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MrLee12138
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Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
PyTorch Tutorial for Deep Learning Researchers
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Effortless data labeling with AI support from Segment Anything and other awesome models.
PyTorch ,ONNX and TensorRT implementation of YOLOv4
OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.
A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset
Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"
experiments on Paper <Bag of Tricks for Image Classification with Convolutional Neural Networks> and other useful tricks to improve CNN acc
A flexible, effective and fast cross-view gait recognition network
A PyTorch implementation of MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis
Yolov5+SlowFast: Realtime Action Detection Based on PytorchVideo
Official PyTorch implementation of "A Comprehensive Overhaul of Feature Distillation" (ICCV 2019)
天池2019广东工业智造创新大赛 布匹疵点检测 天池水也太深了 季军解决方案
The official PyTorch implementation of CVPR 2020 paper "Improving Convolutional Networks with Self-Calibrated Convolutions"
This is the implementation of YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design
Deep learning models for change detection of remote sensing images
Temporal part-based model for gait recognition.
A Comprehensive Study on Cross-View Gait Based Human Idendification with Deep CNNs
An Implementation of GaitPart: Temporal Part-based Model for Gait Recognition
🎨 Pytorch YOLO v4 训练自己的数据集超详细教程！！！ 🎨(提供PDF训练教程下载）