JH's starred repositories
tennis_analysis
This project analyzes Tennis players in a video to measure their speed, ball shot speed and number of shots. This project will detect players and the tennis ball using YOLO and also utilizes CNNs to extract court keypoints. This hands on project is perfect for polishing your machine learning, and computer vision skills.
tennis-court-detection
Fully automatic algorithm for tennis court line detection.
anthropic-tokenizer
Approximation of the Claude 3 tokenizer by inspecting generation stream
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
vit-explain
Explainability for Vision Transformers
Transformer-MM-Explainability
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
Transformer-Explainability
[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
disentangling-vae
Experiments for understanding disentanglement in VAE latent representations
disentanglement_lib
disentanglement_lib is an open-source library for research on learning disentangled representations.
Multicore-TSNE
[outdated] see https://github.com/sg-s/tsne-wrappers
Awesome-Image-Quality-Assessment
A comprehensive collection of IQA papers
AGIQA-3k-Database
[IEEE TCSVT2023] A Fine-grained Subjective Perception & Alignment Database for AI Generated Image Quality Assessment
IQA-PyTorch
👁️ 🖼️ 🔥PyTorch Toolbox for Image Quality Assessment, including LPIPS, FID, NIQE, NRQM(Ma), MUSIQ, TOPIQ, NIMA, DBCNN, BRISQUE, PI and more...
Universal-Image-Embeddings
A large-scale benchmark for the evaluation of embeddings across a number of fine-grained and instance-level visual domains.
CLIP-visualization
Attention visualization in CLIP
CLIP_prefix_caption
Simple image captioning model
vit-interpret
Official implementation of "Interpreting and Controlling Vision Foundation Models via Text Explanations"