The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…

Neural Graph Differential Equations (Neural GDEs)

Graph Transformer Networks (Authors' PyTorch implementation for the NeurIPS 19 paper)

Graph Convolutional Networks in PyTorch

PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.

Modular Graph Transformer Networks

Video classification tools using 3D ResNet

PyTorch implementation of the AAAI-21 paper "Dual Adversarial Label-aware Graph Neural Networks for Cross-modal Retrieval" and the TPAMI-22 paper "Integrating Multi-Label Contrastive Learning with …

ADD-GCN: Attention-Driven Dynamic Graph Convolutional Network for Multi-Label Image Recognition (ECCV 2020)

code of Graph Attention Transformer Network for Multi-Label Image Classification

Tips for Writing a Research Paper using LaTeX

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"

Vision Transformer (ViT) in PyTorch

This is an official implementation for "Video Swin Transformers".

Video Swin Transformer - PyTorch

PyTorch implementation of SENet

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"

Plug in and Play Implementation of Tree of Thoughts: Deliberate Problem Solving with Large Language Models that Elevates Model Reasoning by atleast 70%

2nd place solution to ImageCLEF 2021 Tuberculosis - TBT classification task.

左程云的算法和数据结构通关课

3.8% and 18.3% on CIFAR-10 and CIFAR-100

This is a PyTorch implementation of the ECCV2018 paper "Learning to Navigate for Fine-grained Classification" (Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Liwei Wang).

Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"

[NeurIPS 2019] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss