update

CHANGELOG.md

+# Changelog
+
+### v1.3
+
+#### New Features
+- Vision Transformer backbone, see config in `configs/Market1501/bagtricks_vit.yml`
+- Self-Distillation with EMA update
+- Gradient Clip
+
+#### Improvements
+- Faster dataloader with pre-fetch thread and cuda stream
+- Optimize DDP training speed by removing `find_unused_parameters` in DDP
+
+
+### v1.2 (06/04/2021)
+
+#### New Features
+
+- Multiple machine training support
+- [RepVGG](https://github.com/DingXiaoH/RepVGG) backbone 
+- [Partial FC](projects/FastFace)
+
+#### Improvements
+
+- Torch2trt pipeline 
+- Decouple linear transforms and softmax
+- config decorator
+
+### v1.1 (29/01/2021)
+
+#### New Features
+
+- NAIC20(reid track) [1-st solution](projects/NAIC20) 
+- Multi-teacher Knowledge Distillation
+- TRT network definition APIs in [FastRT](projects/FastRT)
+
+#### Bug Fixes
+
+#### Improvements
\ No newline at end of file

GETTING_STARTED.md

+# Getting Started with Fastreid
+
+## Prepare pretrained model
+
+If you use backbones supported by fastreid, you do not need to do anything. It will automatically download the pre-train models.
+But if your network is not connected, you can download pre-train models manually and put it in `~/.cache/torch/checkpoints`.
+
+If you want to use other pre-train models, such as MoCo pre-train, you can download by yourself and set the pre-train model path in `configs/Base-bagtricks.yml`.
+
+## Compile with cython to accelerate evalution
+
+```bash
+cd fastreid/evaluation/rank_cylib; make all
+```
+
+## Training & Evaluation in Command Line
+
+We provide a script in "tools/train_net.py", that is made to train all the configs provided in fastreid.
+You may want to use it as a reference to write your own training script.
+
+To train a model with "train_net.py", first setup up the corresponding datasets following [datasets/README.md](https://github.com/JDAI-CV/fast-reid/tree/master/datasets), then run:
+
+```bash
+python3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml MODEL.DEVICE "cuda:0"
+```
+
+The configs are made for 1-GPU training.
+
+If you want to train model with 4 GPUs, you can run:
+
+```bash
+python3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --num-gpus 4
+```
+
+If you want to train model with multiple machines, you can run:
+
+```
+# machine 1
+export GLOO_SOCKET_IFNAME=eth0
+export NCCL_SOCKET_IFNAME=eth0
+
+python3 tools/train_net.py --config-file configs/Market1501/bagtricks_R50.yml \
+--num-gpus 4 --num-machines 2 --machine-rank 0 --dist-url tcp://ip:port 
+
+# machine 2
+export GLOO_SOCKET_IFNAME=eth0
+export NCCL_SOCKET_IFNAME=eth0
+
+python3 tools/train_net.py --config-file configs/Market1501/bagtricks_R50.yml \
+--num-gpus 4 --num-machines 2 --machine-rank 1 --dist-url tcp://ip:port 
+```
+
+Make sure the dataset path and code are the same in different machines, and machines can communicate with each other. 
+
+To evaluate a model's performance, use
+
+```bash
+python3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --eval-only \
+MODEL.WEIGHTS /path/to/checkpoint_file MODEL.DEVICE "cuda:0"
+```
+
+For more options, see `python3 tools/train_net.py -h`.

INSTALL.md

+# Installation
+
+## Requirements
+
+- Linux or macOS with python ≥ 3.6
+- PyTorch ≥ 1.6
+- torchvision that matches the Pytorch installation. You can install them together at [pytorch.org](https://pytorch.org/) to make sure of this.
+- [yacs](https://github.com/rbgirshick/yacs)
+- Cython (optional to compile evaluation code)
+- tensorboard (needed for visualization): `pip install tensorboard`
+- gdown (for automatically downloading pre-train model)
+- sklearn
+- termcolor
+- tabulate
+- [faiss](https://github.com/facebookresearch/faiss) `pip install faiss-cpu`
+
+
+
+# Set up with Conda
+```shell script
+conda create -n fastreid python=3.7
+conda activate fastreid
+conda install pytorch==1.6.0 torchvision tensorboard -c pytorch
+pip install -r docs/requirements.txt
+```
+
+# Set up with Dockder
+
+Please check the [docker folder](docker)

LICENSE

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MODEL_ZOO.md

+# FastReID Model Zoo and Baselines
+
+## Introduction
+
+This file documents collection of baselines trained with fastreid. All numbers were obtained with 1 NVIDIA V100 GPU.
+The software in use were PyTorch 1.6, CUDA 10.1.
+
+In addition to these official baseline models, you can find more models in [projects/](https://github.com/JDAI-CV/fast-reid/tree/master/projects).
+
+### How to Read the Tables
+
+- The "Name" column contains a link to the config file.
+Running `tools/train_net.py` with this config file and 1 GPU will reproduce the model.
+
+### Common Settings for all Person reid models
+
+**BoT**:
+
+[Bag of Tricks and A Strong Baseline for Deep Person Re-identification](http://openaccess.thecvf.com/content_CVPRW_2019/papers/TRMTMCT/Luo_Bag_of_Tricks_and_a_Strong_Baseline_for_Deep_Person_CVPRW_2019_paper.pdf). CVPRW2019, Oral.
+
+**AGW**:
+
+[ReID-Survey with a Powerful AGW Baseline](https://github.com/mangye16/ReID-Survey).
+
+**MGN**:
+
+[Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://arxiv.org/abs/1804.01438v1)
+
+**SBS**:
+
+stronger baseline on top of BoT:
+
+Bag of Freebies(BoF):
+
+1. Circle loss
+2. Freeze backbone training
+3. Cutout data augmentation & Auto Augmentation
+4. Cosine annealing learning rate decay
+5. Soft margin triplet loss
+
+Bag of Specials(BoS):
+
+1. Non-local block
+2. GeM pooling
+
+### Market1501 Baselines
+
+**BoT**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---: |
+| [BoT(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_R50.yml) | ImageNet | 94.4% | 86.1% | 59.4% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_R50.pth) |
+| [BoT(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_R50-ibn.yml) | ImageNet | 94.9% | 87.6% | 64.1% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_R50-ibn.pth) |
+| [BoT(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_S50.yml) | ImageNet | 95.2% | 88.7% | 66.9% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_S50.pth) |
+| [BoT(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/bagtricks_R101-ibn.yml) | ImageNet| 95.4% | 88.9% | 67.4% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_bot_R101-ibn.pth) |
+
+**AGW**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: |:---: |
+| [AGW(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R50.yml) | ImageNet | 95.3% | 88.2% | 66.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R50.pth) |
+| [AGW(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R50-ibn.yml) | ImageNet | 95.1% | 88.7% | 67.1% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R50-ibn.pth) |
+| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_S50.yml) | ImageNet | 95.3% | 89.3% | 68.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_S50.pth) |
+| [AGW(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/AGW_R101-ibn.yml) | ImageNet | 95.5% | 89.5% | 69.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_agw_R101-ibn.pth) |
+
+**SBS**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: |:---:|
+| [SBS(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_R50.yml) | ImageNet | 95.4% | 88.2% | 64.8% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_R50.pth) |
+| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_R50-ibn.yml) | ImageNet | 95.7% | 89.3% | 67.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_R50-ibn.pth) |
+| [SBS(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_S50.yml) | ImageNet | 95.8% | 89.4% | 67.6% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_S50.pth) |
+| [SBS(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/sbs_R101-ibn.yml) | ImageNet | 96.3% | 90.3% | 70.0% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_sbs_R101-ibn.pth) |
+
+**MGN**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---:|
+| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/Market1501/mgn_R50-ibn.yml) | ImageNet | 95.8% | 89.8% | 67.7% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/market_mgn_R50-ibn.pth) |
+
+### DukeMTMC Baseline
+
+**BoT**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---: |
+| [BoT(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R50.yml) | ImageNet | 87.2% | 77.0% | 42.1% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_R50.pth) |
+| [BoT(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R50-ibn.yml) | ImageNet | 89.3% | 79.6% | 45.2% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_R50-ibn.pth) |
+| [BoT(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_S50.yml) | ImageNet | 90.0% | 80.13% | 45.8% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_S50.pth) |
+| [BoT(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/bagtricks_R101-ibn.yml) | ImageNet| 91.2% | 81.2% | 47.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_bot_R101-ibn.pth) |
+
+**AGW**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---:|
+| [AGW(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R50.yml) | ImageNet | 89.0% | 79.9% | 46.1% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_R50.pth) |
+| [AGW(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R50-ibn.yml) | ImageNet | 90.5% | 80.8% | 47.6% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_R50-ibn.pth) |
+| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_S50.yml) | ImageNet | 90.9% | 82.4% | 49.2% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_S50.pth) |
+| [AGW(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/AGW_R101-ibn.yml) | ImageNet | 91.7% | 82.3% | 50.0% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_agw_R101-ibn.pth) |
+
+**SBS**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---:|
+| [SBS(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R50.yml) | ImageNet | 90.3% | 80.3% | 46.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_R50.pth) |
+| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R50-ibn.yml) | ImageNet | 90.8% | 81.2% | 47.0% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_R50-ibn.pth) |
+| [SBS(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_S50.yml) | ImageNet | 91.0% | 81.4% | 47.6% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_S50.pth) |
+| [SBS(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/sbs_R101-ibn.yml) | ImageNet | 91.9% | 83.6% | 51.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_sbs_R101-ibn.pth) |
+
+**MGN**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---:|
+| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/DukeMTMC/mgn_R50-ibn.yml) | ImageNet | 91.1% | 82.0% | 46.8% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/duke_mgn_R50-ibn.pth) |
+
+### MSMT17 Baseline
+
+**BoT**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---:|
+| [BoT(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R50.yml) | ImageNet | 74.1%  | 50.2% | 10.4% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_R50.pth) |
+| [BoT(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R50-ibn.yml) | ImageNet | 77.0% | 54.4% | 12.5% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_R50-ibn.pth) |
+| [BoT(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_S50.yml) | ImageNet | 80.8% | 59.9% | 16.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_S50.pth) |
+| [BoT(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/bagtricks_R101-ibn.yml) | ImageNet| 81.0% | 59.4% | 15.6% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_bot_R101-ibn.pth) |
+
+**AGW**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---:|
+| [AGW(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R50.yml) | ImageNet | 78.3% | 55.6% | 12.9% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_R50.pth) |
+| [AGW(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R50-ibn.yml) | ImageNet | 81.2% | 59.7% | 15.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_R50-ibn.pth) |
+| [AGW(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_S50.yml) | ImageNet | 82.6% | 62.6% | 17.7% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_S50.pth) |
+| [AGW(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/AGW_R101-ibn.yml) | ImageNet | 82.0% | 61.4% | 17.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_agw_R101-ibn.pth) |
+
+**SBS**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---:|
+| [SBS(R50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R50.yml) | ImageNet | 81.8% | 58.4% | 13.9% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_R50.pth) |
+| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R50-ibn.yml) | ImageNet | 83.9% | 60.6% | 15.2% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_R50-ibn.pth) |
+| [SBS(S50)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_S50.yml) | ImageNet | 84.1% | 61.7% | 15.2% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_S50.pth) |
+| [SBS(R101-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/sbs_R101-ibn.yml) | ImageNet | 84.8% | 62.8% | 16.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/msmt_sbs_R101-ibn.pth) |
+
+**MGN**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---:|
+| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/MSMT17/mgn_R50-ibn.yml) | ImageNet | 85.1% | 65.4% | 18.4% | - |
+
+### VeRi Baseline
+
+**SBS**:
+
+| Method | Pretrained | Rank@1 | mAP | mINP | download |
+| :---: | :---: | :---: |:---: | :---: | :---:| 
+| [SBS(R50-ibn)](https://github.com/JDAI-CV/fast-reid/blob/master/configs/VeRi/sbs_R50-ibn.yml) | ImageNet | 97.0%  | 81.9% | 46.3% | [model](https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/veri_sbs_R50-ibn.pth) |
+
+### VehicleID Baseline
+
+**BoT**:  
+Test protocol: 10-fold cross-validation; trained on 4 NVIDIA P40 GPU.
+
+<table>
+<thead>
+  <tr>
+    <th rowspan="3" align="center">Method</th>
+    <th rowspan="3" align="center">Pretrained</th>
+    <th colspan="6" align="center">Testset size</th>
+    <th rowspan="3" align="center">download</th>
+  </tr>
+  <tr>
+    <td colspan="2" align="center">Small</td>
+    <td colspan="2" align="center">Medium</td>
+    <td colspan="2" align="center">Large</td>
+  </tr>
+  <tr>
+    <td align="center">Rank@1</td>
+    <td align="center">Rank@5</td>
+    <td align="center">Rank@1</td>
+    <td align="center">Rank@5</td>
+    <td align="center">Rank@1</td>
+    <td align="center">Rank@5</td>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td nowrap align="center"><a href="https://github.com/JDAI-CV/fast-reid/blob/master/configs/VehicleID/bagtricks_R50-ibn.yml">BoT(R50-ibn)</a></td>
+    <td align="center">ImageNet</td>
+    <td align="center">86.6%</td>
+    <td align="center">97.9%</td>
+    <td align="center">82.9%</td>
+    <td align="center">96.0%</td>
+    <td align="center">80.6%</td>
+    <td align="center">93.9%</td>
+    <td align="center"><a href="https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/vehicleid_bot_R50-ibn.pth">model</a></td>
+  </tr>
+</tbody>
+</table>
+
+### VERI-Wild Baseline
+
+**BoT**:  
+Test protocol: Trained on 4 NVIDIA P40 GPU.
+
+<table>
+<thead>
+  <tr>
+    <th rowspan="3" align="center"> Method</th>
+    <th rowspan="3" align="center">Pretrained</th>
+    <th colspan="9" align="center">Testset size</th>
+    <th rowspan="3" align="center">download</th>
+  </tr>
+  <tr>
+    <td colspan="3" align="center">Small</td>
+    <td colspan="3" align="center">Medium</td>
+    <td colspan="3" align="center">Large</td>
+  </tr>
+  <tr>
+    <td align="center">Rank@1</td>
+    <td align="center">mAP</td>
+    <td align="center">mINP</td>
+    <td align="center">Rank@1</td>
+    <td align="center">mAP</td>
+    <td align="center">mINP</td>
+    <td align="center">Rank@1</td>
+    <td align="center">mAP</td>
+    <td align="center">mINP</td>
+  </tr>
+</thead>
+<tbody>
+  <tr>
+    <td nowrap align="center"><a href="https://github.com/JDAI-CV/fast-reid/blob/master/configs/VERIWild/bagtricks_R50-ibn.yml">BoT(R50-ibn)</a></td>
+    <td align="center">ImageNet</td>
+    <td align="center">96.4%</td>
+    <td align="center">87.7%</td>
+    <td align="center">69.2%</td>
+    <td align="center">95.1%</td>
+    <td align="center">83.5%</td>
+    <td align="center">61.2%</td>
+    <td align="center">92.5%</td>
+    <td align="center">77.3%</td>
+    <td align="center">49.8%</td>
+    <td align="center"><a href="https://github.com/JDAI-CV/fast-reid/releases/download/v0.1.1/veriwild_bot_R50-ibn.pth">model</a></td>
+  </tr>
+</tbody>
+</table>

README copy.md

+<img src=".github/FastReID-Logo.png" width="300" >
+
+[![Gitter](https://badges.gitter.im/fast-reid/community.svg)](https://gitter.im/fast-reid/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+
+Gitter: [fast-reid/community](https://gitter.im/fast-reid/community?utm_source=share-link&utm_medium=link&utm_campaign=share-link)
+
+Wechat: 
+
+<img src=".github/wechat_group.png" width="150" >
+
+
+FastReID is a research platform that implements state-of-the-art re-identification algorithms. It is a ground-up rewrite of the previous version, [reid strong baseline](https://github.com/michuanhaohao/reid-strong-baseline).
+
+## What's New
+
+- [Sep 2021] [DG-ReID](https://github.com/xiaomingzhid/sskd) is updated, you can check the [paper](https://arxiv.org/pdf/2108.05045.pdf).
+- [June 2021] [Contiguous parameters](https://github.com/PhilJd/contiguous_pytorch_params) is supported, now it can
+  accelerate ~20%.
+- [May 2021] Vision Transformer backbone supported, see `configs/Market1501/bagtricks_vit.yml`.
+- [Apr 2021] Partial FC supported in [FastFace](projects/FastFace)!
+- [Jan 2021] TRT network definition APIs in [FastRT](projects/FastRT) has been released! 
+Thanks for [Darren](https://github.com/TCHeish)'s contribution.
+- [Jan 2021] NAIC20(reid track) [1-st solution](projects/NAIC20) based on fastreid has been released！
+- [Jan 2021] FastReID V1.0 has been released！🎉
+  Support many tasks beyond reid, such image retrieval and face recognition. See [release notes](https://github.com/JDAI-CV/fast-reid/releases/tag/v1.0.0).
+- [Oct 2020] Added the [Hyper-Parameter Optimization](projects/FastTune) based on fastreid. See `projects/FastTune`.
+- [Sep 2020] Added the [person attribute recognition](projects/FastAttr) based on fastreid. See `projects/FastAttr`.
+- [Sep 2020] Automatic Mixed Precision training is supported with `apex`. Set `cfg.SOLVER.FP16_ENABLED=True` to switch it on.
+- [Aug 2020] [Model Distillation](projects/FastDistill) is supported, thanks for [guan'an wang](https://github.com/wangguanan)'s contribution.
+- [Aug 2020] ONNX/TensorRT converter is supported.
+- [Jul 2020] Distributed training with multiple GPUs, it trains much faster.
+- Includes more features such as circle loss, abundant visualization methods and evaluation metrics, SoTA results on conventional, cross-domain, partial and vehicle re-id, testing on multi-datasets simultaneously, etc.
+- Can be used as a library to support [different projects](projects) on top of it. We'll open source more research projects in this way.
+- Remove [ignite](https://github.com/pytorch/ignite)(a high-level library) dependency and powered by [PyTorch](https://pytorch.org/).
+
+We write a [fastreid intro](https://l1aoxingyu.github.io/blogpages/reid/fastreid/2020/05/29/fastreid.html) 
+and [fastreid v1.0](https://l1aoxingyu.github.io/blogpages/reid/fastreid/2021/04/28/fastreid-v1.html) about this toolbox.
+
+## Changelog
+
+Please refer to [changelog.md](CHANGELOG.md) for details and release history.
+
+## Installation
+
+See [INSTALL.md](INSTALL.md).
+
+## Quick Start
+
+The designed architecture follows this guide [PyTorch-Project-Template](https://github.com/L1aoXingyu/PyTorch-Project-Template), you can check each folder's purpose by yourself.
+
+See [GETTING_STARTED.md](GETTING_STARTED.md).
+
+Learn more at out [documentation](https://fast-reid.readthedocs.io/). And see [projects/](projects) for some projects that are build on top of fastreid.
+
+## Model Zoo and Baselines
+
+We provide a large set of baseline results and trained models available for download in the [Fastreid Model Zoo](MODEL_ZOO.md).
+
+## Deployment
+
+We provide some examples and scripts to convert fastreid model to Caffe, ONNX and TensorRT format in [Fastreid deploy](tools/deploy).
+
+## License
+
+Fastreid is released under the [Apache 2.0 license](LICENSE).
+
+## Citing FastReID
+
+If you use FastReID in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.
+
+```BibTeX
+@article{he2020fastreid,
+  title={FastReID: A Pytorch Toolbox for General Instance Re-identification},
+  author={He, Lingxiao and Liao, Xingyu and Liu, Wu and Liu, Xinchen and Cheng, Peng and Mei, Tao},
+  journal={arXiv preprint arXiv:2006.02631},
+  year={2020}
+}
+```

README.md

+# MiniMax-M2
+## 论文
+暂无
+
+## 模型简介
+FastReID 是一个研究平台，实现了最先进的重识别算法。它是对先前版本reid strong baseline的彻底重写
+- 核心功能**
+- **任务扩展**：从最初的ReID扩展到图像分类（FastCls）、属性识别（FastAttr）、人脸识别（FastFace）和细粒度图像检索（FastRetri）等任务 。 ‌
+- **模型优化**：提供模型蒸馏、自动超参搜索、分布式训练加速等功能，支持模型导出（如Caffe/ONNX/TensorRT） 。 ‌
+- **技术亮点**
+- **蒸馏方法**：采用JS Div Loss优化蒸馏过程，并集成Overhaul Distillation提升性能 。 ‌
+- **超参搜索**：集成Ray Tune实现Bayesian优化，支持分布式并行实验 
+
+
+## 环境依赖
+
+| 软件 | 版本 |
+| :------: | :------: |
+| DTK | 25.04.2 |
+| python | 3.10.12 |
+| transformers | 4.57.1 |
+| torch | 2.5.1+das.opt1.dtk25042 |
+| triton | 3.1+das.opt1.3c5d12d.dtk25041 |
+| flash_attn | 2.6.1+das.opt1.dtk2504 |
+| flash_mla | 1.0.0+das.opt1.dtk25042 |
+
+当前仅支持镜像:
+- 挂载地址`-v`根据实际模型情况修改
+
+```bash
+docker run -it --shm-size 60g --network=host --name minimax_m2 --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /opt/hyhal/:/opt/hyhal/:ro -v /path/your_code_path/:/path/your_code_path/ image.sourcefind.cn:5000/dcu/admin/base/custom:vllm-ubuntu22.04-dtk25.04.2-py3.10-minimax-m2 bash
+```
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
+
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
+
+## 数据集
+Market1501:[百度云盘](https://pan.baidu.com/s/1ntIi2Op)
+```
+datasets/
+    Market-1501-v15.09.15/
+        bounding_box_test/
+        bounding_box_train/
+```
+DukeMTMC-ReID:
+```
+datasets/
+    DukeMTMC-reID/
+        bounding_box_train/
+        bounding_box_test/
+```
+MSMT17:
+```
+datasets/
+    MSMT17_V2/
+        mask_train_v2/
+        mask_test_v2/
+```
+## 训练
+
+### 单卡训练
+```bash
+python3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml MODEL.DEVICE "cuda:0"
+```
+### 多卡训练
+```bash
+# machine 1
+export GLOO_SOCKET_IFNAME=eth0
+export NCCL_SOCKET_IFNAME=eth0
+
+python3 tools/train_net.py --config-file configs/Market1501/bagtricks_R50.yml \
+--num-gpus 4 --num-machines 2 --machine-rank 0 --dist-url tcp://ip:port 
+
+# machine 2
+export GLOO_SOCKET_IFNAME=eth0
+export NCCL_SOCKET_IFNAME=eth0
+
+python3 tools/train_net.py --config-file configs/Market1501/bagtricks_R50.yml \
+--num-gpus 4 --num-machines 2 --machine-rank 1 --dist-url tcp://ip:port 
+```
+
+
+## 推理
+### 推理验证
+```bash
+python3 tools/train_net.py --config-file ./configs/Market1501/bagtricks_R50.yml --eval-only \
+MODEL.WEIGHTS /path/to/checkpoint_file MODEL.DEVICE "cuda:0"
+``` 
+
+
+## 效果展示
+暂无
+
+### 精度
+DCU与GPU精度一致。
+
+## 预训练权重
+| reid模型  | 权重大小  | DCU型号  | 最低卡数需求 |下载地址|
+|:-----:|:----------:|:----------:|:---------------------:|:----------:|
+| AGW | 287MB | K100AI | 1 | https://github.com/JDAI-CV/fast-reid/blob/master/MODEL_ZOO.md
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/fast-reid_pytorch
+
+## 参考资料
+- https://github.com/JDAI-CV/fast-reid/tree/master

configs/Base-AGW.yml

+_BASE_: Base-bagtricks.yml
+
+MODEL:
+  BACKBONE:
+    WITH_NL: True
+
+  HEADS:
+    POOL_LAYER: GeneralizedMeanPooling
+
+  LOSSES:
+    NAME: ("CrossEntropyLoss", "TripletLoss")
+    CE:
+      EPSILON: 0.1
+      SCALE: 1.0
+
+    TRI:
+      MARGIN: 0.0
+      HARD_MINING: False
+      SCALE: 1.0

configs/Base-MGN.yml

+_BASE_: Base-SBS.yml
+
+MODEL:
+  META_ARCHITECTURE: MGN
+
+  FREEZE_LAYERS: [backbone, b1, b2, b3,]
+
+  BACKBONE:
+    WITH_NL: False
+
+  HEADS:
+    EMBEDDING_DIM: 256

configs/Base-SBS.yml

+_BASE_: Base-bagtricks.yml
+
+MODEL:
+  FREEZE_LAYERS: [ backbone ]
+
+  BACKBONE:
+    WITH_NL: True
+
+  HEADS:
+    NECK_FEAT: after
+    POOL_LAYER: GeneralizedMeanPoolingP
+    CLS_LAYER: CircleSoftmax
+    SCALE: 64
+    MARGIN: 0.35
+
+  LOSSES:
+    NAME: ("CrossEntropyLoss", "TripletLoss",)
+    CE:
+      EPSILON: 0.1
+      SCALE: 1.0
+
+    TRI:
+      MARGIN: 0.0
+      HARD_MINING: True
+      NORM_FEAT: False
+      SCALE: 1.0
+
+INPUT:
+  SIZE_TRAIN: [ 384, 128 ]
+  SIZE_TEST: [ 384, 128 ]
+
+  AUTOAUG:
+    ENABLED: True
+    PROB: 0.1
+
+DATALOADER:
+  NUM_INSTANCE: 16
+
+SOLVER:
+  AMP:
+    ENABLED: True
+  OPT: Adam
+  MAX_EPOCH: 60
+  BASE_LR: 0.00035
+  WEIGHT_DECAY: 0.0005
+  IMS_PER_BATCH: 64
+
+  SCHED: CosineAnnealingLR
+  DELAY_EPOCHS: 30
+  ETA_MIN_LR: 0.0000007
+
+  WARMUP_FACTOR: 0.1
+  WARMUP_ITERS: 2000
+
+  FREEZE_ITERS: 1000
+
+  CHECKPOINT_PERIOD: 20
+
+TEST:
+  EVAL_PERIOD: 10
+  IMS_PER_BATCH: 128
+
+CUDNN_BENCHMARK: True

configs/Base-bagtricks.yml

+MODEL:
+  META_ARCHITECTURE: Baseline
+
+  BACKBONE:
+    NAME: build_resnet_backbone
+    NORM: BN
+    DEPTH: 50x
+    LAST_STRIDE: 1
+    FEAT_DIM: 2048
+    WITH_IBN: False
+    PRETRAIN: True
+
+  HEADS:
+    NAME: EmbeddingHead
+    NORM: BN
+    WITH_BNNECK: True
+    POOL_LAYER: GlobalAvgPool
+    NECK_FEAT: before
+    CLS_LAYER: Linear
+
+  LOSSES:
+    NAME: ("CrossEntropyLoss", "TripletLoss",)
+
+    CE:
+      EPSILON: 0.1
+      SCALE: 1.
+
+    TRI:
+      MARGIN: 0.3
+      HARD_MINING: True
+      NORM_FEAT: False
+      SCALE: 1.
+
+INPUT:
+  SIZE_TRAIN: [ 256, 128 ]
+  SIZE_TEST: [ 256, 128 ]
+
+  REA:
+    ENABLED: True
+    PROB: 0.5
+
+  FLIP:
+    ENABLED: True
+
+  PADDING:
+    ENABLED: True
+
+DATALOADER:
+  SAMPLER_TRAIN: NaiveIdentitySampler
+  NUM_INSTANCE: 4
+  NUM_WORKERS: 8
+
+SOLVER:
+  AMP:
+    ENABLED: True
+  OPT: Adam
+  MAX_EPOCH: 120
+  BASE_LR: 0.00035
+  WEIGHT_DECAY: 0.0005
+  WEIGHT_DECAY_NORM: 0.0005
+  IMS_PER_BATCH: 64
+
+  SCHED: MultiStepLR
+  STEPS: [ 40, 90 ]
+  GAMMA: 0.1
+
+  WARMUP_FACTOR: 0.1
+  WARMUP_ITERS: 2000
+
+  CHECKPOINT_PERIOD: 30
+
+TEST:
+  EVAL_PERIOD: 30
+  IMS_PER_BATCH: 128
+
+CUDNN_BENCHMARK: True

configs/DukeMTMC/AGW_R101-ibn.yml

+_BASE_: ../Base-AGW.yml
+
+MODEL:
+  BACKBONE:
+    DEPTH: 101x
+    WITH_IBN: True
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: logs/dukemtmc/agw_R101-ibn

configs/DukeMTMC/AGW_R50-ibn.yml

+_BASE_: ../Base-AGW.yml
+
+MODEL:
+  BACKBONE:
+    WITH_IBN: True
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: logs/dukemtmc/agw_R50-ibn

configs/DukeMTMC/AGW_R50.yml

+_BASE_: ../Base-AGW.yml
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: logs/dukemtmc/agw_R50

configs/DukeMTMC/AGW_S50.yml

+_BASE_: ../Base-AGW.yml
+
+MODEL:
+  BACKBONE:
+    NAME: build_resnest_backbone
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: logs/dukemtmc/agw_S50

configs/DukeMTMC/bagtricks_R101-ibn.yml

+_BASE_: ../Base-bagtricks.yml
+
+MODEL:
+  BACKBONE:
+    DEPTH: 101x
+    WITH_IBN: True
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: logs/dukemtmc/bagtricks_R101-ibn

configs/DukeMTMC/bagtricks_R50-ibn.yml

+_BASE_: ../Base-bagtricks.yml
+
+MODEL:
+  BACKBONE:
+    WITH_IBN: True
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: logs/dukemtmc/bagtricks_R50-ibn

configs/DukeMTMC/bagtricks_R50.yml

+_BASE_: ../Base-bagtricks.yml
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: logs/dukemtmc/bagtricks_R50

configs/DukeMTMC/bagtricks_S50.yml

+_BASE_: ../Base-bagtricks.yml
+
+MODEL:
+  BACKBONE:
+    NAME: build_resnest_backbone
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: logs/dukemtmc/bagtricks_S50

configs/DukeMTMC/mgn_R50-ibn.yml

+_BASE_: ../Base-MGN.yml
+
+MODEL:
+  BACKBONE:
+    WITH_IBN: True
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: logs/dukemtmc/mgn_R50-ibn