Releases: JDAI-CV/fast-reid
Releases · JDAI-CV/fast-reid
V1.3.0
Features & Improvements
New Features
- Vision Transformer backbone, see config in
configs/Market1501/bagtricks_vit.yml
- Self-Distillation with EMA update
- Gradient Clip
Improvements
- Faster dataloader with pre-fetch thread and cuda stream
- Impl
freezebackbone
function in step of optimizer - Optimize DDP training speed by removing
find_unused_parameters
in DDP
v1.0.0
Features & Improvements:
- Built-in distillation and memory bank are supported.
- Add automatically hyper-parameters optimization is
projects/FastTune
. - Add some extension tasks in
projects/
, including face recogntion, attributed recognition, image classification, overhaul distillation and image retrieval. - Support mixed precision training (using
cfg.SOLVER.FP16_ENABLED
) powered byapex
. - Accelerate re-rank with
faiss-gpu
. - Change lr scheduler by epoch, and warmup by iter.
v0.1.1
Some major features:
-
Distributed DataParallel(DDP) training and evaluation.
-
Model distillation.
-
Caffe/ONNX/TensoRT deployment.
-
Pretrained model release.
v0.1
Some major features:
-
High modular design.
-
SoTA results on conventional person re-id, cross-domain person re-id, partial person re-id, and vehicle re-id.
-
multi-GPU training and multi-dataset testing simultaneously
-
standard dataset splits used by the most paper.
-
visualization methods including rank list and annotation visualization.
-
abundant evaluation metrics including CMC, mAP, mINP, and ROC.