lee-gwang/aiconnect_action_recognition

1st solution

Aiconnect_21_Action_Recognition

Summary

Data Path

​```
${Solution_Folder}
├── train.sh
├── inference.sh
├── preprocess.py
├── postprocess.py
├── extract.py
├── install.sh
├── mmaction2
|   ├── ...
|   |
├── mmdetection
|   ├── ...
├── data
|   ├── train.csv                         (original)
|   ├── sample_submission.csv             (original)
|   ├── train_list_rawframes_fold0.txt    (Preprocessed file for classifier training)
|   ├── train_list_rawframes_fold0_tw.txt (Preprocessed file for classifier training)
|   ├── val_list_rawframes_fold0.txt      (Preprocessed file for classifier validation)
|   ├── test_list_rawframes.txt           (Preprocessed file for classifier testing)
|   ├── train_detect_fold3.json           (Preprocessed file for detector training)
|   ├── val_detect_fold3.json             (Preprocessed file for detector validation)
|   ├── test.json                         (Preprocessed file for detector testing)
|   ├── results
|   |   ├── pretrained
|   |   |   ├── best_model.pth (model 1 / classifier)
|   |   |   ├── best_over_model.pth (model 2 / classifier)
|   |   |   ├── det.pth (model 3 / detector)
|   |   ├── results
|   |   |   ├── model1.pkl (model 1 output results/ classifier)
|   |   |   ├── model2.pkl (model 2 output results/ classifier)
|   |   |   ├── det_results.pkl (model 3 output results/ detector)
|   |   |   ├── leaderboard_final.csv (Our final leaderboard file)
|   |   |   ├── final.csv (Our reproduce file)
├── img
|   ├── summary.jpg
|   ├── leaderboard.JPG

​```

Environments Settings

• CUDA version >= 11.1

• Pytorch version >= 1.8

• Ubuntu 18.04

• I trained the model using 4 Gpus (Rtx3090).

# create environment
$ conda create -n aiconnect python=3.9 -y
$ conda activate aiconnect

# install pytorch
$ conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge

# install mmcv
$ pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html

# install requirements
~/aiconnect_action$ bash install.sh

Data Preparation

# mp4를 제 폴더에 옮길 필요없이 server에 있는 mp4 folder path만 지정해주시면 됩니다.

# Extract train video frames
~/aiconnect_action$ python extract.py --dir={competition_train_mp4_folder_dir_path} --output-root=./data/train_raw_frame

# Extract test video frames
~/aiconnect_action$ python extract.py --dir={competition_test_mp4_folder_dir_path} --output-root=./data/test_raw_frame

# Generate train files for mmaction & mmdetection
~/aiconnect_action$ python preprocess.py

Inference & Post-processing & Generate final submission file

~/aiconnect_action$ bash inference.sh {number_of_gpu_to_use} # bash inference.sh 4

Training Script

• I chose the best model1 & model2 & model3 models.

# train model1(classifier) & model2(classifier) & model3(detector)
~/aiconnect_action$ bash train.sh {number_of_gpu_to_use} # bash inference.sh 4

# or
cd mmaction2

# train model1
~/aiconnect_action/mmaction2$ bash tools/dist_train.sh configs/recognition/tsm/super_tsm_r50_modify.py 4 --work-dir data/results/model1 --validate --seed 0 --deterministic

# train model2
~/aiconnect_action/mmaction2$ bash tools/dist_train.sh configs/recognition/tsm/super_tsm_r50_modify_oversampling.py 4 --work-dir data/results/model2 --validate --seed 0 --deterministic

cd ..
cd mmdetection
# train model3
~/aiconnect_action/mmdetection$ bash tools/dist_train.sh configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py 4 --work-dir data/results/model3 --seed 0 --deterministic

Result

	실험	Public
v1	Baseline (TSM-ResNet50)(Aug X)	0.298
v2	+ Heavy Aug	0.413
v3	+ Umbrella Processing	0.466
v4	+ Oversampling ensemble	0.486
v5	+ Naive Postprocessing	0.490
v6	+ Detection Model	0.559

LeaderBoard

aiconnect_action_recognition's Issues

Welcome update to OpenMMLab 2.0

Welcome update to OpenMMLab 2.0

I am Vansin, the technical operator of OpenMMLab. In September of last year, we announced the release of OpenMMLab 2.0 at the World Artificial Intelligence Conference in Shanghai. We invite you to upgrade your algorithm library to OpenMMLab 2.0 using MMEngine, which can be used for both research and commercial purposes. If you have any questions, please feel free to join us on the OpenMMLab Discord at https://discord.gg/A9dCpjHPfE or add me on WeChat (ID: van-sin) and I will invite you to the OpenMMLab WeChat group.

Here are the OpenMMLab 2.0 repos branches:

	OpenMMLab 1.0 branch	OpenMMLab 2.0 branch
MMEngine		0.x
MMCV	1.x	2.x
MMDetection	0.x 、1.x、2.x	3.x
MMAction2	0.x	1.x
MMClassification	0.x	1.x
MMSegmentation	0.x	1.x
MMDetection3D	0.x	1.x
MMEditing	0.x	1.x
MMPose	0.x	1.x
MMDeploy	0.x	1.x
MMTracking	0.x	1.x
MMOCR	0.x	1.x
MMRazor	0.x	1.x
MMSelfSup	0.x	1.x
MMRotate	1.x	1.x
MMYOLO		0.x

Attention: please create a new virtual environment for OpenMMLab 2.0.