update readme
KaiyangZhou
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README.md
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README.md
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@ -11,277 +11,65 @@ We support
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- download of trained models.
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- fast cython-based evaluation.
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## Dependencies
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- [PyTorch](http://pytorch.org/) (0.4.0)
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- [torchvision](https://github.com/pytorch/vision/) (0.2.1)
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Python2 is recommended for current version.
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## Install
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## Get Started
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1. `cd` to the folder where you want to download this repo.
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2. Run `git clone https://github.com/KaiyangZhou/deep-person-reid`.
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3. To accelerate evaluation (10x faster), you can use cython-based evaluation code (developed by [luzai](https://github.com/luzai)). First `cd` to `eval_lib`, then do `make` or `python setup.py build_ext -i`. After that, run `python test_cython_eval.py` to test if the package is successfully installed.
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3. Install dependencies by `pip install -r requirements.txt`.
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4. To accelerate evaluation (10x faster), you can use cython-based evaluation code (developed by [luzai](https://github.com/luzai)). First `cd` to `eval_lib`, then do `make` or `python setup.py build_ext -i`. After that, run `python test_cython_eval.py` to test if the package is successfully installed.
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## Prepare data
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Create a directory to store reid datasets under this repo via
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```bash
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cd deep-person-reid/
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mkdir data/
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```
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Instructions regarding how to prepare datasets can be found [here]().
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If you wanna store datasets in another directory, you need to specify `--root path_to_your/data` when running the training code. Please follow the instructions below to prepare each dataset. After that, you can simply do `-d the_dataset` when running the training code.
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Please do not call image dataset when running video reid scripts, otherwise error would occur, and vice versa.
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**Market1501** [7]:
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1. Download dataset to `data/` from http://www.liangzheng.org/Project/project_reid.html.
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2. Extract dataset and rename to `market1501`. The data structure would look like:
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```
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market1501/
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bounding_box_test/
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bounding_box_train/
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...
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```
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3. Use `-d market1501` when running the training code.
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**CUHK03** [13]:
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1. Create a folder named `cuhk03/` under `data/`.
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2. Download dataset to `data/cuhk03/` from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and extract `cuhk03_release.zip`, so you will have `data/cuhk03/cuhk03_release`.
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3. Download new split [14] from [person-re-ranking](https://github.com/zhunzhong07/person-re-ranking/tree/master/evaluation/data/CUHK03). What you need are `cuhk03_new_protocol_config_detected.mat` and `cuhk03_new_protocol_config_labeled.mat`. Put these two mat files under `data/cuhk03`. Finally, the data structure would look like
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```
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cuhk03/
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cuhk03_release/
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cuhk03_new_protocol_config_detected.mat
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cuhk03_new_protocol_config_labeled.mat
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...
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```
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4. Use `-d cuhk03` when running the training code. In default mode, we use new split (767/700). If you wanna use the original splits (1367/100) created by [13], specify `--cuhk03-classic-split`. As [13] computes CMC differently from Market1501, you might need to specify `--use-metric-cuhk03` for fair comparison with their method. In addition, we support both `labeled` and `detected` modes. The default mode loads `detected` images. Specify `--cuhk03-labeled` if you wanna train and test on `labeled` images.
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**DukeMTMC-reID** [16, 17]:
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1. Create a directory under `data/` called `dukemtmc-reid`.
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2. Download dataset `DukeMTMC-reID.zip` from https://github.com/layumi/DukeMTMC-reID_evaluation#download-dataset and put it to `data/dukemtmc-reid`. Extract the zip file, which leads to
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```
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dukemtmc-reid/
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DukeMTMC-reid.zip # (you can delete this zip file, it is ok)
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DukeMTMC-reid/ # this folder contains 8 files.
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```
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3. Use `-d dukemtmcreid` when running the training code.
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**MSMT17** [22]:
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1. Create a directory named `msmt17/` under `data/`.
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2. Download dataset `MSMT17_V1.tar.gz` to `data/msmt17/` from http://www.pkuvmc.com/publications/msmt17.html. Extract the file under the same folder, so you will have
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```
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msmt17/
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MSMT17_V1.tar.gz # (do whatever you want with this .tar file)
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MSMT17_V1/
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train/
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test/
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list_train.txt
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... (totally six .txt files)
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```
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3. Use `-d msmt17` when running the training code.
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**VIPeR** [28]:
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1. The code supports automatic download and formatting. Just use `-d viper` as usual. The final data structure would look like:
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```
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viper/
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VIPeR/
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VIPeR.v1.0.zip # useless
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splits.json
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```
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**GRID** [29]:
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1. The code supports automatic download and formatting. Just use `-d grid` as usual. The final data structure would look like:
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```
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grid/
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underground_reid/
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underground_reid.zip # useless
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splits.json
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```
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**CUHK01** [30]:
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1. Create `cuhk01/` under `data/` or your custom data dir.
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2. Download `CUHK01.zip` from http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html and place it in `cuhk01/`.
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3. Do `-d cuhk01` to use the data.
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**PRID450S** [31]:
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1. The code supports automatic download and formatting. Just use `-d prid450s` as usual. The final data structure would look like:
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```
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prid450s/
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cam_a/
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cam_b/
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readme.txt
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splits.json
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```
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**MARS** [8]:
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1. Create a directory named `mars/` under `data/`.
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2. Download dataset to `data/mars/` from http://www.liangzheng.com.cn/Project/project_mars.html.
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3. Extract `bbox_train.zip` and `bbox_test.zip`.
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4. Download split information from https://github.com/liangzheng06/MARS-evaluation/tree/master/info and put `info/` in `data/mars` (we want to follow the standard split in [8]). The data structure would look like:
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```
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mars/
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bbox_test/
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bbox_train/
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info/
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```
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5. Use `-d mars` when running the training code.
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**iLIDS-VID** [11]:
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1. The code supports automatic download and formatting. Simple use `-d ilidsvid` when running the training code. The data structure would look like:
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```
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ilids-vid/
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i-LIDS-VID/
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train-test people splits/
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splits.json
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```
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**PRID** [12]:
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1. Under `data/`, do `mkdir prid2011` to create a directory.
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2. Download dataset from https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/PRID11/ and extract it under `data/prid2011`.
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3. Download the split created by [iLIDS-VID](http://www.eecs.qmul.ac.uk/~xiatian/downloads_qmul_iLIDS-VID_ReID_dataset.html) from [here](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/datasets/prid2011/splits_prid2011.json), and put it in `data/prid2011/`. We follow [11] and use 178 persons whose sequences are more than a threshold so that results on this dataset can be fairly compared with other approaches. The data structure would look like:
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```
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prid2011/
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splits_prid2011.json
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prid_2011/
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multi_shot/
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single_shot/
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readme.txt
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```
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4. Use `-d prid` when running the training code.
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**DukeMTMC-VideoReID** [16, 23]:
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1. Make a directory `data/dukemtmc-vidreid`.
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2. Download `dukemtmc_videoReID.zip` from https://github.com/Yu-Wu/DukeMTMC-VideoReID. Unzip the file to `data/dukemtmc-vidreid`. You need to have
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```
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dukemtmc-vidreid/
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dukemtmc_videoReID/
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train_split/
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query_split/
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gallery_split/
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... (and two license files)
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```
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3. Use `-d dukemtmcvidreid` when running the training code.
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## Dataset loaders
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These are implemented in `dataset_loader.py` where we have two main classes that subclass [torch.utils.data.Dataset](http://pytorch.org/docs/master/_modules/torch/utils/data/dataset.html#Dataset):
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* [ImageDataset](https://github.com/KaiyangZhou/deep-person-reid/blob/master/dataset_loader.py#L22): processes image-based person reid datasets.
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* [VideoDataset](https://github.com/KaiyangZhou/deep-person-reid/blob/master/dataset_loader.py#L38): processes video-based person reid datasets.
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These two classes are used for [torch.utils.data.DataLoader](http://pytorch.org/docs/master/_modules/torch/utils/data/dataloader.html#DataLoader) that can provide batched data. Data loader wich `ImageDataset` outputs batch data of `(batch, channel, height, width)`, while data loader with `VideoDataset` outputs batch data of `(batch, sequence, channel, height, width)`.
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## Models
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* `models/ResNet.py`: ResNet50 [1], ResNet101 [1], ResNet50M [2].
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* `models/ResNeXt.py`: ResNeXt101 [26].
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* `models/SEResNet.py`: SEResNet50 [25], SEResNet101 [25], SEResNeXt50 [25], SEResNeXt101 [25].
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* `models/DenseNet.py`: DenseNet121 [3].
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* `models/MuDeep.py`: MuDeep [10].
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* `models/HACNN.py`: HACNN [15].
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* `models/SqueezeNet.py`: SqueezeNet [18].
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* `models/MobileNet.py`: MobileNetV2 [19].
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* `models/ShuffleNet.py`: ShuffleNet [20].
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* `models/Xception.py`: Xception [21].
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* `models/InceptionV4.py`: InceptionV4 [24].
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* `models/InceptionResNetV2.py`: InceptionResNetV2 [24].
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* `models/DPN.py`: DPN92 [27].
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Currently, we have the following models:
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* `models/resnet.py`: ResNet50 [1], ResNet101 [1], ResNet50M [2].
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* `models/resnext.py`: ResNeXt101 [26].
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* `models/seresnet.py`: SEResNet50 [25], SEResNet101 [25], SEResNeXt50 [25], SEResNeXt101 [25].
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* `models/densenet.py`: DenseNet121 [3].
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* `models/mudeep.py`: MuDeep [10].
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* `models/hacnn.py`: HACNN [15].
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* `models/squeezenet.py`: SqueezeNet [18].
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* `models/mobilenetv2.py`: MobileNetV2 [19].
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* `models/shufflenet.py`: ShuffleNet [20].
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* `models/xception.py`: Xception [21].
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* `models/inceptionv4.py`: InceptionV4 [24].
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* `models/inceptionresnetv2.py`: InceptionResNetV2 [24].
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* `models/dpn.py`: DPN92 [27].
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See `models/__init__.py` for details regarding what keys to use to call these models.
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## Loss functions
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* `xent`: cross entropy + label smoothing regularizer [5].
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* `htri`: triplet loss with hard positive/negative mining [4] .
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* `cent`: center loss [9].
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Benchmarks can be found [here]().
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Optimizers are wrapped in `optimizers.py`, which supports `adam` (default) and `sgd`. Use `--optim string_name` to manage the optimizer.
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## Train
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Training codes are implemented mainly in
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* `train_img_model_xent.py`: train image model with cross entropy loss.
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* `train_img_model_xent_htri.py`: train image model with combination of cross entropy loss and hard triplet loss.
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* `train_img_model_cent.py`: train image model with center loss.
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* `train_vid_model_xent.py`: train video model with cross entropy loss.
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* `train_vid_model_xent_htri.py`: train video model with combination of cross entropy loss and hard triplet loss.
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* `train_imgreid_xent.py`: train image model with cross entropy loss.
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* `train_imgreid_xent_htri.py`: train image model with combination of cross entropy loss and hard triplet loss.
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* `train_vidreid_xent.py`: train video model with cross entropy loss.
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* `train_vidreid_xent_htri.py`: train video model with combination of cross entropy loss and hard triplet loss.
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For example, to train an image reid model using ResNet50 and cross entropy loss, run
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```bash
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python train_img_model_xent.py -d market1501 -a resnet50 --max-epoch 60 --train-batch 32 --test-batch 32 --stepsize 20 --eval-step 20 --save-dir log/resnet50-xent-market1501 --gpu-devices 0
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python train_imgreid_xent.py -d market1501 -a resnet50 --optim adam --lr 0.0003 --max-epoch 60 --stepsize 20 40 --train-batch 32 --test-batch 32 --eval-step 20 --save-dir log/resnet50-xent-market1501 --gpu-devices 0
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```
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To use multiple GPUs, you can set `--gpu-devices 0,1,2,3`.
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Please run `python train_blah_blah.py -h` for more details regarding arguments.
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## Results
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:dog: means that model is initialized with imagenet pretrained weights.
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### Image person reid
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#### Market1501
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| Model | Param Size (M) | Loss | Rank-1/5/10 (%) | mAP (%) | Model weights | Published Rank | Published mAP |
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| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| DenseNet121:dog: | 7.72 | xent | 86.5/93.6/95.7 | 67.8 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/densenet121_xent_market1501.pth.tar) | | |
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| DenseNet121:dog: | 7.72 | xent+htri | 89.5/96.3/97.5 | 72.6 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/densenet121_xent_htri_market1501.pth.tar) | | |
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| ResNet50:dog: | 25.05 | xent | 85.4/94.1/95.9 | 68.8 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/resnet50_xent_market1501.pth.tar) | | |
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| ResNet50:dog: | 25.05 | xent+htri | 87.5/95.3/97.3 | 72.3 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/resnet50_xent_htri_market1501.pth.tar) | | |
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| ResNet50M:dog: | 30.01 | xent | 89.4/95.9/97.4 | 75.0 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/resnet50m_xent_market1501.pth.tar) | 89.9/-/- | 75.6 |
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| ResNet50M:dog: | 30.01 | xent+htri | 90.7/97.0/98.2 | 76.8 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/resnet50m_xent_htri_market1501.pth.tar) | | |
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| MuDeep | 138.02 | xent+htri| 71.5/89.3/96.3 | 47.0 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/mudeep_xent_htri_market1501.pth.tar) | | |
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| SqueezeNet | 1.13 | xent | 65.1/82.3/87.9 | 41.6 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/squeezenet_xent_market1501.pth.tar) | | |
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| MobileNetV2 | 3.19 | xent | 77.0/89.5/92.8 | 56.3 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/mobilenet_xent_market1501.pth.tar) | | |
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| ShuffleNet | 1.63 | xent | 68.7/85.7/90.2 | 44.9 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/shufflenet_xent_market1501.pth.tar) | | |
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| Xception | 22.39 | xent | 72.1/88.2/92.1 | 52.8 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/xception_xent_market1501.pth.tar) | | |
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| HACNN | 3.70 | xent | 88.7/95.3/97.4 | 71.2 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/hacnn_xent_market1501.pth.tar) | 91.2/-/- | 75.7 |
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#### CUHK03 (detected, [new protocol (767/700)](https://github.com/zhunzhong07/person-re-ranking#the-new-trainingtesting-protocol-for-cuhk03))
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| Model | Param Size (M) | Loss | Rank-1/5/10 (%) | mAP (%) | Model weights | Published Rank | Published mAP |
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| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| DenseNet121:dog: | 7.74 | xent | 41.0/61.7/71.5 | 40.6 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/densenet121_xent_cuhk03.pth.tar) | | |
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| ResNet50:dog: | 25.08 | xent | 48.8/69.4/78.4 | 47.5 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/resnet50_xent_cuhk03.pth.tar) | | |
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| ResNet50M:dog: | 30.06 | xent | 57.5/75.4/82.5 | 55.2 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/resnet50m_xent_cuhk03.pth.tar) | 47.1/-/- | 43.5 |
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| HACNN | 3.72 | xent | 42.4/60.9/70.5 | 40.9 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/hacnn_xent_cuhk03.pth.tar) | 41.7/-/- |38.6 |
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| SqueezeNet | 1.13 | xent | 20.0/38.4/48.2 | 20.0 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/squeezenet_xent_cuhk03.pth.tar) | | |
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| MobileNetV2 | 3.21 | xent | 35.1/55.8/64.7 | 33.8 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/mobilenet_xent_cuhk03.pth.tar) | | |
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| ShuffleNet | 1.64 | xent | 22.0/39.3/49.9 | 21.2 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/shufflenet_xent_cuhk03.pth.tar) | | |
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#### DukeMTMC-reID
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| Model | Param Size (M) | Loss | Rank-1/5/10 (%) | mAP (%) | Model weights | Published Rank | Published mAP |
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| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| DenseNet121:dog: | 7.67 | xent | 74.9/86.0/88.8 | 54.5 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/densenet121_xent_dukemtmcreid.pth.tar) | | |
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| ResNet50:dog: | 24.94 | xent | 76.3/87.1/90.9 | 59.5 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/resnet50_xent_dukemtmcreid.pth.tar) | | |
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| ResNet50M:dog: | 29.86 | xent | 80.5/89.8/92.4 | 63.3 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/resnet50m_xent_dukemtmcreid.pth.tar) | 80.4/-/- | 63.9 |
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| SqueezeNet | 1.10 | xent | 50.2/68.9/75.3 | 30.3 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/squeezenet_xent_dukemtmcreid.pth.tar) | | |
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| MobileNetV2 | 3.12 | xent | 65.6/79.2/83.7 | 43.6 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/mobilenet_xent_dukemtmcreid.pth.tar) | | |
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| ShuffleNet | 1.58 | xent | 56.9/74.680.5 | 37.8 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/shufflenet_xent_dukemtmcreid.pth.tar) | | |
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| HACNN | 3.65 | xent | 78.5/88.8/91.3 | 60.8 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/image-models/hacnn_xent_dukemtmcreid.pth.tar) | 80.5/-/- | 63.8 |
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### Video person reid
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#### MARS
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| Model | Param Size (M) | Loss | Rank-1/5/10 (%) | mAP (%) | Model weights | Published Rank | Published mAP |
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| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| DenseNet121:dog: | 7.59 | xent | 65.2/81.1/86.3 | 52.1 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/video-models/densenet121_xent_mars.pth.tar) | | |
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| DenseNet121:dog: | 7.59 | xent+htri | 82.6/93.2/95.4 | 74.6 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/video-models/densenet121_xent_htri_mars.pth.tar) | | |
|
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| ResNet50:dog: | 24.79 | xent | 74.5/88.8/91.8 | 64.0 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/video-models/resnet50_xent_mars.pth.tar) | | |
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| ResNet50:dog: | 24.79 | xent+htri | 80.8/92.1/94.3 | 74.0 | [download](http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/video-models/resnet50_xent_htri_mars.pth.tar) | | |
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| ResNet50M:dog: | 29.63 | xent | 77.8/89.8/92.8 | 67.5 | - | | |
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| ResNet50M:dog: | 29.63 | xent+htri | 82.3/93.8/95.3 | 75.4 | - | | |
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## Test
|
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Say you have downloaded ResNet50 trained with `xent` on `market1501`. The path to this model is `'saved-models/resnet50_xent_market1501.pth.tar'` (create a directory to store model weights `mkdir saved-models/`). Then, run the following command to test
|
||||
Say you have downloaded ResNet50 trained with `xent` on `market1501`. The path to this model is `'saved-models/resnet50_xent_market1501.pth.tar'` (create a directory to store model weights `mkdir saved-models/` beforehand). Then, run the following command to test
|
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```bash
|
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python train_img_model_xent.py -d market1501 -a resnet50 --evaluate --resume saved-models/resnet50_xent_market1501.pth.tar --save-dir log/resnet50-xent-market1501 --test-batch 32
|
||||
python train_imgreid_xent.py -d market1501 -a resnet50 --evaluate --resume saved-models/resnet50_xent_market1501.pth.tar --save-dir log/resnet50-xent-market1501 --test-batch 100 --gpu-devices 0
|
||||
```
|
||||
|
||||
Likewise, to test video reid model, you should have a pretrained model saved under `saved-models/`, e.g. `saved-models/resnet50_xent_mars.pth.tar`, then run
|
||||
```bash
|
||||
python train_vid_model_xent.py -d mars -a resnet50 --evaluate --resume saved-models/resnet50_xent_mars.pth.tar --save-dir log/resnet50-xent-mars --test-batch 2
|
||||
python train_vid_model_xent.py -d mars -a resnet50 --evaluate --resume saved-models/resnet50_xent_mars.pth.tar --save-dir log/resnet50-xent-mars --test-batch 2 --gpu-devices 0
|
||||
```
|
||||
**Note** that `--test-batch` in video reid represents number of tracklets. If we set this argument to 2, and sample 15 images per tracklet, the resulting number of images per batch is 2*15=30. Adjust this argument according to your GPU memory.
|
||||
**Note** that `--test-batch` in video reid represents number of tracklets. If you set this argument to 2, and sample 15 images per tracklet, the resulting number of images per batch is 2*15=30. Adjust this argument according to your GPU memory.
|
||||
|
||||
## Q&A
|
||||
1. **How do I set different learning rates to different components in my model?**
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|
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Loading…
Reference in New Issue