Localization of thoracic abnormalities model based on VinBigData (top 1%)

Last update: May 24, 2022

Overview

Repository contains the code for 2nd place solution of VinBigData Chest X-ray Abnormalities Detection competition. The goal of competition was to automatically localize and classify thoracic abnormalities from chest radiographs.

Kaggle forum posts about the solution:

Details

Solution consists of 3 parts. Each part is models from each team member. Predictions of each part in the end ensembled in single 2nd place submission on LeaderBoard. You can use only inference or train models from scratch.

Warning: since some of the data is hosted on Kaggle, in order to be able to download it, save your Kaggle API token to .kaggle/kaggle.json

Only inference

cd part_zfturbo
pip install -r requirements.txt
sh ./preproc.sh
sh ./inference.sh
cd ..

cd part_ivan
sh ./setup.sh
sh ./preproc.sh
sh ./inference.sh
cd ..

cd part_sergey
sh ./inference.sh
cd ..

python3 ensemble_models.py

Train

cd part_zfturbo
pip install -r requirements.txt
sh ./preproc.sh
sh ./train.sh
sh ./inference.sh
cd ..

cd part_ivan
sh ./setup.sh
sh ./preproc_train.sh
sh ./train.sh
sh ./inference.sh
cd ..

cd part_sergey
sh ./train.sh
sh ./inference.sh
cd ..

python3 ensemble_models.py

Comments

Question to sergey part, 640 or 1024 size?

Question to Sergey Part, why he first resizes to 640 for inference, but then normalizes the resultant boxes like they were for 1024*1024 pixels image?

pred_boxes, pred_scores, pred_labels = predict_for_files(weights, folder, imagenames, 640, is_TTA)
....
if len(cur_boxes) > 0:
    cur_boxes[:, [0, 2]] = (cur_boxes[:, [0, 2]] * image_width / 1024).astype(int)
    cur_boxes[:, [1, 3]] = (cur_boxes[:, [1, 3]] * image_height / 1024).astype(int)

opened by RedMoon32 2

Regarding Learning Rate

Regarding ivan part on mmdetection Can you please upload schedule_1x.py. The below error is seen. There was another solution recommended in (https://github.com/open-mmlab/mmdetection/issues/6456) Can you confirm on the same.

Traceback (most recent call last): File "/home/prashant/anaconda3/envs/kmmdet/lib/python3.7/site-packages/mmcv/utils/registry.py", line 52, in build_from_cfg return obj_cls(**args) File "/home/prashant/anaconda3/envs/kmmdet/lib/python3.7/site-packages/mmcv/runner/hooks/lr_updater.py", line 264, in init super(CosineAnnealingLrUpdaterHook, self).init(**kwargs) TypeError: init() got an unexpected keyword argument 'step'

opened by kailasdayanandan 1
No hyper-parameter yaml found

Thanks for sharing your great work.

I was trying to run the training code for yolov5, but then it gives me an error saying "AssertionError: File Not Found: data/hyp.scratch.yaml"

I can not find any scrip that generates hyp.scratch.yaml.

Am I missing something?

Thanks, Joseph

opened by shreka116 2
No MegaMix 341_healthy file

Hello! Again question about Sergey part) After inference, postprocess.py requires "../MegaMix/341_healthy.csv" file, can you please say where I can download it and why it is needed? Блин чет забыл что авторы русские и написал на английском вопрос опять)

opened by RedMoon32 1
Any pre trained weights

Thanks for posting the solution. do you have guys have trained weights that can be used for further fine tuning of different related tasks ? Regards Jaideep

opened by jaideep11061982 2

Releases(v1.0)

v1.0(Apr 12, 2021)

Source code(tar.gz)
Source code(zip)
retinanet_resnet101_500_classes_0.4986.h5(752.34 MB)
retinanet_resnet101_sqr.zip(989.96 MB)
retinanet_resnet101_sqr_removed_rads.zip(989.96 MB)
yolov5x.pt(167.96 MB)
yolo_best.zip(1129.64 MB)

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