CVWC2019-Amur-Tiger-Re-ID

Example result of Rank-7 .

🏆Code for 1st Place Soluition in both the Tiger Re-ID in the plain track and Tiger Re-ID in the wild trackCVWC2019 @ICCV19 Workshop.

🔖Paper: Part-Pose Guided Amur Tiger Re-Identification

---

🏃 Getting Started

1️⃣ Clone the repo:

git clone https://github.com/LcenArthas/CWCV2019-Amur-Tiger-Re-ID.git

2️⃣ Dependencies

Tested under python3.6 Ubantu16.04

• python packages
  • pytorch=1.0.1
  • torchvision==0.2.1
  • pytorch-ignite=0.1.2 (Note: V0.2.0 may result in an error)
  • yacs==0.1.6
  • tensorboardx
  • h5py==2.9.0
  • imgaug==0.2.9
  • matplotlib==3.1.0
  • numpy==1.16.4
  • opencv==4.1.0.15
  • pillow==6.0.0
  • scikit-image==0.15.0
  • scipy==1.3.0
  • tensorboardx==1.6
  • tqdm==4.32.1
  • yacs==0.1.6

---

👉 Section1 The Tiger Plain Re-ID:

🏃 Train

1️⃣ Data Prearation

• Train Dataset

🔸 Download the train dataset and put them(atrw_reid_train, atrw_anno_reid_train) into the {repo_root}/process_data/.

🔸 Transform the data style for the model

python data_process.py

2️⃣ Pre-trained weight

🔸 Creat a new folder named /pretrained_model/ under the {repo_root}/:

cd data
mkdir pretrained_model

🔸 Download the pre-trained weighte and put them into the {repo_root}/pretrained_model/.

• ResNet50

• ResNet101

• ResNet18

• ResNet34

• ResNet152

• SEResNet50

• SEResNet101

• ResNeXt50

• ResNeXt101

• SEResNeXt50

• SEResNeXt101

And make sure the repo files as the following structure:

{repo_root}
 ├── config
 ├── configs
 ├── data
 |   ├── AmurTiger
 │   │   ├── flod0
 │   │   └── flod1
 │   │   ├── flod2
 │   │   └── flod3
 │   ├── datasets
 │   ├── samplers
 │   └── ...
 ├── engine
 ├── layers
 ├── modeling
 ├── pre_data
 ├── pretrained_model
 ├── solver
 ├── tests
 ├── utils
 ├── check_result.py
 ├── data_process.py
 ├── medo.py
 ├── medo_wide.py
 ├── test.py
 └── train.py

👏 Train Now!

In this competition, I use 4-fold to train and ues the Resnet152 for the backbone.

python train.py --config_file ./configs/tiger_b_resnet152_34.yml --index_flod 0

python train.py --config_file ./configs/tiger_b_resnet152_34.yml --index_flod 1

python train.py --config_file ./configs/tiger_b_resnet152_34.yml --index_flod 2

python train.py --config_file ./configs/tiger_b_resnet152_34.yml --index_flod 3

Eventually the trained model will be saved in {repo_root}/tiger_out/b_resnet152_34/

---

🏃 Inference

1️⃣ Data Preparation

🔸 Creat a new folder named /reid_test/ under the {repo_root}/data/AmurTiger/:

cd data
cd AmurTiger
mkdir reid_test

🔸 Put the test images in the {repo_root}/data/AmurTiger/reid_test/.

2️⃣ Download Pretrained Model

The trained weights are following:

• Trained weight

Download it and create a new folder under the {repo_root} named /trained_weight/

mkdir trained_weight

Unzip the model.zip(there will be 8 trained weights) and put them in the {repo_root}/trained_weight/.

And make sure the repo files as the following structure:

  {repo_root}
  ├── config
  ├── configs
  ├── data
  |   ├── AmurTiger
  │   │   ├── flod0
  │   │   └── reid_test
  │   │       ├── 000000.jpg
  │   │       ├── 000004.jpg
  │   │       ├── 000005.jpg
  │   │       ├── 000006.jpg
  │   │       ├── 000008.jpg
  │   │       └── ...
  │   ├── datasets
  │   ├── samplers
  │   └── ...
  ├── engine
  ├── layers
  ├── modeling
  ├── solver
  ├── tests
  ├── trained_weight
  │   ├── resnet101-bsize_model_100.pth
  │   ├── resnet101-bsize_model_300.pth       
  │   ├── resnet101-bsize_model_301.pth
  │   ├── resnet101-bsize_model_400.pth
  │   └──...
  ├── utils
  ├── check_result.py
  ├── medo.py
  ├── medo_wide.py
  ├── test.py
  └── train.py
      

👏 Inference Now!

python demo.py

This process will take about 6 minutes, just a moment, please.

It will generate a submission in the {repo_root/}:

• submission_plain.json —-you can submit to the Tiger Plain Re-ID track.

---

👉 Section2  The Tiger Wild Re-ID

🏃 Train

Same thing up here

🏃 Inference

In this task, it's a two-step process: Detection and Re-id

1️⃣ Detection

🔸 Please follow this repo: CWCV2019-Amur-Tiger-Detection

Note that the two repos depend on different environments(Re-ID is pytorch==1.0.1, Detection is pytorch==0.4.1)

🔸 Run scrip in above repo will generate 3 files in the {repo_root/}:

• det_submission.json

• wide_box.json

• reid_test(a folder) --it contains images that have been detected and croped.

This wide_box.json and reid_test(a folder) are what we need next.

2️⃣ Re-ID

Use this reop.

🔸 Data Preparation

Put wide_box.json and reid_test(a folder)(created by the detector above) under the {repo_root}/data/AmurTiger/.

🔸 Download Pretrained Model(Same as the plain re-id)

The trained weight is following:

• Trained weight

Download it and create a new folder under the {repo_root} named /trained_weight/

mkdir trained_weight

Unzip the model.zip and put them into the {repo_root}/trained_weight/.

And make sure the repo files as the following structure:

 {repo_root}
 ├── config
 ├── configs
 ├── data
 |   ├── AmurTiger
 │   │   ├── flod0
 │   │   ├── wide_box.json
 │   │   └── reid_test
 │   │       ├── 000000.jpg
 │   │       ├── 000004.jpg
 │   │       ├── 000005.jpg
 │   │       ├── 000006.jpg
 │   │       ├── 000008.jpg
 │   │       └── ...
 │   ├── datasets
 │   ├── samplers
 │   └── ...
 ├── engine
 ├── layers
 ├── modeling
 ├── solver
 ├── tests
 ├── trained_weight
 │   ├── best_model.pth
 │   ├── resnet101-bsize_model_100.pth
 │   ├── resnet101-bsize_model_300.pth       
 │   ├── resnet101-bsize_model_301.pth
 │   ├── resnet101-bsize_model_400.pth
 │   └──...
 ├── utils
 ├── check_result.py
 ├── medo.py
 ├── medo_wide.py
 ├── test.py
 └── train.py
     

👏 Inference Now!

python demo_wide.py

This process will take about 15 minutes, just a moment, please.

It will generate a submission in the {repo_root/}:

• submission_wide.json —-you can submit to the Tiger Wide Re-ID track.