

Hope you guys are always in good health.

Cheers

:rotating_light: Select submissions for final evaluation

12 months ago

Hi @dipam , just need a little clarifications about your post :

The detailed steps are given below:

  1. Eligible teams will select two of their submissions to evaluate - Eligibility criteria to be announced soon, it will be based on Round 2 leaderboard.
  2. Each submission will run through the pre-train and the purchase phase on the end of competition dataset.
  3. The same purchased labels will be put through 5 training pipelines - Details to be released soon.
  4. Each training pipeline will be run for 2 seeds and scores averaged, to address any stochasticity in scores.
  5. To avoid issues due to difference of average scores from different training pipelines, a Borda ranking system will be used.

while the 5 training pipelines results scored using Borda ranking system, how about the submissions? is it the highest score from the submission that is being used or is it an average from both submission results?

Simple Way to know any defect on image, finding noisy label, etc using OpenCV

12 months ago

Hi guys, I made a notebook about a simple method to detect defects on images using OpenCV.
It really helps me in detecting noisy labels and adding extra strategies on selecting which data to buy/skip.

you can read it here: AIcrowd | Simple Way to Detect Noisy Label with opencv | Posts

Hope it helps with your training or buying strategy too!
Also pls leave some likes if you don’t mind!

📹 Town Hall Recording & Resources from top participants

About 1 year ago

I tried this locally too! :grin:
but still beaten by buying naive prediction on dent label

Need Clarification for Round 2

About 1 year ago

Hi AIcrowd Team, just want to clarify something :

  1. In the post-purchase training phase,
# Create a runtime instance of the purchased dataset with the right labels
purchased_dataset = instantiate_purchased_dataset(unlabelled_dataset, purchased_labels)
aggregated_dataset = torch.utils.data.ConcatDataset(
    [training_dataset, purchased_dataset]
)
print("Training Dataset Size : ", len(training_dataset))
print("Purchased Dataset Size : ", len(purchased_dataset))
print("Aggregataed Dataset Size : ", len(aggregated_dataset))

DEBUG_MODE = os.getenv("AICROWD_DEBUG_MODE", False)
if DEBUG_MODE:
    TRAINER_CLASS = ZEWDPCDebugTrainer
else:
    TRAINER_CLASS = ZEWDPCTrainer

trainer = ZEWDPCTrainer(num_classes=6, use_pretrained=True)
trainer.train(
    training_dataset, num_epochs=10, validation_percentage=0.1, batch_size=5
)

y_pred = trainer.predict(val_dataset)
y_true = val_dataset_gt._get_all_labels()

shouldn’t it be something like this?

trainer.train(
    aggregated_dataset , num_epochs=10, validation_percentage=0.1, batch_size=5
)
  1. Because the combined and different time budget, shouldn’t it be something like this?

instead of the original diagram?

or did I assume it wrong?

Thanks.

Brainstorming On Augmentations

About 1 year ago

  1. I just want to make it more versatile to any augmentation pipeline I want to use. or maybe that’s the incorrect way? Does anyone else mess with the dataset classes only me? (asking the others)

  2. I deleted it to show the result “my way” of training the random pick one from scratch.
    My main pipeline is consist of pretraining, using the model to select purchases, resetting the weight then train it from scratch. I don’t think pretraining won’t do anything helpful if I want to do that.

  3. I think reproducing is supposed to be doing the same and using the same thing. so probably just like you guess or the maybe seed. thanks for the indirect suggestion I’ll try to add every method from here Reproducibility — PyTorch 1.10 documentation

  4. sorry for that I guess?

hi @shivam , sorry to drag you in, just to make sure are there any specific rules about only using a certain way in making the solution (like class, code writing, ml pipelines, frameworks, save path, etc)?

Brainstorming On Augmentations

About 1 year ago

Yep.
At first, I tried feeding both the raw + pre-processed ones but it gives a really bad score.
probably because different way of convnet learns from those two types of images.
now I go either using the pre-processed only or raw only.

the seismic challenge while back, the rms attribute does help scale the amplitude. while the raw doesn’t really help me. Apparently, it’s quite different now while the raw can perform well too, the pre-trained weight also helps significantly.

Brainstorming On Augmentations

About 1 year ago

I’m only using :

  • RandomHorizontalFlip,
  • RandomVerticalFlip,
  • RandomRotation,

you can see it on my notebook here :

I don’t use any color augmentation at all because some of my current high submissions came from using no raw image input (though I still run some experiments on raw input one in case the preprocess one hit the ceiling, the same experience from the seismic competition before with @santiactis )

Experiments with “unlabelled” data

About 1 year ago

yes, it’s very significant.

From my experiment notebook its something like this :

exp no. augmentation pretrained purchase_method score_pretraining_phase score_purchase_phase score_validation_phase LB_Score
1 NO NO NO 0.773 0.773 0.760
2 NO NO RANDOM 3000 0.773 0.804 0.760
3 NO NO ALL 10000 0.773 0.841 0.835
4 NO YES NO 0.857 0.857 0.850
5 NO YES RANDOM 3000 0.857 0.864 0.845 0.851
6 NO YES ALL 10000 0.857 0.892 0.875
7 YES YES NO 0.868 0.868 0.865
8 YES YES RANDOM 3000 0.868 0.886 0.869 0.880
9 YES YES ALL 10000 0.868 0.902 0.893

the notebook :

My Multiple Experiments Results ( the random one got 0.88 on LB)

About 1 year ago

Here’s my multiple experiment results score that I log into tables.

I’m using this same parameter for each experiment :

model : efficienet-b1
input: raw image
epoch: 20
optim: Adam

tl;dr, use augmentation and pre-trained weight.

I hope you it help you guys, especially for those who just joined.

Size of Datasets

About 1 year ago

what’s your comment about this @shivam ?
I think it’s 5000 training images, 3000 to purchase, 3000 to test right? just as in the overview.

Full list of available pretrained weights

About 1 year ago

wow, which pytorch version that got vit? nightly version?

Submit failed with no error log

About 1 year ago

yes, it should be like that but mine didn’t show up.

What is this validation submission phase error log means?

About 1 year ago

==================================
Deleting unsupported pre-trained model: ./.cache/pip/wheels/76/ee/9c/36bfe3e079df99acf5ae57f4e3464ff2771b34447d6d2f2148/gym-0.21.0-py3-none-any.whl
Deleting unsupported pre-trained model: ./.cache/pip/http/1/3/0/c/a/130ca645ced2b235e6f69505044bb4923f610dbb4bc6c8e1d76a50bb
Deleting unsupported pre-trained model: ./.cache/pip/http/8/f/8/e/b/8f8eb31d64d7424ab679aad519c22a7bf4f40ab17d1c4bad52b49a9c
Deleting unsupported pre-trained model: ./.cache/pip/http/a/d/c/0/3/adc03ed04ad13ffdeee3c838911d25a9f3659c9e3590f34fa6bf3a7e
Deleting unsupported pre-trained model: ./.git/objects/pack/pack-c148ae0f71d82068775278a3044e1a3c25b5f4a3.pack
Time left: 10800
timeout: the monitored command dumped core
/home/aicrowd/run.sh: line 38:    61 Segmentation fault      timeout -s 9 $AICROWD_TIMEOUT_INFO python aicrowd_client/launcher.py

Submit failed with no error log

About 1 year ago

I got error in the “Validate Submission” phase but with no log too

🚀 Discussion on Starter Kit

About 1 year ago

hi @vrv ,

I tried using this submission method instead : AIcrowd
the push works, checked it on gitlab, but somehow it’s not on the submission. the tag `submission-`` prefix is right too. Any idea why?

Can we access the labels.csv files from the training data folder?

About 1 year ago

Is it possible to access and read the csv files of training data labels?
or we only have the access to training_dataset class?
what’s the filepath to access it? is it just ./data/training/images/labels.csv just like in the notebook example?

Allowance of Pre-trained Model

About 1 year ago

In my past experience doing challenges in Aicrowd, the committee has been really fair. I spotted some accounts that I suspected of cheating that before I reported it (with evidence of course), they have already taken care of it.

What did you get so far?

About 1 year ago

I just turned on my PC, you guys are so fast…

Seismic Facies Identification Challenge

Which average method is used in the calculation of f1-score?

Over 2 years ago

From the discord, @mohanty said it’s macro. But they might change it to weighted maybe in the 2nd round.
cmiiw

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