There are a couple popular deep learning based ReID frameworks such as torchreid and fastreid.
There projects are very helpful for benchmarking SOTA methods as well as implementing new ideas quickly.
For my personal projects, I've been heavily using these projects.
The one problem that reduced my productivity was how I needed to add configuration defaults for every module that I added.
Inspired by OpenMMLab's projects, I created my own modular framework that uses mmcv that significantly reduced this complexity.
Key points:
- Customisable: experiments can be configured easily with the help of
mmcv; no more bloated configs!peppermodules can be integrated into other projects such asmmclsandmmdet. - Scalable: add modules easily with "registries" and implement new ideas quickly without the hassle of breaking things
- Fast: training and evaluations are done using distributed processing
- Robust: borrows and implements techniques from other projects
Other features:
- supports image and video ReID
- supports various datasets (including MOT datasets)
- supports cross-dataset evaluation
- supports training on multiple datasets
- multi-process multi-gpu distributed training
- separate dataset preparation scripts
- etc...
Notes:
- I will get around to creating a detailed documentation later, but for now please read the code or reference similar frameworks such as
mmclsandmmdet. - Please open issues or PR if you spot any bugs or improvements.
Clone the project:
git clone --recursive git@github.com:haruishi43/pepper.git
cd peppertorchandtorchvisionmmcvfaiss-gpu
Other dependencies can be installed using the following command:
pip install -r requirements.txtTwo options:
- Install
pepperas a global library:
python setup.py develop
# or
pip install -e .- Install locally:
No need to run any commands except for when you want the optimized evaluation functionality:
cd pepper/core/evaluation/rank_cylib; make allCUDA_VISIBLE_DEVICES=<gpu_ids> ./tools/dist_train.sh <config> <num_gpus>- README
- Documentation
- Upload model weights
- Test codes
- PyPI installation
- Update to 1.0