A standard, medium-sized baseline code for Person-reID (based on pytorch).

• Standard. Arrived at Rank@1:86.7280% Rank@5:94.6853% Rank@10:96.3777% mAP:67.7160% @epoch16. You may learn from console.log if you want to know about the decline of losses and errors in detail.

ResNet(pretrained) @ epoch16:

Quick facts:

Running time(on CPU):

• train: 1 hours 8 minutes for one epoch
• test: 28 minutes for 10000 pics
• evaluate: 12 seconds if no re-rank, 3 minutes 20 seconds if re-rank
• visualize: 6 seconds

• Medium-sized. The model, resnet-50 (from Deep Residual Learning for Image Recognition), has 27M parameters. The baseline could be trained with only 2GB memory.

• Features
• Some News
• Model Structure
• Prerequisites
• Getting Started
  • Installation
  • Dataset Preparation
  • Quick Start
  • Train
  • Test
  • Evaluation
  • Visualization
• Intro to some data files
• Related Repos

Now supported:

• ResNet50 with a self-defined classification block
• Single-shot/Multi-shot
• Train set split
• Re-ranking(supported by Re-ranking Person Re-identification with k-reciprocal Encoding)
• Visualize Training Curves & Ranking Result(supported by Tensorboard)

25 May 2020 Finish the draft.

20 May 2020 Start writing my thesis.

15 May 2020 Collect related literature for reference.

29 April 2020 Try to improve the visualization of embeddings. The t-SNE produces a too dense embedding representation so I choose UMAP for better display effects.

28 April 2020 Find that euclidean distance computation is 100 times slower than matrix multiplication. That really surprises me. So I replace all the euclidean distance with normalization and matrix computation, and it saves maybe 3 minutes running time on CPU. Nearly finishes the visualization module. It is hardly possible to interpret heatmaps and know what exactly the network learns.

27 April 2020 Do evaluation on test set and get nearly 90%@r1. ResNet really learns from (but maybe overfits) the Market-1501 dataset.

19 April 2020 Run Person re-ID baseline with triplet loss for 8 hours. It seems like the crazy code needs more than 10GB memory to run for option batch_size=32 and it's impractical for my PC. I think maybe some unnecessary gradients are computed during online mining. I will check later if possible.

13 April 2020 Visualize some rank list. Distractors have some notable impact on performance.

12 April 2020 Get the awful result of Rank@1:5.0178% Rank@5:12.2328% Rank@10:17.7553% mAP:1.3463%. Wonder if the entire training is just a big joke.

11 April 2020 Use 320 minutes to train MssNet for 50 epochs on CPU.

23 March 2020 Make minor modification to Person_reID_baseline_pytorch to train a pretrained Resnet-50 on CPU.

The re-ranking in Zhong Z, Zheng L, Cao D, et al. Re-ranking Person Re-identification with k-reciprocal Encoding[J]. 2017. enhance the mAP remarkably.

Quick facts:

Running time(on CPU):

• train: 75 minutes for one epoch
• test: 60 minutes
• evaluate: less than 30 seconds
• visualize: 3 minutes

Use Cross Entropy Loss

Use k-reciprokal for re-ranking

The backbone of the model is ResNet, which consists of four blocks of bottlenecks' structure. The design of bottleneck-like structure is very efficient as it reduces parameters by times.

You may learn more from models/model.py.

• CPU Memory >= 2G
• Python 3
• Numpy
• Pytorch
• torchvision
• Scipy
• Matplotlib

Note that the code is tested inside the environment listed in requirements.txt.

• Install Pytorch using some package manager, e.g. pip/conda

pip install torch torchvision

or

conda install torch torchvision

Download Market1501 Dataset from [Baidu link].

Preparation: Change the download_dir in settings.txt to your own path.

# from settings.txt
[market1501]
download_dir = YOUR PATH HERE

Additionally, you can change other paths(e.g. dataset_dir, train_path, test_path, etc) in settings.txt to your desired paths.

Make sure you've done the steps above.

Then run main.py and the code is supposed to work.

If any wrong happens, open requirements.txt to check if all required packages and their versions.

If you want to customize anything, please continue reading.

Train a model in components/trainers.py.

Read from: None

Write to: models/resnet50_epochNum.pth, analysis/train.mat

Trainer(self, config, config_path, name, dataset, split, model, pretrain: bool, optimizer, lr: float, momentum: float, weight_decay: float, start_epoch: int)

config a ConfigParser object which reads settings.txt

config_path the path to settings.txt

name a dataset's name in name_list = ['market1501']

dataset a dataset corresponding to an organizational form for data in dataset_list = ['image', 'extended', 'triplet']

split a training set split method in split_list = ['train_only', 'train_val']

model a model in model_list = ['mssnet', 'resnet50']

pretrain a pretrain option in [True, False]

optimizer an optimizer in optimizer_list = ['sgd']

(optional)lr the learning rate in sgd, default 0.05

(optional)momentum the momentum in sgd, default 0.9

(optional)weight_decay the weight decay in sgd, default 5e-4

(optional)if you want to train from a saved model, set start_epoch to the epoch number and training will start from there.

Use a trained model to extract feature in components/testers.py.

Read from: models/resnet50_epochNum.pth

Write to: analysis/test.mat

Tester(self, config, name: str, dataset: str, model, epoch: int, scene: str)

config a ConfigParser object which reads settings.txt

name a dataset's name in name_list = ['market1501']

dataset a dataset corresponding to an organizational form for data in dataset_list = ['image', 'extended', 'triplet']

model a model in model_list = ['mssnet', 'resnet50']

epoch the epoch of the saved trained model for testing

scene the scene for testing & evaluating & visualizing in scene_list = ['single_shot', 'multi_shot']

Output Rank@1, Rank@5, Rank@10 and mAP results in components/evaluators.py.

Use the triangle mAP calculation protocol here (consistent with the Market1501 original code Baidu Link).

Read from: analysis/test.mat

Write to: analysis/evaluate.mat

Market1501Evaluator(self, config, mode: str, re_rank: bool)

config a ConfigParser object which reads settings.txt

mode the mode for testing & evaluating in mode_list = ['single_shot', 'multi_shot']

re_rank whether to re-rank or not, note that it takes 3 minutes and at least 15GB memory(at peak) to re-rank

Display training loss curves, CMC curves and rank lists of selected queries in components/visualizer.py.

Use Tensorboard for rendering.

Start service: tensorboard --logdir=runs

View the results: http://localhost:6006/

Read from: analysis/train.mat, analysis/test.mat, analysis/evaluate.mat

Write to: runs/market1501/SummaryWriterLogFiles

Visualizer(self, config, name, model, pretrain, epoch, split, scene, query_list: list, length: int)

config a ConfigParser object which reads settings.txt

name a dataset's name in name_list = ['market1501']

model a model in model_list = ['resnet50']

pretrain a pretrain option in [True, False]

epoch the epoch of the saved trained model for visualizing

split a training set split method in split_list = ['train_only', 'train_val']

scene the scene for testing & evaluating & visualizing in scene_list = ['single_shot', 'multi_shot']

query_list a list of query indices

length the length of ranking list

models/resnet_epochNum.pth: model's static dictionary

analysis/train.mat: the dictionary of training loss

dictionary = {
    'training_loss': [[loss@epoch1 loss@epoch2 ...]](ndarray),
    'training_error': [[error@epoch1 error@epoch2 ...]](ndarray), 
    (if applicable)'validation_loss': [[loss@epoch1 loss@epoch2 ...]](ndarray),
    (if applicable)'validation_error': [[error@epoch1 error@epoch2 ...]](ndarray)
}

analysis/test.mat: the dictionary of gallery and query info

dictionary = {
    'gallery_feature': [[dim1 dim2 ... in index1] [dim1 dim2 ... in index2] ...](ndarray),
    'gallery_label': [[label1 label1 label2 ...]](ndarray),
    'gallery_cam': [[cam1 cam1 cam2 ...]](ndarray),
    'query_feature': [[dim1 dim2 ... in index1] [dim1 dim2 ... in index2] ...](ndarray),
    'query_label': [[label1 label1 label2 ...]](ndarray),
    'query_cam': [[cam1 cam1 cam2 ...]](ndarray), 
    (if applicable)'multi_index': [[[[index1 index2 index3 ...]] [[index4 index5 index6 ...]] ...]](ndarray)
}

analysis/evaluate.mat: the dictionary of evaluation indicators and rank lists

dictionary = {
    'index': [[[[index9 index11 index3 ...]] [[index1 index5 index9 ...]] ...]](ndarray),
    'ap': [[ap1 ap2 ap3 ...]](ndarray),
    'CMC': [[cmc1 cmc2 cmc3 ...]](ndarray)
}

1. This README is also in the manner of the README in Person_reID_baseline_pytorch
2. Use only 50 lines to train on many ReID datasets and get decent results in Torchreid: Deep learning person re-identification in PyTorch
3. Another advanced library for ReID in Open source person re-identification library in python