Rottinson's starred repositories
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
annotated_deep_learning_paper_implementations
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
chatGPTBox
Integrating ChatGPT into your browser deeply, everything you need is here
Deformable-ConvNets
Deformable Convolutional Networks
EDSR-PyTorch
PyTorch version of the paper 'Enhanced Deep Residual Networks for Single Image Super-Resolution' (CVPRW 2017)
Awesome-Super-Resolution
Collect super-resolution related papers, data, repositories
Smart_Construction
Base on YOLOv5 Head Person Helmet Detection on Construction Sites,基于目标检测工地安全帽和禁入危险区域识别系统,🚀😆附 YOLOv5 训练自己的数据集超详细教程🚀😆2021.3新增可视化界面❗❗
Deformable-Convolution-V2-PyTorch
Deformable ConvNets V2 (DCNv2) in PyTorch
awesome-ncnn
😎 A Collection of Awesome NCNN-based Projects
Invertible-Image-Rescaling
[ECCV 2020, IJCV 2022] Invertible Image Rescaling
SCUT-HEAD-Dataset-Release
SCUT HEAD is a large-scale head detection dataset, including 4405 images labeld with 111251 heads.
alibabacloud-quantization-networks
alibabacloud-quantization-networks
Recent-Image-Quality-Related-Papers
A list of image quality related papers published in top conferences and journals
LinearityIQA
[official] Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment (ACM MM 2020)
DualCNN-TF
Tensorflow based Implementation of the CVPR2018 paper: Learning Dual Convolutional Neural Networks for Low-Level Vision
The-PaddleX-QT-Visualize-GUI
The-PaddleX-QT-Visualize-GUI
siti-tools
SI TI calculation tools
Content-Weighted-Image-Compression
PyTorch implementation of Learning Convolutional Networks for Content-Weighted Image Compression
MetaSR-PyTorch
PyTorch implements `Meta-SR: A Magnification-Arbitrary Network for Super-Resolution` paper