Brings classic computer vision into PyTorch as differentiable, GPU-accelerated tensor operators — filters, geometric transforms, feature matching, camera calibration — so each step lives inside autograd and trains end-to-end with neural networks.
Bundles hundreds of pretrained image backbones — ResNet, EfficientNet, ViT, ConvNeXt, Swin and more — behind one consistent API for classification and feature extraction, with training and inference scripts that reproduce published ImageNet results.
Modular implementations of object detection, instance/semantic/panoptic segmentation and related vision models for research and deployment. Offers a large model zoo, export to TorchScript/Caffe2, and PyTorch-native optimizations for faster training and extensibility.
PyTorch object detector built for shipping: train on your own data, then export to ONNX, CoreML, TFLite, or TensorRT with one command. Comes in five sizes (n/s/m/l/x) and adds instance-segmentation and classification heads beyond bounding-box detection.
Learns a joint image–text embedding via contrastive pretraining to enable zero-shot image classification. Uses a ViT‑B/32 image encoder and transformer text encoder; intended primarily for research into robustness and generalization, not untested deployment.
Library for benchmarking, developing, and deploying deep-learning visual anomaly-detection models — includes ready-to-use model implementations (PatchCore, DINO-based), experiment/HPO tooling, OpenVINO export for edge inference, and a low-code Studio for deployment.
Contains 73 images used in Hugging Face library documentation, packaged in Parquet and imagefolder formats for examples, demos, and small-scale image experiments; distributed under CC BY‑NC‑SA 4.0.
Offline desktop OCR for Windows and Linux that extracts text from screenshots, image batches, and scanned PDFs without requiring a network connection. Bundles multilingual offline engines (PaddleOCR / RapidOCR), supports ignore-regions, searchable PDF output, CLI and HTTP interfaces for automation and integration.
Interactive IDE for composing AI-generated 2D and 3D scenes and producing images and videos. Provides visual scene-blocking, 3D image compositing, character posing, image-to-3D conversion, and multi-provider model integrations to make generation consistent and repeatable.
Canonical ILSVRC ImageNet-1k for 1,000-way image classification — provides roughly 1.2M labeled images (train/val/test) packaged as optimized Parquet for easy loading with Hugging Face Datasets, Dask, and Polars. Verify licensing and distribution constraints before use.
Runs pretrained diffusion models for image, video, and audio generation through composable pipelines. It separates pipelines, schedulers, models, adapters, and memory optimizations so teams can prototype quickly without locking into one model family.
Enlarges and enhances low-resolution images using AI models (Real-ESRGAN) through a cross-platform desktop app. Runs on a local NCNN/Vulkan backend (requires a Vulkan-compatible GPU), offers an Electron GUI plus a CLI backend (upscayl-ncnn), and supports custom models for different image types.