83 lines
3.5 KiB
Markdown
83 lines
3.5 KiB
Markdown
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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](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6). This repository also provides a `resnet.py` script and its pre-training weights on Imagenet here.
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```
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# resnet18
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https://download.pytorch.org/models/resnet18-5c106cde.pth
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# resnet34
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https://download.pytorch.org/models/resnet34-333f7ec4.pth
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# resnet50
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https://download.pytorch.org/models/resnet50-19c8e357.pth
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# resnext50_32x4d
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https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth
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```
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## Dataset PrePare
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To train the model, first you need download [Market1501](http://www.liangzheng.com.cn/Project/project_reid.html) dataset or [Mars](http://www.liangzheng.com.cn/Project/project_mars.html) dataset.
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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.
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```
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├── dataset_root: The root dir of the dataset.
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├── class1: Category 1 is located in the folder dir.
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├── xxx1.jpg: Image belonging to category 1.
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├── xxx2.jpg: Image belonging to category 1.
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├── class2: Category 2 is located in the folder dir.
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├── xxx3.jpg: Image belonging to category 2.
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├── xxx4.jpg: Image belonging to category 2.
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├── class3: Category 3 is located in the folder dir.
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...
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...
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```
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## Training the RE-ID model
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Assuming you have already prepare the dataset. Then you can use the following command to start your training progress.
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#### training on a single GPU
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```python
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usage: train.py [--data-dir]
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[--epochs]
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[--batch_size]
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[--lr]
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[--lrf]
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[--weights]
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[--freeze-layers]
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[--gpu_id]
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# default use cuda:0, use Net in `model.py`
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python train.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path]
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# you can use `--freeze-layers` option to freeze full convolutional layer parameters except fc layers parameters
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python train.py --data-dir [dataset/root/path] --weights [(optional)pre-train/weight/path] --freeze-layers
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```
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#### training on multiple GPU
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```python
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usage: train_multiGPU.py [--data-dir]
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[--epochs]
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[--batch_size]
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[--lr]
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[--lrf]
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[--syncBN]
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[--weights]
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[--freeze-layers]
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# not change the following parameters, the system will automatically assignment
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[--device]
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[--world_size]
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[--dist_url]
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# default use cuda:0, cuda:1, cuda:2, cuda:3, use resnet18 in `resnet.py`
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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]
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# you can use `--freeze-layers` option to freeze full convolutional layer parameters except fc layers parameters
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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
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```
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An example of training progress is as follows:
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The last, you can evaluate it using [test.py](deep_sort/deep/test.py) and [evaluate.py](deep_sort/deep/evalute.py).
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