每个推荐都保留与其仓库、审计和安装路径的明确关联。
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
Largest multi-label image database; ResNet-101 model; 80.73% top-1 acc on ImageNet
Video classification tools using 3D ResNet
Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).
DeepLab resnet v2 model in pytorch
This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based on accelerate/PyTorch, as well as scripts for experiments with CIFAR-10 and Tiny ImageNet.
A guide to transfer learning with inception-resnet-v2.
Google DeepLab V3 for Image Semantic Segmentation
每个推荐都保留与其仓库、审计和安装路径的明确关联。
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
Largest multi-label image database; ResNet-101 model; 80.73% top-1 acc on ImageNet
Video classification tools using 3D ResNet
Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).
DeepLab resnet v2 model in pytorch
This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based on accelerate/PyTorch, as well as scripts for experiments with CIFAR-10 and Tiny ImageNet.
A guide to transfer learning with inception-resnet-v2.
Google DeepLab V3 for Image Semantic Segmentation
每个推荐都保留与其仓库、审计和安装路径的明确关联。
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
Largest multi-label image database; ResNet-101 model; 80.73% top-1 acc on ImageNet
Video classification tools using 3D ResNet
Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).
DeepLab resnet v2 model in pytorch
This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based on accelerate/PyTorch, as well as scripts for experiments with CIFAR-10 and Tiny ImageNet.
A guide to transfer learning with inception-resnet-v2.
Google DeepLab V3 for Image Semantic Segmentation
每个推荐都保留与其仓库、审计和安装路径的明确关联。
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
Largest multi-label image database; ResNet-101 model; 80.73% top-1 acc on ImageNet
Video classification tools using 3D ResNet
Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).
DeepLab resnet v2 model in pytorch
This project is dedicated to the implementation and research of Kolmogorov-Arnold convolutional networks. The repository includes implementations of 1D, 2D, and 3D convolutions with different kernels, ResNet-like and DenseNet-like models, training code based on accelerate/PyTorch, as well as scripts for experiments with CIFAR-10 and Tiny ImageNet.
A guide to transfer learning with inception-resnet-v2.
Google DeepLab V3 for Image Semantic Segmentation