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CHANGELOG.md 0 → 100644
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# 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 0 → 100644
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# 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 0 → 100644
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# 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 0 → 100644
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MODEL_ZOO.md 0 → 100644
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# 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 0 → 100644
View file @ b6c19984
<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 0 → 100644
View file @ b6c19984
# 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 0 → 100644
View file @ b6c19984
_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 0 → 100644
View file @ b6c19984
_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 0 → 100644
View file @ b6c19984
_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 0 → 100644
View file @ b6c19984
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 0 → 100644
View file @ b6c19984
_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 0 → 100644
View file @ b6c19984
_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 0 → 100644
View file @ b6c19984
_BASE_: ../Base-AGW.yml
DATASETS:
NAMES: ("DukeMTMC",)
TESTS: ("DukeMTMC",)
OUTPUT_DIR: logs/dukemtmc/agw_R50
configs/DukeMTMC/AGW_S50.yml 0 → 100644
View file @ b6c19984
_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 0 → 100644
View file @ b6c19984
_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 0 → 100644
View file @ b6c19984
_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 0 → 100644
View file @ b6c19984
_BASE_: ../Base-bagtricks.yml
DATASETS:
NAMES: ("DukeMTMC",)
TESTS: ("DukeMTMC",)
OUTPUT_DIR: logs/dukemtmc/bagtricks_R50
configs/DukeMTMC/bagtricks_S50.yml 0 → 100644
View file @ b6c19984
_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 0 → 100644
View file @ b6c19984
_BASE_: ../Base-MGN.yml
MODEL:
BACKBONE:
WITH_IBN: True
DATASETS:
NAMES: ("DukeMTMC",)
TESTS: ("DukeMTMC",)
OUTPUT_DIR: logs/dukemtmc/mgn_R50-ibn
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