HHCL-ReID
fast-reid
HHCL-ReID | fast-reid | |
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1 | 3 | |
133 | 3,281 | |
- | 0.9% | |
0.0 | 1.2 | |
almost 2 years ago | 5 months ago | |
Python | Python | |
- | Apache License 2.0 |
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HHCL-ReID
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Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification
Unsupervised person re-identification (Re-ID) is a promising and very challenging research problem in computer vision. Learning robust and discriminative features with unlabeled data is of central importance to Re-ID. Recently, more attention has been paid to unsupervised Re-ID algorithms based on clustered pseudo-label. However, the previous approaches did not fully exploit information of hard samples, simply using cluster centroid or all instances for contrastive learning. In this paper, we propose a Hard-sample Guided Hybrid Contrast Learning (HHCL) approach combining cluster-level loss with instance-level loss for unsupervised person Re-ID. Our approach applies cluster centroid contrastive loss to ensure that the network is updated in a more stable way. Meanwhile, introduction of a hard instance contrastive loss further mines the discriminative information. Extensive experiments on two popular large-scale Re-ID benchmarks demonstrate that our HHCL outperforms previous state-of-the-art methods and significantly improves the performance of unsupervised person Re-ID. The code of our work is available soon at https://github.com/bupt-ai-cz/HHCL-ReID.
fast-reid
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DeepSort with PyTorch(support yolo series)
fast-reid
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Assign ID and track moving object with optical flow
On failure, you can try using a re-identification methods like FastReid: https://github.com/JDAI-CV/fast-reid in combination with your detector. A good pipeline that combines everything you seem to need is here: https://github.com/GeekAlexis/FastMOT. It uses a combination of Yolov4 (detector) + Kalman filters, Optical flow (tracker) and FastReid (re-identification)
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What are some alternatives?
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
l2rpn-baselines - L2RPN Baselines a repository to host baselines for l2rpn competitions.
pytorch-metric-learning - The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
DOLG-pytorch - Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"
apex-configs-by-deafps - Apex config & tweaks
Hekate-Toolbox - A toolbox for Hekate
pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch
pymarl2 - Fine-tuned MARL algorithms on SMAC (100% win rates on most scenarios)
PPYOLOE_pytorch - An unofficial implementation of Pytorch version PP-YOLOE,based on Megvii YOLOX training code.
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/