### Install Project Dependencies

Source: https://github.com/vlsomers/bpbreid/blob/main/README.md

Install all required Python packages listed in 'requirements.txt'. Ensure 'which python' and 'which pip' point to the correct paths within your activated environment.

```bash
pip install -r requirements.txt
```

--------------------------------

### Clone Repository

Source: https://github.com/vlsomers/bpbreid/blob/main/README.md

Clone the bpbreid repository from GitHub to start.

```bash
git clone https://github.com/VlSomers/bpbreid
```

--------------------------------

### Launch Training Procedure

Source: https://github.com/vlsomers/bpbreid/blob/main/README.md

Initiate the training procedure for specified datasets using the main script. Ensure the 'bpbreid' conda environment is activated and provide the path to the training configuration file. Download and install human parsing labels for your training dataset beforehand.

```bash
conda activate bpbreid
python ./torchreid/scripts/main.py --config-file configs/bpbreid/bpbreid_<target_dataset>_train.yaml
```

```bash
conda activate bpbreid
python torchreid/scripts/main.py --config-file configs/bpbreid/bpbreid_occ_duke_train.yaml
```

--------------------------------

### Install PyTorch and Torchvision

Source: https://github.com/vlsomers/bpbreid/blob/main/README.md

Install PyTorch and Torchvision, specifying the appropriate CUDA toolkit version for your system. This command uses the pytorch channel.

```bash
conda install pytorch torchvision cudatoolkit=9.0 -c pytorch
```

--------------------------------

### Install torchreid

Source: https://github.com/vlsomers/bpbreid/blob/main/README.md

Install the torchreid package in development mode. This allows for modifications to the source code without needing to rebuild.

```bash
python setup.py develop
```

--------------------------------

### Show Available Models

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Displays all available models in the torchreid library. No setup is required.

```python
import torchreid
torchreid.models.show_avai_models()
```

--------------------------------

### Clone Torchreid Repository and Set Up Environment

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Clone the Torchreid repository and set up a conda environment with necessary dependencies. This includes installing PyTorch and torchvision with a specific CUDA version.

```bash
git clone https://github.com/KaiyangZhou/deep-person-reid.git

cd deep-person-reid/
conda create --name torchreid python=3.7
conda activate torchreid

pip install -r requirements.txt

conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

python setup.py develop
```

--------------------------------

### Two-Stepped Transfer Learning Example

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Pretrain randomly initialized layers for a few epochs while freezing base layers. 'fixbase_epoch' sets the number of epochs to freeze, and 'open_layers' specifies which layers to train.

```python
engine.run(
    save_dir='log/resnet50',
    max_epoch=60,
    eval_freq=10,
    test_only=False,
    fixbase_epoch=5,
    open_layers='classifier'
)
# or open_layers=['fc', 'classifier'] if there is another fc layer that
# is randomly initialized, like resnet50_fc512
```

--------------------------------

### Cross-Domain Training OSNet

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Train OSNet on DukeMTMC-reID and test on Market1501. This setup is useful for evaluating generalization to unseen datasets. Note the change in transforms.

```bash
python scripts/main.py \
    --config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad.yaml \
    -s dukemtmcreid \
    -t market1501 \
    --transforms random_flip color_jitter \
    --root $PATH_TO_DATA
```

--------------------------------

### Initialize Image Data Manager

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Configure and initialize the ImageDataManager for handling image-based person re-identification datasets. This setup specifies data sources, dimensions, batch sizes, and image transformations.

```python
datamanager = torchreid.data.ImageDataManager(
    root='reid-data',
    sources='market1501',
    targets='market1501',
    height=256,
    width=128,
    batch_size_train=32,
    batch_size_test=100,
    transforms=['random_flip', 'random_crop']
)
```

--------------------------------

### Feature Extraction with a Pre-trained Model

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Use the FeatureExtractor utility to get feature embeddings from a list of images using a specified model and path. The device can be set to 'cuda' or 'cpu'.

```python
from torchreid.utils import FeatureExtractor

extractor = FeatureExtractor(
    model_name='osnet_x1_0',
    model_path='a/b/c/model.pth.tar',
    device='cuda'
)

image_list = [
    'a/b/c/image001.jpg',
    'a/b/c/image002.jpg',
    'a/b/c/image003.jpg',
    'a/b/c/image004.jpg',
    'a/b/c/image005.jpg'
]

features = extractor(image_list)
print(features.shape) # output (5, 512)
```

--------------------------------

### Build and Initialize Training Engine

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Initialize the ImageSoftmaxEngine for training and evaluation. This engine integrates the data manager, model, optimizer, scheduler, and loss smoothing settings.

```python
engine = torchreid.engine.ImageSoftmaxEngine(
    datamanager,
    model,
    optimizer=optimizer,
    scheduler=scheduler,
    label_smooth=True
)
```

--------------------------------

### Build and Configure Model, Optimizer, and LR Scheduler

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Build a deep learning model (e.g., ResNet50) for person re-identification, configure the optimizer (e.g., Adam), and set up a learning rate scheduler (e.g., single_step). The model is moved to the GPU.

```python
model = torchreid.models.build_model(
    name='resnet50',
    num_classes=datamanager.num_train_pids,
    loss='softmax',
    pretrained=True
)

model = model.cuda()

optimizer = torchreid.optim.build_optimizer(
    model,
    optim='adam',
    lr=0.0003
)

scheduler = torchreid.optim.build_lr_scheduler(
    optimizer,
    lr_scheduler='single_step',
    stepsize=20
)
```

--------------------------------

### Visualize Activation Maps

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Run this command to visualize activation maps for a given model and dataset. Specify the model weights and save directory.

```shell
python tools/visualize_actmap.py \
    --root $DATA/reid \
    -d market1501 \
    -m osnet_x1_0 \
    --weights PATH_TO_PRETRAINED_WEIGHTS \
    --save-dir log/visactmap_osnet_x1_0_market1501
```

--------------------------------

### Train OSNet on Market1501

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Use this command to train the OSNet model on the Market1501 dataset. Ensure PATH_TO_DATA is set to your dataset directory.

```bash
python scripts/main.py \
    --config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
    --transforms random_flip random_erase \
    --root $PATH_TO_DATA
```

--------------------------------

### Run Training and Testing

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Execute the training and testing process using the configured engine. This includes specifying the save directory for logs, maximum training epochs, evaluation frequency, and whether to run in test-only mode.

```python
engine.run(
    save_dir='log/resnet50',
    max_epoch=60,
    eval_freq=10,
    test_only=False
)
```

--------------------------------

### Initialize Image Data Manager with Multiple Datasets

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Combine your custom dataset with existing datasets in the ImageDataManager. Provide a list of dataset names for the 'sources' argument.

```python
# combine with other datasets
datamanager = torchreid.data.ImageDataManager(
    root='reid-data',
    sources=['new_dataset', 'dukemtmcreid']
)
```

--------------------------------

### Run Inference with Pre-trained Models

Source: https://github.com/vlsomers/bpbreid/blob/main/README.md

Test downloaded pre-trained models by running the main script with a specific configuration file. Activate the 'bpbreid' conda environment and specify the path to your test configuration file.

```bash
conda activate bpbreid
python torchreid/scripts/main.py --config-file configs/bpbreid/bpbreid_<target_dataset>_test.yaml
```

```bash
conda activate bpbreid
python torchreid/scripts/main.py --config-file configs/bpbreid/bpbreid_market1501_test.yaml
```

--------------------------------

### Resume Training from Checkpoint

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Resumes a training process from a saved checkpoint. Ensure the model and optimizer objects are available.

```python
start_epoch = torchreid.utils.resume_from_checkpoint(
    'log/resnet50/model.pth.tar-30',
    model,
    optimizer
)

engine.run(
    save_dir='log/resnet50',
    max_epoch=60,
    start_epoch=start_epoch
)
```

--------------------------------

### Create and Activate Conda Environment

Source: https://github.com/vlsomers/bpbreid/blob/main/README.md

Create a new conda environment named 'bpbreid' with Python 3.10 and activate it.

```bash
cd bpbreid/ # enter project folder
conda create --name bpbreid python=3.10
conda activate bpbreid
```

--------------------------------

### Initialize Image Data Manager with a Single Custom Dataset

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Configure the ImageDataManager to use only your newly registered dataset. Specify the root directory for your data and the dataset name.

```python
# use your own dataset only
datamanager = torchreid.data.ImageDataManager(
    root='reid-data',
    sources='new_dataset'
)
```

--------------------------------

### Import Torchreid Library

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Import the Torchreid library to begin using its functionalities for person re-identification tasks.

```python
import torchreid
```

--------------------------------

### Initialize Image Data Manager for Cross-Dataset Evaluation

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Set up the ImageDataManager for cross-dataset evaluation by specifying both training sources and target datasets. This allows evaluation on datasets different from the training set.

```python
# cross-dataset evaluation
datamanager = torchreid.data.ImageDataManager(
    root='reid-data',
    sources=['new_dataset', 'dukemtmcreid'],
    targets='market1501' # or targets=['market1501', 'cuhk03']
)
```

--------------------------------

### Define a New Image Dataset

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

This template shows how to create a custom image dataset by defining its structure and data lists (train, query, gallery). It should inherit from ImageDataset.

```python
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division

import sys
import os
import os.path as osp

from torchreid.data import ImageDataset


class NewDataset(ImageDataset):
    dataset_dir = 'new_dataset'

    def __init__(self, root='', **kwargs):
        self.root = osp.abspath(osp.expanduser(root))
        self.dataset_dir = osp.join(self.root, self.dataset_dir)

        # All you need to do here is to generate three lists,
        # which are train, query and gallery.
        # Each list contains tuples of (img_path, pid, camid),
        # where
        # - img_path (str): absolute path to an image.
        # - pid (int): person ID, e.g. 0, 1.
        # - camid (int): camera ID, e.g. 0, 1.
        # Note that
        # - pid and camid should be 0-based.
        # - query and gallery should share the same pid scope (e.g.
        #   pid=0 in query refers to the same person as pid=0 in gallery).
        # - train, query and gallery share the same camid scope (e.g.
        #   camid=0 in train refers to the same camera as camid=0
        #   in query/gallery).
        train = ...
        query = ...
        gallery = ...

        super(NewDataset, self).__init__(train, query, gallery, **kwargs)
```

--------------------------------

### Train OSNet on DukeMTMC-reID

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Train the OSNet model on the DukeMTMC-reID dataset. This command specifies the dataset and configures the save directory for logs and models.

```bash
python scripts/main.py \
    --config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
    -s dukemtmcreid \
    -t dukemtmcreid \
    --transforms random_flip random_erase \
    --root $PATH_TO_DATA \
    data.save_dir log/osnet_x1_0_dukemtmcreid_softmax_cosinelr
```

--------------------------------

### Combine All Data for Training

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Enables combining all subsets (train, query, gallery) into the training set by setting combineall=True. This affects the dataset statistics.

```python
datamanager = torchreid.data.ImageDataManager(
    root='reid-data',
    sources='market1501',
    height=256,
    width=128,
    batch_size=32,
    market1501_500k=False,
    combineall=True # it's me, here
)
```

--------------------------------

### Combine Multiple Datasets for Training

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Instantiates an ImageDataManager to use multiple datasets for training. Specify dataset keys in the 'sources' argument.

```python
datamanager = torchreid.data.ImageDataManager(
    root='reid-data',
    sources=['market1501', 'dukemtmcreid', 'cuhk03', 'msmt17'],
    height=256,
    width=128,
    batch_size=32
)
```

--------------------------------

### Market1501 Dataset Structure

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/datasets.rst

The expected directory structure for the Market1501 dataset after extraction. This includes query, training, and testing bounding box images.

```none
market1501/
    Market-1501-v15.09.15/
        query/
        bounding_box_train/
        bounding_box_test/
```

--------------------------------

### Evaluate Trained OSNet Model

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Run this command to re-evaluate a trained OSNet model. Specify the path to the saved model weights for evaluation.

```bash
python scripts/main.py \
    --config-file configs/im_osnet_x1_0_softmax_256x128_amsgrad_cosine.yaml \
    --root $PATH_TO_DATA \
    model.load_weights log/osnet_x1_0_market1501_softmax_cosinelr/model.pth.tar-250 \
    test.evaluate True
```

--------------------------------

### CUHK03 Dataset Structure

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/datasets.rst

The expected directory structure for the CUHK03 dataset, including extracted release files and configuration matrices for different protocols.

```none
cuhk03/
    cuhk03_release/
    cuhk03_new_protocol_config_detected.mat
    cuhk03_new_protocol_config_labeled.mat
```

--------------------------------

### DukeMTMC-reID Dataset Structure

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/datasets.rst

The expected directory structure for the DukeMTMC-reID dataset after extraction. It should contain query, training, and testing bounding box images.

```none
dukemtmc-reid/
    DukeMTMC-reID/
        query/
        bounding_box_train/
        bounding_box_test/
        ...
```

--------------------------------

### Set Different Learning Rates for Base and New Layers

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Use this to set a smaller learning rate for base layers and a larger one for newly initialized layers. Ensure 'staged_lr' is True and 'new_layers' are specified.

```python
# New layer "classifier" has a learning rate of 0.01
# The base layers have a learning rate of 0.001
optimizer = torchreid.optim.build_optimizer(
    model,
    optim='sgd',
    lr=0.01,
    staged_lr=True,
    new_layers='classifier',
    base_lr_mult=0.1
)
```

--------------------------------

### MSMT17 Dataset Structure

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/datasets.rst

The expected directory structure for the MSMT17 dataset, including training and testing data, along with text files listing the data splits.

```none
msmt17/
    MSMT17_V1/ # or MSMT17_V2
        train/
        test/
        list_train.txt
        list_query.txt
        list_gallery.txt
        list_val.txt
```

--------------------------------

### Generate Human Parsing Labels

Source: https://github.com/vlsomers/bpbreid/blob/main/README.md

Use this command to generate human parsing labels for your custom dataset. Ensure you have activated the 'bpbreid' conda environment and replace '[Dataset Path]' with the actual path to your dataset.

```bash
conda activate bpbreid
python torchreid/scripts/get_labels --source [Dataset Path]
```

--------------------------------

### Perform Cross-Dataset Evaluation

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Configures an ImageDataManager for cross-dataset evaluation. Use the 'targets' argument to specify evaluation datasets.

```python
datamanager = torchreid.data.ImageDataManager(
    root='reid-data',
    sources='market1501',
    targets='dukemtmcreid', # or targets='cuhk03' or targets=['dukemtmcreid', 'cuhk03']
    height=256,
    width=128,
    batch_size=32
)
```

--------------------------------

### Register a New Image Dataset

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Register your custom dataset with torchreid using the provided function. This allows the data manager to recognize and use your dataset.

```python
import torchreid
torchreid.data.register_image_dataset('new_dataset', NewDataset)
```

--------------------------------

### BPBReID Citation

Source: https://github.com/vlsomers/bpbreid/blob/main/README.md

Use this BibTeX entry to cite the BPBReID method in your research. It includes archive prefix, author, DOI, and publication details.

```bibtex
@article{bpbreid,
    archivePrefix = {arXiv},
    arxivId = {2211.03679},
    author = {Somers, Vladimir and {De Vleeschouwer}, Christophe and Alahi, Alexandre},
    doi = {10.48550/arxiv.2211.03679},
    eprint = {2211.03679},
    isbn = {2211.03679v1},
    journal = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV23)},
    month = {nov},
    title = {{Body Part-Based Representation Learning for Occluded Person Re-Identification}},
    url = {https://arxiv.org/abs/2211.03679v1 http://arxiv.org/abs/2211.03679},
    year = {2023}
}
```

--------------------------------

### Compute Model Complexity

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/user_guide.rst

Calculates the number of parameters and FLOPs for a given model. The input shape is (batch_size, channels, height, width).

```python
from torchreid import models, utils

model = models.build_model(name='resnet50', num_classes=1000)
num_params, flops = utils.compute_model_complexity(model, (1, 3, 256, 128))
```

```python
# show detailed complexity for each module
utils.compute_model_complexity(model, (1, 3, 256, 128), verbose=True)
```

```python
# count flops for all layers including ReLU and BatchNorm
utils.compute_model_complexity(model, (1, 3, 256, 128), verbose=True, only_conv_linear=False)
```

--------------------------------

### evaluate_rank

Source: https://github.com/vlsomers/bpbreid/blob/main/docs/pkg/metrics.rst

Evaluates rank-based metrics for re-identification tasks. This function is part of the torchreid.metrics.rank module.

```APIDOC
## evaluate_rank

### Description
Evaluates rank-based metrics for re-identification tasks.

### Method
(Not specified in source, likely a Python function call)

### Endpoint
(Not applicable, this is a Python module function)

### Parameters
(Parameters are not detailed in the source, but typically include predictions and ground truth labels)

### Request Example
(Not applicable for a Python function)

### Response
(Response details are not specified in the source, but typically include metrics like mAP and rank-1 accuracy)

#### Success Response
(Not specified in source)

#### Response Example
(Not specified in source)
```

--------------------------------

### Torchreid Citation

Source: https://github.com/vlsomers/bpbreid/blob/main/Torchreid_original_README.rst

Citation details for the Torchreid library and related research papers. This is typically used for academic referencing.

```bash
@article{torchreid,
  title={Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch},
  author={Zhou, Kaiyang and Xiang, Tao},
  journal={arXiv preprint arXiv:1910.10093},
  year={2019}
}

@inproceedings{zhou2019osnet,
  title={Omni-Scale Feature Learning for Person Re-Identification},
  author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
  booktitle={ICCV},
  year={2019}
}

@article{zhou2019learning,
  title={Learning Generalisable Omni-Scale Representations for Person Re-Identification},
  author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
  journal={arXiv preprint arXiv:1910.06827},
  year={2019}
}
```

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