aicheckv2-api/deep_sort/deep/GETTING_STARTED.md

In deepsort algorithm, appearance feature extraction network used to extract features from image_crops for matching purpose.The original model used in paper is in model.py, and its parameter here ckpt.t7. This repository also provides a resnet.py script and its pre-training weights on Imagenet here.

# resnet18
https://download.pytorch.org/models/resnet18-5c106cde.pth
# resnet34    
https://download.pytorch.org/models/resnet34-333f7ec4.pth
# resnet50
https://download.pytorch.org/models/resnet50-19c8e357.pth
# resnext50_32x4d
https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth

Dataset PrePare

To train the model, first you need download Market1501 dataset or Mars dataset.

If you want to train on your own dataset, assuming you have already downloaded the dataset.The dataset should be arranged in the following way.

 ├── dataset_root: The root dir of the dataset.
  ├── class1: Category 1 is located in the folder dir.
   ├── xxx1.jpg: Image belonging to category 1.
   ├── xxx2.jpg: Image belonging to category 1.
  ├── class2: Category 2 is located in the folder dir.
   ├── xxx3.jpg: Image belonging to category 2.
   ├── xxx4.jpg: Image belonging to category 2.
  ├── class3: Category 3 is located in the folder dir.
  ...
  ...

Training the RE-ID model

Assuming you have already prepare the dataset. Then you can use the following command to start your training progress.

training on a single GPU

usage: train.py [--data-dir]
                [--epochs]
                [--batch_size]
                [--lr]
                [--lrf]
                [--weights]
                [--freeze-layers]
                [--gpu_id]

# default use cuda:0, use Net in `model.py`
python train.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path]
# you can use `--freeze-layers` option to freeze full convolutional layer parameters except fc layers parameters
python train.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] --freeze-layers

training on multiple GPU

usage: train_multiGPU.py [--data-dir]
                         [--epochs]
                         [--batch_size]
                         [--lr]
                         [--lrf]
                         [--syncBN]
                         [--weights]
                         [--freeze-layers]
                         # not change the following parameters, the system will automatically assignment
                         [--device]
                         [--world_size]
                         [--dist_url]
                         
# default use cuda:0, cuda:1, cuda:2, cuda:3, use resnet18 in `resnet.py`
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 train_multiGPU.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] 
# you can use `--freeze-layers` option to freeze full convolutional layer parameters except fc layers parameters
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 train_multiGPU.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] --freeze-layers

An example of training progress is as follows:

The last, you can evaluate it using test.py and evaluate.py.