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🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
Datasets, Transforms and Models specific to Computer Vision
An open source implementation of CLIP.
Low-code framework for building custom LLMs, neural networks, and other AI models
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
An agent skill that finds where a photo was taken — OpenStreetMap geometry, elevation skylines, satellite imagery and street view — and shows its work. Works with Claude Code, Codex, Cursor, Gemini CLI, OpenCode and GitHub Copilot.
:earth_americas: Simple and ready-to-use tutorials for TensorFlow
A python library for self-supervised learning and latent world models on images.
OpenMMLab Foundational Library for Training Deep Learning Models
Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.
Upscale an image by a factor of 4, while generating photo-realistic details.
Build fully-functioning computer vision models with PyTorch
[IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
The PASS dataset: pretrained models and how to get the data
Agent skill: outline, mask and count objects in photos with Meta's SAM 3 on your own NVIDIA GPU, with review, exports and a viewer. Works with Claude Code, Codex, Cursor, Pi and other Agent Skills clients.
A foundation model-driven whole slide image-scale cell phenotyping method with QuPath integration Resources