Hugging Face's state-of-the-art library for diffusion-based image, video, and audio generation models.

Fast and flexible image augmentation library with 70+ transforms for computer vision model training.

The world's most popular computer vision library with Python bindings — image processing, video, and ML pipelines.

The largest collection of pretrained image models for PyTorch — ViT, ConvNeXt, EfficientNet, Swin, and 900+ more.

Meta's Segment Anything Model 2 — real-time promptable segmentation for images and videos with streaming memory.

Industrial-grade multilingual OCR toolkit supporting 80+ languages with text detection, recognition, and layout analysis.

Google's on-device ML framework for face, hand, pose, and object detection across mobile, desktop, web, and edge.

Production-grade Stable Diffusion studio with a node-based workflow editor, ControlNet, IP-Adapter, and full REST API.

Meta AI's promptable image segmentation model that can segment any object from a single click or bounding box.

One-line OCR for 80+ languages including Latin, Chinese, Arabic, Devanagari, and Cyrillic — no training required.

Meta AI's modular object detection platform supporting Mask R-CNN, Faster R-CNN, DETR, and panoptic segmentation.

Roboflow's reusable computer vision utilities for annotation, tracking, and visualising detection model outputs.

Combines Grounding DINO and Segment Anything for text-prompt-driven object detection and precise segmentation in one pipeline.

OpenMMLab's comprehensive object detection toolbox with 40+ architectures and 300+ pretrained models.

Meta's self-supervised ViT model producing universal visual features for classification, segmentation, depth estimation, and retrieval.

OpenMMLab's unified semantic segmentation toolbox with 40+ architectures (DeepLab, SegFormer, Mask2Former) and 250+ pretrained models.

Differentiable computer vision library built on PyTorch: geometry, augmentation, colour, filtering, feature extraction, and more.

Open-set object detector that grabs any object by text description — no class list needed at inference time.

Foundation model for monocular depth estimation with state-of-the-art relative and metric depth from a single image.