Loading
Feedback

rohitmidha23 237

Name

Rohit Midha

Organization

SSN College of Engineering, Anna University

Location

Chennai, IN

Badges

0
3
2

Activity

May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Mon
Wed
Fri

Ratings Progression

Loading...

Challenge Categories

Loading...

Challenges Entered

Latest submissions

No submissions made in this challenge.

Airborne Object Tracking Challenge

Latest submissions

No submissions made in this challenge.

A benchmark for image-based food recognition

Latest submissions

See All
graded 115895
failed 113237
failed 113221

Predicting smell of molecular compounds

Latest submissions

See All
graded 93231
graded 93227
graded 93225

Latest submissions

No submissions made in this challenge.

3D seismic image Interpretation by Machine Learning

Latest submissions

See All
graded 86014
graded 82969
graded 82879

Latest submissions

No submissions made in this challenge.

Latest submissions

No submissions made in this challenge.

Latest submissions

No submissions made in this challenge.

Grouping/Sorting players into their respective teams

Latest submissions

See All
graded 85322
graded 85294
graded 84890

Latest submissions

See All
failed 9976

5 Problems 15 Days. Can you solve it all?

Latest submissions

No submissions made in this challenge.

Sample-efficient reinforcement learning in Minecraft

Latest submissions

No submissions made in this challenge.

Multi Agent Reinforcement Learning on Trains.

Latest submissions

No submissions made in this challenge.

Recognise Handwritten Digits

Latest submissions

See All
graded 60257

Online News Prediction

Latest submissions

See All
graded 60274

Crowdsourced Map Land Cover Prediction

Latest submissions

See All
graded 60300

5 Problems 15 Days. Can you solve it all?

Latest submissions

See All
graded 67394
failed 67393
graded 67389

Project 2: Road extraction from satellite images

Latest submissions

No submissions made in this challenge.

Project 2: build our own text classifier system, and test its performance.

Latest submissions

No submissions made in this challenge.

Help improve humanitarian crisis response through better NLP modeling

Latest submissions

See All
graded 58201
graded 58181
graded 58179

Reincarnation of personal data entities in unstructured data sets

Latest submissions

No submissions made in this challenge.

Robots that learn to interact with the environment autonomously

Latest submissions

No submissions made in this challenge.

Latest submissions

No submissions made in this challenge.

5 PROBLEMS 3 WEEKS. CAN YOU SOLVE THEM ALL?

Latest submissions

See All
graded 77370
graded 77319

Latest submissions

No submissions made in this challenge.

Latest submissions

See All
graded 67545
graded 66085
failed 66070

Latest submissions

No submissions made in this challenge.

Latest submissions

No submissions made in this challenge.

Latest submissions

No submissions made in this challenge.

Latest submissions

No submissions made in this challenge.

Classify Scrambled Text

Latest submissions

No submissions made in this challenge.
Gold 0
Silver 3
Boltzmann's Favourite
May 16, 2020
Boltzmann's Favourite
May 16, 2020
Boltzmann's Favourite
May 16, 2020
Bronze 2
Trustable
May 16, 2020
Newtonian
May 16, 2020

Badges


  • May 16, 2020

  • May 16, 2020
  • Has filled their profile page
    May 16, 2020

  • May 16, 2020

  • May 16, 2020

  • May 16, 2020

  • May 16, 2020

  • May 16, 2020

  • May 16, 2020

  • May 16, 2020

  • May 16, 2020

  • May 16, 2020
  • Kudos! You've been awarded a silver badge for this challenge. Keep up the great work!
    Challenge: ORIENTME
    May 16, 2020
  • Kudos! You've been awarded a silver badge for this challenge. Keep up the great work!
    Challenge: AMLD 2020 - Transfer Learning for International Crisis Response
    May 16, 2020
  • Kudos! You've been awarded a silver badge for this challenge. Keep up the great work!
    Challenge: Food Recognition Challenge
    May 16, 2020
  • Kudos! You've won a bronze badge in this challenge. Keep up the great work!
    Challenge: NeurIPS 2019: Learn to Move - Walk Around
    May 16, 2020
Participant Rating
jyoti_yadav2 0
shivam
shraddhaa_mohan 252
nimishsantosh107 134
Participant Rating
shivam
shraddhaa_mohan 252
nimishsantosh107 134

Seismic Facies Identification Challenge

Clarification : Submission Count

8 months ago

On the submissions page it says “5 submissions remaining”. Is this on a per day basis or across the challenge?

Can this also be made clear on the challenge page?

Thank You,
Rohit Midha

Hockey Team Classification

Is this a fully unsupervised clustering challenge

9 months ago

@jason_brumwell just so I’ve understood your reply clearly, we can use external datasets and (or) create a dataset on our own, for training a supervised model, so long as we don’t hand label the current dataset provided by you?

Secondly, is there a private test set in the challenge since you’ve mentioned “when additional teams are added”? If there isn’t a private test set can you explain what you mean by this or is this just a general statement?

Any clarity on this would be greatly appreciated.

Thanking you,
Rohit

FOODC

FOODC Editorial

12 months ago

The Challenge

Maintaining a healthy diet is difficult. As the saying goes, the best way to escape a problem is to solve it. So why not leverage the power of deep learning and computer vision to build the foundation of a semi-automated food tracking application?

With over 9300 hand-annotated images with 61 classes, the challenge is to train accurate models that can look at images of food items and detect the food items present in the image.

It's time to unleash the food (data)scientist in you! Given any image, identify the food item present in it.

Downloads and Installs

In [0]:
!wget -q https://s3.eu-central-1.wasabisys.com/aicrowd-practice-challenges/public/foodc/v0.1/train_images.zip
!wget -q https://s3.eu-central-1.wasabisys.com/aicrowd-practice-challenges/public/foodc/v0.1/test_images.zip
!wget -q https://s3.eu-central-1.wasabisys.com/aicrowd-practice-challenges/public/foodc/v0.1/train.csv
!wget -q https://s3.eu-central-1.wasabisys.com/aicrowd-practice-challenges/public/foodc/v0.1/test.csv
In [0]:
!mkdir data
!mkdir data/test
!mkdir data/train
!unzip train_images -d data/train
!unzip test_images -d data/test
In [0]:
!mkdir models

Imports

In [0]:
import sys
import os
import gc
import warnings
import torch

import torch.nn as nn
import numpy as np
import pandas as pd 
import torch.nn.functional as F

from fastai.script import *
from fastai.vision import *
from fastai.callbacks import *
from fastai.distributed import *
from fastprogress import fastprogress
from torchvision.models import *
In [0]:
np.random.seed(23)
torch.cuda.device(0)
warnings.filterwarnings("ignore")
torch.multiprocessing.freeze_support()
print("[INFO] GPU:", torch.cuda.get_device_name())
[INFO] GPU: Tesla P100-PCIE-16GB

DataBunch and Model

Here we use a technique called progressive resizing. At each step the model is loaded with weights trained on images of lower sizes.

In [0]:
def get_data(size, batch_size):
  """
  function that returns a DataBunch as needed for the Learner
  """
  train = pd.read_csv("train.csv")
  src = (ImageList.from_df(train, path="data/", folder="train/train_images/").split_by_rand_pct(0.1).label_from_df())
  src.add_test_folder("test/test_images/")
  tfms = get_transforms(do_flip=True, flip_vert=False, max_rotate=10.0, 
                      max_zoom=1.1, max_lighting=0.2, max_warp=0.2, p_affine=0.75, p_lighting=0.75)

  data = (src.transform(
      tfms,
      size=size,
      resize_method=ResizeMethod.SQUISH)
      .databunch(bs=batch_size)
      .normalize(imagenet_stats))
  assert sorted(set(train.ClassName.unique())) == sorted(data.classes), "Class Mismatch"
  print("[INFO] Number of Classes: ", data.c)
  data.num_workers = 4
  return data
In [0]:
sample_data = get_data(32, (2048//32))
sample_data.show_batch(3, 3)
[INFO] Number of Classes:  61

As you can see, the transforms have been applied and the image is normalized as well!

We first initialize all the models.

In [0]:
learn = create_cnn(get_data(32, (2048//32)), models.densenet161, 
                   metrics=[accuracy, FBeta(beta=1,average='macro')])
learn.model_dir = "models/"
learn.save("densenet_32")

learn = create_cnn(get_data(64, (2048//64)), models.densenet161, 
                   metrics=[accuracy, FBeta(beta=1,average='macro')]).load("densenet_32")
learn.model_dir = "models/"
learn.save("densenet_64")

learn = create_cnn(get_data(128, (2048//128)), models.densenet161, 
                   metrics=[accuracy, FBeta(beta=1,average='macro')]).load("densenet_64")
learn.model_dir = "models/"
learn.save("densenet_128")

learn = create_cnn(get_data(256, (2048//256)), models.densenet161, 
                   metrics=[accuracy, FBeta(beta=1,average='macro')]).load("densenet_128")
learn.model_dir = "models/"
learn.save("densenet_256")
[INFO] Number of Classes:  61
Downloading: "https://download.pytorch.org/models/densenet161-8d451a50.pth" to /root/.cache/torch/checkpoints/densenet161-8d451a50.pth
[INFO] Number of Classes:  61
[INFO] Number of Classes:  61
[INFO] Number of Classes:  61
In [0]:
def train_model(size, iter1, iter2, mixup=False):
  """
  function to quickly train a model for a certain number of iterations.
  """
  size_match = {"256": "128", "128": "64", "64": "32"}
  learn = create_cnn(get_data(size, (2048//size)), models.densenet161, 
                     metrics=[accuracy, 
                              FBeta(beta=1,average='macro')])
  learn.model_dir = "models/"
  if mixup:
    learn.mixup()
  if str(size) != str(32):
    learn.load("densenet_" + str(size_match[str(size)]))

  name = "densenet_" + str(size)
  print("[INFO] Training for : ", name)

  learn.fit_one_cycle(iter1, 1e-4, callbacks=[ShowGraph(learn),
                            SaveModelCallback(learn, monitor='f_beta', mode='max', name=name)])
  learn.unfreeze()
  learn.fit_one_cycle(iter2, 5e-5, callbacks=[ShowGraph(learn),
                            SaveModelCallback(learn, monitor='f_beta', mode='max', name=name)])

Here you might notice the use of a function mixup. mixup is a callback in fastai that is extremely efficient at regularizing models in computer vision.

Instead of feeding the model the raw images, we take two images (not necessarily from the same class) and make a linear combination of them: in terms of tensors, we have:

new_image = t * image1 + (1-t) * image2

where t is a float between 0 and 1. The target we assign to that new image is the same combination of the original targets:

new_target = t * target1 + (1-t) * target2

assuming the targets are one-hot encoded (which isn’t the case in PyTorch usually). And it's as simple as that.

For example:

Source Dog or cat? The right answer here is 70% dog and 30% cat!
In [0]:
train_model(32, 5, 3)
[INFO] Number of Classes:  61
[INFO] Training for :  densenet_32
epoch train_loss valid_loss accuracy f_beta time
0 5.436698 4.320179 0.106223 0.053227 01:54
1 4.155217 3.488357 0.257511 0.111412 01:54
2 3.625813 3.116575 0.283262 0.144687 01:55
3 3.403799 3.113646 0.290773 0.148819 01:56
4 3.333214 3.136955 0.293991 0.144410 01:56
Better model found at epoch 0 with f_beta value: 0.05322723090648651.
Better model found at epoch 1 with f_beta value: 0.1114121824502945.
Better model found at epoch 2 with f_beta value: 0.14468735456466675.
Better model found at epoch 3 with f_beta value: 0.14881914854049683.
epoch train_loss valid_loss accuracy f_beta time
0 3.269448 2.944852 0.311159 0.151784 02:01
1 3.095446 2.667753 0.329399 0.163058 02:01
2 2.985259 2.677143 0.334764 0.164230 02:02
Better model found at epoch 0 with f_beta value: 0.15178431570529938.
Better model found at epoch 1 with f_beta value: 0.1630583107471466.
Better model found at epoch 2 with f_beta value: 0.1642296463251114.
In [0]:
train_model(64, 5, 4)
[INFO] Number of Classes:  61
[INFO] Training for :  densenet_64
epoch train_loss valid_loss accuracy f_beta time
0 3.042036 2.391506 0.375536 0.202430 02:24
1 2.755056 2.175985 0.427039 0.274385 02:23
2 2.513455 2.062872 0.440987 0.286241 02:23
3 2.333173 2.029333 0.448498 0.294666 02:23
4 2.274806 2.010746 0.449571 0.299761 02:23
Better model found at epoch 0 with f_beta value: 0.20242981612682343.
Better model found at epoch 1 with f_beta value: 0.2743850350379944.
Better model found at epoch 2 with f_beta value: 0.286241352558136.
Better model found at epoch 3 with f_beta value: 0.2946656346321106.
Better model found at epoch 4 with f_beta value: 0.2997610867023468.
epoch train_loss valid_loss accuracy f_beta time
0 2.224584 2.064080 0.450644 0.308239 02:32
1 2.183188 1.941107 0.477468 0.358477 02:32
2 1.866471 1.893163 0.482833 0.357009 02:33
3 1.833622 1.912134 0.483906 0.363549 02:33
Better model found at epoch 0 with f_beta value: 0.3082387149333954.
Better model found at epoch 1 with f_beta value: 0.3584773540496826.
Better model found at epoch 3 with f_beta value: 0.36354920268058777.
In [0]:
train_model(128, 7, 4, mixup=True)
[INFO] Number of Classes:  61
[INFO] Training for :  densenet_128
epoch train_loss valid_loss accuracy f_beta time
0 3.102915 1.607829 0.563305 0.414498 03:27
1 2.943032 1.549630 0.581545 0.438603 03:26
2 2.808276 1.498592 0.587983 0.435788 03:26
3 2.682379 1.481404 0.592275 0.444419 03:27
4 2.538528 1.465215 0.580472 0.441078 03:28
5 2.511207 1.447936 0.597640 0.465081 03:26
6 2.440458 1.438690 0.604077 0.465968 03:25
Better model found at epoch 0 with f_beta value: 0.4144982099533081.
Better model found at epoch 1 with f_beta value: 0.43860334157943726.
Better model found at epoch 3 with f_beta value: 0.44441917538642883.
Better model found at epoch 5 with f_beta value: 0.4650808572769165.
Better model found at epoch 6 with f_beta value: 0.46596816182136536.
epoch train_loss valid_loss accuracy f_beta time
0 2.546155 1.477883 0.585837 0.457701 03:43
1 2.494597 1.511773 0.579399 0.443396 03:44
2 2.333117 1.432688 0.595494 0.473695 03:44
3 2.253165 1.432526 0.597640 0.471653 03:43
Better model found at epoch 0 with f_beta value: 0.4577012360095978.
Better model found at epoch 2 with f_beta value: 0.4736945331096649.
In [0]:
train_model(256, 7, 5, mixup=True)
[INFO] Number of Classes:  61
[INFO] Training for :  densenet_256
epoch train_loss valid_loss accuracy f_beta time
0 2.703704 1.285418 0.629828 0.506337 05:32
1 2.622411 1.273359 0.631974 0.494505 05:30
2 2.474278 1.328985 0.607296 0.483533 05:31
3 2.390934 1.312649 0.619099 0.496389 05:32
4 2.265631 1.301950 0.610515 0.480573 05:33
5 2.341162 1.284232 0.624463 0.505368 05:35
6 2.306352 1.292962 0.621245 0.501745 05:36
Better model found at epoch 0 with f_beta value: 0.50633704662323.
epoch train_loss valid_loss accuracy f_beta time
0 2.633306 1.271392 0.637339 0.507305 06:12
1 2.680736 1.447017 0.596566 0.460401 06:13
2 2.451501 1.412368 0.596566 0.469816 06:13
3 2.242612 1.392771 0.609442 0.487551 06:13
4 2.171517 1.368796 0.619099 0.496713 06:12
Better model found at epoch 0 with f_beta value: 0.5073045492172241.
In [0]:
learn = create_cnn(get_data(300, (2048//300)), models.densenet161, 
                   metrics=[accuracy, FBeta(beta=1,average='macro')]).load("densenet_256")
learn.model_dir = "models/"
learn.mixup()
size = 300
name = "densenet_" + str(size)
print("[INFO] Training for : ", name)

learn.fit_one_cycle(5, 1e-4, callbacks=[ShowGraph(learn),
                          SaveModelCallback(learn, monitor='f_beta', mode='max', name=name)])
[INFO] Number of Classes:  61
[INFO] Training for :  densenet_300
epoch train_loss valid_loss accuracy f_beta time
0 2.749508 1.281459 0.644850 0.566936 06:56
1 2.606565 1.301558 0.634120 0.522477 06:56
2 2.626434 1.291356 0.637339 0.534306 06:55
3 2.604175 1.296236 0.650215 0.560165 07:01
4 2.425535 1.281673 0.648069 0.548248 07:00
Better model found at epoch 0 with f_beta value: 0.5669360160827637.
In [0]:
learn.load("densenet_300")
interp = ClassificationInterpretation.from_learner(learn)
losses, idxs = interp.top_losses()

display(interp.plot_top_losses(9, figsize=(15,11)))
display(interp.plot_confusion_matrix(figsize=(12,12), dpi=100))
None
None
In [0]:
print("[INFO] MOST CONFUSED:")
interp.most_confused(min_val=5)
[INFO] MOST CONFUSED:
Out[0]:
[('coffee-with-caffeine', 'espresso-with-caffeine', 15),
 ('salad-leaf-salad-green', 'mixed-salad-chopped-without-sauce', 11),
 ('bread-white', 'butter', 7),
 ('bread-sourdough', 'bread-wholemeal', 6),
 ('bread-white', 'bread-wholemeal', 6),
 ('salad-leaf-salad-green', 'leaf-spinach', 6),
 ('butter', 'bread-wholemeal', 5),
 ('coffee-with-caffeine', 'white-coffee-with-caffeine', 5),
 ('espresso-with-caffeine', 'coffee-with-caffeine', 5)]

The model is getting confused between some very common categories like coffee-with-caffeine and espresso-with-caffeine.

The model needs to be made more robust to this and hence appropriate augmentations can be used.

In [0]:
def make_submission(learn, name):
  images = []
  prediction = []
  probability = []
  test_path = "data/test/test_images/"
  test = pd.read_csv("test.csv")
  files = test.ImageId
  for i in files:
        images.append(i)
        img = open_image(os.path.join(test_path, i))
        pred_class, pred_idx, outputs = learn.predict(img)
        prediction.append(pred_class.obj)
        probability.append(outputs.abs().max().item())
  answer = pd.DataFrame({'ImageId': images, 'ClassName': prediction, 'probability': probability})
  display(answer.head())
  answer[["ImageId","ClassName"]].to_csv(name, index=False)
In [0]:
make_submission(learn, name="submission_size300.csv")
ImageId ClassName probability
0 90e63a2fde.jpg water 0.994021
1 a554d1ca8d.jpg water-mineral 0.990370
2 48317e8ee8.jpg water 0.856607
3 79528df667.jpg hard-cheese 0.901751
4 6d2f2f63f5.jpg bread-wholemeal 0.979332

Improving Further

  • Appropriate augmnentations
  • Different models like densenet201, resnet50
  • Mixed Precision training (i.e. to_fp16() in fastai)

Authors

🚀 Rohit Midha

👾 Shraddhaa Mohan

Food Recognition Challenge

Can I submit code in PyTorch?

About 1 year ago

Yes, absolutely.
The libraries installed on the computer that the evaluation runs on is defined by you in the Docker file. As long as you make the respective changes there, you can use any library that you want!

Regards,
AIcrowd Team

Dataset on Kaggle

About 1 year ago

Please note that the dataset is now available for access on Kaggle as well. This is to allow for the problem statement, the dataset and the starter notebooks to be accessible from Kaggle’s vast data science community.

Please find the dataset here: https://www.kaggle.com/rohitmidha23/food-recognition-challenge/

Do let us know if you face any problems accessing the data.

Regards
AIcrowd Team

Kaggle Dataset Related

About 1 year ago

(topic withdrawn by author, will be automatically deleted in 24 hours unless flagged)

There’s a Round 2?!

About 1 year ago

@HarryWalters we’d love for you to participate.
We’ve added a few more starter notebooks and updated the prizes for Round 2 as well. Do take a look.

Graded test set similar to being as already uploaded one?

About 1 year ago

Hey @hannan4252, when you submit, your model is made to predict on a private test set which is different from the val/test set released.

I hope this clears your doubt.

Regards,
Rohit

Submissions failing, no reason given

Over 1 year ago

We also tagged aicrowd-bot but no information/logs were provided cause the issue randomly restarted evaluation and then failed.

Weird submission pattern

Over 1 year ago

(topic withdrawn by author, will be automatically deleted in 24 hours unless flagged)

Submission confusion. Am I dumb?

Over 1 year ago

Not sure what could be your problem, but we wrote code to check if the GPU was even there and it gave an error. So if your code uses GPU you have your answer.

@shivam @mohanty please add the GPU back. Thanks :smile:

Issue with aicrowd_helpers.py

Over 1 year ago

@nikhil_rayaprolu then our code seems to be exiting local eval properly and is giving proper outputs, but when we submit to aicrowd, it doesn’t fail/succeed :confused:

Issue with aicrowd_helpers.py

Over 1 year ago

In particular,

    aicrowd_events.register_event(
                event_type=aicrowd_events.AICROWD_EVENT_SUCCESS,
                message="execution_success",
                payload={ #Arbitrary Payload
                    "event_type": "food_recognition_challenge:execution_success",
                    "predictions_output_path" : predictions_output_path
                    },
                blocking=True
                )

this is the part of the code that doesn’t seem to be working.

Further, one thing I noticed while running ./debug.sh was that even if an error occurred, the command didn’t stop.
A suggestion would be to add a check for that, or maybe even a timer, since our submissions are getting delayed.

Issues with submitting

Over 1 year ago

@shivam I seem to be getting a HTTPS error. Can you check?

Issues with submitting

Over 1 year ago

@shivam @nikhil_rayaprolu my submission has be in the “submitted” phase for more than a day now. Can you check up on it?

Or at least cancel it so I can submit other stuff?

Issues with submitting

Over 1 year ago

@shivam I made a submission at 10.45am IST and it still hasn’t finished evaluating. Is there any problem on the server side?

Issues with submitting

Over 1 year ago

@shivam is the test set on the server different? When running local evaluation we got a different mAP and recall, hence the question.

ImageCLEF 2020 VQA-Med - VQA

Challenge completed?

About 1 year ago

Any idea why this challenge says it’s completed?

ImageCLEF 2020 Caption - Concept Detection

Possibility of mixed teams

About 1 year ago

Hey,
As per the rules we need to have an affiliation to an organization. Is it possible to form teams across organizations?
Say between two independent researchers and two researchers from a company?

@mohanty can you clarify?

Thanks!

AMLD 2020 - Transfer Learning for International...

Rssfete and tearth: Thank you so much

Over 1 year ago

@student same here. We did this competition more as a getting started with NLP competition. So if you don’t mind, can you give us a brief overview of your solution?

Congrats on winning!

rohitmidha23 has not provided any information yet.

Notebooks

Create Notebook