Runs one-command evaluation of vision-language models across 80+ multimodal benchmarks, handling data download, inference, and metric scoring in a single pass. Supports 220+ LMMs; adding a new model means writing one generate_inner() function.
Provides a diffusion-model studio for image, video, audio-video, editing, LoRA, and full training workflows so many model families share one inference and training framework.
Estimates and tracks 6D poses of novel objects without per-object fine-tuning — supports both model-based (CAD) and model-free (few reference images) setups. Trained on large-scale synthetic data with a transformer-based architecture and contrastive learning; CVPR 2024 highlight with demos and pretrained weights.
Performs document OCR, layout analysis, reading-order detection and table recognition across 90+ languages using a ~650M-parameter vision–language model; offers per-page and per-block modes and supports GPU (vllm) and CPU/Apple Silicon backends.
A family of GUI agents that operate phones, desktops, and browsers by perceiving the screen visually rather than reading app code. Ships open GUI-Owl vision-language models (7B/32B) plus a multi-agent framework for planning, reflection, and tool use.
A PyTorch-native, hardware-agnostic stack for robot learning: data collection, training, and deployment across 11+ robots, from SO100 to Unitree G1. Includes imitation, RL, and vision-language-action policies (ACT, Diffusion, Pi0, SmolVLA).
Pocket-sized multimodal LLM for efficient image- and video-understanding on mobile and edge devices, featuring mixed 4x/16x visual-token compression (MiniCPM‑V 4.6), compact 1.3B variants, and ready guides for iOS/Android/HarmonyOS deployment.
Runs GPT-4o-class vision, speech, and full-duplex audio-video conversation on a 9B model small enough to deploy on phones and tablets. The 4.5 release scores 77.6 on OpenCompass and adds real-time bilingual voice with voice cloning.
Produces real-time 3D reconstructions from multi-view images using Gaussian splatting, with on-device training and interactive viewing across native desktops, Android, and the browser. Uses WebGPU and the Burn ML framework to ship dependency-free binaries, a CLI, live training visualization, and streaming .ply support.
Automates browser workflows using LLMs and computer vision instead of XPath selectors, so it works on unseen sites and survives layout changes. Drive tasks with natural-language prompts: act, extract, validate. Handles 2FA and multi-step flows.
Provides local inference, fine-tuning, and a server/CLI for vision–language and omni (image/audio/video) models via MLX. Supports multi-image chat, audio/video inputs, activation quantization (CUDA), TurboQuant KV cache, and LoRA/QLoRA fine-tuning for on-device workflows.
Segments each PDF page into 11 labeled regions — titles, tables, formulas, figures, footnotes and more — and recovers reading order. Offers two engines: an accurate VGT visual model (~0.96 F1) or a faster CPU-only LightGBM ensemble.