Release GaitLU-1M

README.md

@@ -3,24 +3,26 @@
 <div align="center"><img src="./assets/nm.gif" width = "100" height = "100" alt="nm" /><img src="./assets/bg.gif" width = "100" height = "100" alt="bg" /><img src="./assets/cl.gif" width = "100" height = "100" alt="cl" /></div>
 
 ------------------------------------------
+<!-- 📣📣📣 **[*GaitLU-1M*](https://ieeexplore.ieee.org/document/10242019) relseased, pls checking the [tutorial](datasets/GaitLU-1M/README.md).** 📣📣📣
 📣📣📣 **[*SUSTech1K*](https://lidargait.github.io) relseased, pls checking the [tutorial](datasets/SUSTech1K/README.md).** 📣📣📣
 
-🎉🎉🎉 **[*OpenGait*](https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf) has been accpected by CVPR2023 as a highlight paper！** 🎉🎉🎉
+🎉🎉🎉 **[*OpenGait*](https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf) has been accpected by CVPR2023 as a highlight paper！** 🎉🎉🎉 -->
 
 OpenGait is a flexible and extensible gait recognition project provided by the [Shiqi Yu Group](https://faculty.sustech.edu.cn/yusq/) and supported in part by [WATRIX.AI](http://www.watrix.ai).
 
 ## What's New
+- **[Nov 2023]** The first million-level unlabeled gait dataset, i.e., [GaitLU-1M](https://ieeexplore.ieee.org/document/10242019), is released and supported in [datasets/GaitLU-1M](datasets/GaitLU-1M/README.md).
 - **[Oct 2023]** Several representative pose-based methods are supported in [opengait/modeling/models](./opengait/modeling/models). This feature is mainly inherited from [FastPoseGait](https://github.com/BNU-IVC/FastPoseGait). Many thanks to the contributors😊.
 - **[July 2023]** [CCPG](https://github.com/BNU-IVC/CCPG) is supported in [datasets/CCPG](./datasets/CCPG).
 - **[July 2023]** [SUSTech1K](https://lidargait.github.io) is released and supported in [datasets/SUSTech1K](./datasets/SUSTech1K).
 - [May 2023] A real gait recognition system [All-in-One-Gait](https://github.com/jdyjjj/All-in-One-Gait) provided by [Dongyang Jin](https://github.com/jdyjjj) is available.
-- [Apr 2023] [CASIA-E](datasets/CASIA-E/README.md) is supported by OpenGait.
+<!-- - [Apr 2023] [CASIA-E](datasets/CASIA-E/README.md) is supported by OpenGait.
 - [Feb 2023] [HID 2023 competition](https://hid2023.iapr-tc4.org/) is open, welcome to participate. Additionally, the tutorial for the competition has been updated in [datasets/HID/](./datasets/HID).
 - [Dec 2022] Dataset [Gait3D](https://github.com/Gait3D/Gait3D-Benchmark) is supported in [datasets/Gait3D](./datasets/Gait3D).
-- [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported in [datasets/GREW](./datasets/GREW).
+- [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported in [datasets/GREW](./datasets/GREW). -->
 
 ## Our Publications
-- [**TPAMI 2023**] Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark, [*Paper*](https://ieeexplore.ieee.org/document/10242019), [*Dataset*](https://github.com/ChaoFan996/GaitSSB)(Coming soon), and [*Code*](opengait/modeling/models/gaitssb.py).
+- [**TPAMI 2023**] Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark, [*Paper*](https://ieeexplore.ieee.org/document/10242019), [*Dataset*](datasets/GaitLU-1M/README.md), and [*Code*](opengait/modeling/models/gaitssb.py).
 - [**CVPR 2023**] LidarGait: Benchmarking 3D Gait Recognition with Point Clouds, [*Paper*](https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.pdf), [*Dataset*](https://lidargait.github.io) and [*Code*](datasets/SUSTech1K/README.md).
 - [**CVPR 2023 Highlight**] OpenGait: Revisiting Gait Recognition Toward Better Practicality, [*Paper*](https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf), [*Code*](configs/gaitbase).
 - [**ECCV 2022**] GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality, [*Paper*](), [*Code*](configs/gaitedge/README.md).
@@ -34,7 +36,7 @@ The workflow of [All-in-One-Gait](https://github.com/jdyjjj/All-in-One-Gait) inv
 See [here](https://github.com/jdyjjj/All-in-One-Gait) for details.
 
 ## Highlighted features
-- **Mutiple Dataset supported**: [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp), [OUMVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html), [SUSTech1K](https://lidargait.github.io), [HID](http://hid2022.iapr-tc4.org/), [GREW](https://www.grew-benchmark.org), [Gait3D](https://github.com/Gait3D/Gait3D-Benchmark), [CCPG](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.pdf), and [CASIA-E](https://www.scidb.cn/en/detail?dataSetId=57be0e918db743279baf44a38d013a06).
+- **Multiple Dataset supported**: [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp), [OUMVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html), [SUSTech1K](https://lidargait.github.io), [HID](http://hid2022.iapr-tc4.org/), [GREW](https://www.grew-benchmark.org), [Gait3D](https://github.com/Gait3D/Gait3D-Benchmark), [CCPG](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.pdf), [CASIA-E](https://www.scidb.cn/en/detail?dataSetId=57be0e918db743279baf44a38d013a06), and [GaitLU-1M](https://ieeexplore.ieee.org/document/10242019).
 - **Multiple Models Support**: We reproduced several SOTA methods and reached the same or even better performance. 
 - **DDP Support**: The officially recommended [`Distributed Data Parallel (DDP)`](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) mode is used during both the training and testing phases.
 - **AMP Support**: The [`Auto Mixed Precision (AMP)`](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html?highlight=amp) option is available.

configs/gaitssb/finetune_casiab.yaml

@@ -0,0 +1,95 @@
+data_cfg:
+  dataset_name: CASIA-B
+  dataset_root: your_path
+  dataset_partition: ./datasets/CASIA-B/CASIA-B.json
+  num_workers: 1
+  remove_no_gallery: false # Remove probe if no gallery for it
+  dataset_name: CASIA-B
+
+evaluator_cfg:
+  enable_float16: true
+  restore_ckpt_strict: true
+  restore_hint: 12000
+  save_name: GaitSSB_Finetune
+  sampler:
+    batch_shuffle: false
+    batch_size: 4
+    sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options:   fixed_unordered
+    frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
+  metric: euc # cos
+  transform:
+    - type: BaseSilCuttingTransform
+
+loss_cfg:
+  - loss_term_weight: 1.0
+    margin: 0.3
+    type: TripletLoss
+    log_prefix: triplet
+
+model_cfg:
+  model: GaitSSB_Finetune
+  backbone_cfg:
+    type: ResNet9
+    block: BasicBlock
+    channels: # Layers configuration for automatically model construction
+      - 64
+      - 128
+      - 256
+      - 512
+    layers:
+      - 1
+      - 1
+      - 1
+      - 1
+    strides:
+      - 1
+      - 2
+      - 2
+      - 1
+    maxpool: false
+  parts_num: 31
+  backbone_lr: 
+    - 0. 
+    - 0.001
+    - 0.001
+    - 0.001
+  projector_lr: 0.01
+
+optimizer_cfg:
+  lr: 0.1
+  momentum: 0.9
+  solver: SGD
+  weight_decay: 0.0005
+
+scheduler_cfg:
+  gamma: 0.1
+  milestones: # Learning Rate Reduction at each milestones
+    - 6000
+    - 8000
+    - 10000
+  scheduler: MultiStepLR
+
+trainer_cfg:
+  find_unused_parameters: true
+  enable_float16: true # half_percesion float for memory reduction and speedup
+  fix_BN: true
+  with_test: false
+  log_iter: 100
+  optimizer_reset: true
+  restore_ckpt_strict: false
+  restore_hint: ./output/GaitLU-1M/GaitSSB_Pretrain/GaitSSB_Pretrain/checkpoints/GaitSSB_Pretrain-150000.pt
+  save_iter: 2000
+  save_name: GaitSSB_Finetune
+  sync_BN: true
+  total_iter: 12000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 8  # TripletSampler, batch_size[0] indicates Number of Identity
+      - 16 # batch_size[1] indicates Samples sequqnce for each Identity
+    frames_num_fixed: 30 # fixed frames number for training
+    sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
+    frames_skip_num: 4
+    type: TripletSampler
+  transform:
+    - type: BaseSilCuttingTransform

configs/gaitssb/finetune_grew.yaml

@@ -0,0 +1,96 @@
+data_cfg:
+  dataset_name: GREW
+  dataset_root: your_path
+  dataset_partition: ./datasets/GREW/GREW.json
+  num_workers: 1
+  remove_no_gallery: false # Remove probe if no gallery for it
+  test_dataset_name: GREW
+
+evaluator_cfg:
+  enable_float16: true
+  restore_ckpt_strict: true
+  restore_hint: 80000
+  save_name: GaitSSB_Finetune
+  eval_func: GREW_submission
+  sampler:
+    batch_shuffle: false
+    batch_size: 4
+    sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options:   fixed_unordered
+    frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
+  metric: euc # cos
+  transform:
+    - type: BaseSilCuttingTransform
+
+loss_cfg:
+  - loss_term_weight: 1.0
+    margin: 0.3
+    type: TripletLoss
+    log_prefix: triplet
+
+model_cfg:
+  model: GaitSSB_Finetune
+  backbone_cfg:
+    type: ResNet9
+    block: BasicBlock
+    channels: # Layers configuration for automatically model construction
+      - 64
+      - 128
+      - 256
+      - 512
+    layers:
+      - 1
+      - 1
+      - 1
+      - 1
+    strides:
+      - 1
+      - 2
+      - 2
+      - 1
+    maxpool: false
+  parts_num: 31
+  backbone_lr: 
+    - 0. 
+    - 0.001
+    - 0.001
+    - 0.001
+  projector_lr: 0.01
+
+optimizer_cfg:
+  lr: 0.1
+  momentum: 0.9
+  solver: SGD
+  weight_decay: 0.
+
+scheduler_cfg:
+  gamma: 0.1
+  milestones: # Learning Rate Reduction at each milestones
+    - 50000
+    - 60000
+    - 70000
+  scheduler: MultiStepLR
+
+trainer_cfg:
+  find_unused_parameters: true
+  enable_float16: true # half_percesion float for memory reduction and speedup
+  fix_BN: true
+  with_test: false
+  log_iter: 100
+  optimizer_reset: true
+  restore_ckpt_strict: false
+  restore_hint: ./output/GaitLU-1M/GaitSSB_Pretrain/GaitSSB_Pretrain/checkpoints/GaitSSB_Pretrain-150000.pt
+  save_iter: 20000
+  save_name: GaitSSB_Finetune
+  sync_BN: true
+  total_iter: 80000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 128  # TripletSampler, batch_size[0] indicates Number of Identity
+      - 4 # batch_size[1] indicates Samples sequqnce for each Identity
+    frames_num_fixed: 30 # fixed frames number for training
+    sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
+    frames_skip_num: 4
+    type: TripletSampler
+  transform:
+    - type: BaseSilCuttingTransform

configs/gaitssb/finetune_oumvlp.yaml

@@ -0,0 +1,95 @@
+data_cfg:
+  dataset_name: OUMVLP
+  dataset_root: your_path
+  dataset_partition: ./datasets/OUMVLP/OUMVLP.json
+  num_workers: 1
+  remove_no_gallery: true # Remove probe if no gallery for it
+  test_dataset_name: OUMVLP
+
+evaluator_cfg:
+  enable_float16: true
+  restore_ckpt_strict: true
+  restore_hint: 80000
+  save_name: GaitSSB_Finetune
+  sampler:
+    batch_shuffle: false
+    batch_size: 4
+    sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options:   fixed_unordered
+    frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory
+  metric: euc # cos
+  transform:
+    - type: BaseSilCuttingTransform
+
+loss_cfg:
+  - loss_term_weight: 1.0
+    margin: 0.3
+    type: TripletLoss
+    log_prefix: triplet
+
+model_cfg:
+  model: GaitSSB_Finetune
+  backbone_cfg:
+    type: ResNet9
+    block: BasicBlock
+    channels: # Layers configuration for automatically model construction
+      - 64
+      - 128
+      - 256
+      - 512
+    layers:
+      - 1
+      - 1
+      - 1
+      - 1
+    strides:
+      - 1
+      - 2
+      - 2
+      - 1
+    maxpool: false
+  parts_num: 31
+  backbone_lr: 
+    - 0.001
+    - 0.001
+    - 0.001
+    - 0.001
+  projector_lr: 0.01
+
+optimizer_cfg:
+  lr: 0.1
+  momentum: 0.9
+  solver: SGD
+  weight_decay: 0.005
+
+scheduler_cfg:
+  gamma: 0.1
+  milestones: # Learning Rate Reduction at each milestones
+    - 50000
+    - 60000
+    - 70000
+  scheduler: MultiStepLR
+
+trainer_cfg:
+  find_unused_parameters: true
+  enable_float16: true # half_percesion float for memory reduction and speedup
+  fix_BN: true
+  with_test: false
+  log_iter: 100
+  optimizer_reset: true
+  restore_ckpt_strict: false
+  restore_hint: ./output/GaitLU-1M/GaitSSB_Pretrain/GaitSSB_Pretrain/checkpoints/GaitSSB_Pretrain-150000.pt
+  save_iter: 20000
+  save_name: GaitSSB_Finetune
+  sync_BN: true
+  total_iter: 80000
+  sampler:
+    batch_shuffle: true
+    batch_size:
+      - 32  # TripletSampler, batch_size[0] indicates Number of Identity
+      - 16 # batch_size[1] indicates Samples sequqnce for each Identity
+    frames_num_fixed: 30 # fixed frames number for training
+    sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered
+    frames_skip_num: 4
+    type: TripletSampler
+  transform:
+    - type: BaseSilCuttingTransform

datasets/GaitLU-1M/README.md

@@ -6,14 +6,23 @@ GaitLU-1M is extracted from public videos shot around the world, making it cover
 
 This great diversity and scale offer an excellent chance to learn general gait representation in a self-supervised manner.
 
-## Download (Coming soon)
+## Download
-Download the dataset from [Baidu Yun]() or [OneDrive]() and decompress the file by following command:
+### Step1: 
-```shell
+Download the dataset from [Baidu Yun](https://pan.baidu.com/s/1aexoZY-deZFXSuyfOOjwJg) (code: 4rat) or [OneDrive](https://connecthkuhk-my.sharepoint.com/:f:/g/personal/noahshen_connect_hku_hk/EvFZrNKeV7VCgJKCaLay7T8Bv7TW5PHIcXWzv0XyFPliIA?e=9ZHkx9).
-unzip -P password GaitLU-1M.zip | xargs -n1 tar xzvf
-```
-To obtain the password, you should sign the [Release Agreement](./Release_Agreement.pdf) and [Ethical Requirement](./Ethical_Requirements.pdf) and send the signed documents to the administrator(12131100@mail.sustech.edu.cn). 
 
-Then you can get GaitLU-1M formatted as: 
+There are 6 sub-zip files and you can aggregate them by:
+```shell
+zip -F GaitLU_Anno_part.zip --out GaitLU_Anno.zip
+``` 
+
+### Step2: 
+Then, you can decompress the file by:
+```shell
+unzip -P password GaitLU_Anno.zip -d <output_folder>
+```
+To obtain the password, you should sign the [Release Agreement](./Release_Agreement.pdf) and [Ethical Requirement](./Ethical_Requirements.pdf) and send the signed documents to our administrator (12131100@mail.sustech.edu.cn). 
+
+Finally, you can get GaitLU-1M formatted as: 
 ```
 silhouette_cut_pkl
 ├── 000                           # Random number
@@ -27,12 +36,12 @@ silhouette_cut_pkl
 ```
 
 
-## Usage (Coming soon)
+## Usage
 For the training phase, you should modify the `dataset_root` in `configs/gaitssb/pretrain.yaml` and run the following command:
 ```shell
 CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs configs/gaitssb/pretrain.yaml --phase train --log_to_file
 ```
-The officially provided pretrained checkpoint can be found [here](). 
+The officially provided pretrained checkpoint can be found [here]() (Coming soon). 
 
 Then you can evaluate the pretrained model on labelled gait datasets by runing: 
 ```shell
@@ -60,12 +69,12 @@ If you use this dataset in your research, please cite the following paper:
 ```
 If you think OpenGait is useful, please cite the following paper:
 ```
-@misc{fan2022opengait,
+@InProceedings{Fan_2023_CVPR,
-      title={OpenGait: Revisiting Gait Recognition Toward Better Practicality}, 
+    author    = {Fan, Chao and Liang, Junhao and Shen, Chuanfu and Hou, Saihui and Huang, Yongzhen and Yu, Shiqi},
-      author={Chao Fan and Junhao Liang and Chuanfu Shen and Saihui Hou and Yongzhen Huang and Shiqi Yu},
+    title     = {OpenGait: Revisiting Gait Recognition Towards Better Practicality},
-      year={2022},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-      eprint={2211.06597},
+    month     = {June},
-      archivePrefix={arXiv},
+    year      = {2023},
-      primaryClass={cs.CV}
+    pages     = {9707-9716}
 }
 ```