Open Models for Detection and Segmentation
Open models for spotting objects, segmenting images and captioning them, including YOLO, Segment Anything 2 and CLIP.
An open-source image and text model for local image classification without task-specific training. Runs through PyTorch on CPU or CUDA GPUs under MIT.
34.4KUpdated 6 months agoMIT
#Batch processing#Multimodal input
CLIP is an open-source image and text model that lets developers and researchers classify images using labels written in natural language, without collecting training examples for each task. It runs locally through PyTorch on a CPU or CUDA GPU. The code and model weights use the MIT license.
Computer Vision ModelsEmbedding and Reranker ModelsVision-Language Models+0
An open-source object detection model that finds objects using text prompts. Run it locally with PyTorch on a CPU or NVIDIA GPU. Apache 2.0 licensed.
10.6KUpdated 2 years agoApache-2.0
Docker · Web#Hugging Face integration#Multimodal input
Grounding DINO finds objects in images using category names or descriptive phrases you supply. It's a local AI model for developers and computer vision researchers who need detection beyond a fixed set of labels, including people building dataset annotation tools.
Computer Vision ModelsVision-Language Models+0
Open-source Python library for object detection and segmentation, with pretrained models and deployment exports. Licensed under Apache 2.0.
34.7KUpdated 2 days agoApache-2.0
Detectron2 is an open-source Python library for developers and researchers building computer vision applications. It provides algorithms for locating objects in images and segmenting image regions, with support for training models and building research projects on top of the library. Facebook AI Research developed it as the successor to Detectron and maskrcnn-benchmark.
Computer Vision ModelsInference Libraries and BindingsTraining Frameworks+0
Face analysis toolkit for self-hosted recognition on CPU or NVIDIA GPU, local video face redaction, and commercially licensed models.
29.9KUpdated 3 weeks ago
macOS · Linux · iOS · Android · Web#Image-to-image#ONNX#Quantization
InsightFace is a face analysis toolkit for developers and teams building identity verification, access control, or face editing software. The code uses the MIT license. Its Python tools and self-hosted recognition server run inference on your own hardware. It also offers commercial models and API access for face swapping and deepfake detection.
Computer Vision ModelsFace Swap and PortraitsInference Libraries and BindingsInference Servers and Clusters+0
An open-source vision model that runs locally with PyTorch and Hugging Face Transformers, supports CPU or CUDA GPUs, and uses the MIT license.
huggingface.coComputer Vision Models
#Hugging Face integration#Multimodal input#Structured output
Florence-2 is Microsoft's open-source vision model for developers who want to process images on their own hardware. It handles several image tasks through text prompts, so one model can generate descriptions, read text and locate objects. It runs locally with PyTorch and Hugging Face Transformers on a CPU or CUDA GPU, and uses the MIT license.
Computer Vision ModelsOCR and Document ScanningVision-Language Models+0
An open-weight vision model for image questions, captions and object detection. Run it locally or use hosted inference and fine-tuning.
10.1KUpdated 5 months agoApache-2.0
macOS · Windows · Linux#Hugging Face integration#Multimodal input#Works offline
Moondream is a vision model for developers building software that needs to understand images. It can answer questions about a picture, write captions, locate objects, identify points and segment regions. The open-weight models can run on your own hardware, including in an air-gapped environment. The repository code is licensed under Apache 2.0; check each model checkpoint’s own terms for use.
Computer Vision ModelsEdge and Embedded AIInference Libraries and BindingsVision-Language Models+0
Open-source vision-language models for visual chat, document questions and image retrieval, with downloadable weights and Hugging Face Transformers support.
10.2KUpdated 1 year agoMIT
#Hugging Face integration#Multimodal input
InternVL is a family of downloadable vision-language models for developers and researchers building AI that can interpret images and discuss them in text. It combines visual recognition with language models, supporting both multimodal chat and tasks such as image classification and image-text retrieval.
Computer Vision ModelsVision-Language Models+0
An open-source segmentation model for images and videos. Run it on your own GPU machine, track objects across frames, and refine masks with prompts.
19.9KUpdated 2 years agoApache-2.0
Web#Hugging Face integration
Segment Anything 2 (SAM 2) is Meta's open-source model for selecting objects in images and tracking them through video. It's for developers and researchers who need object masks for visual applications or dataset annotation. The model and its web demo can run on your own GPU machine; Meta also provides a hosted demo.
Computer Vision ModelsData Labeling and Annotation+0
An open source OCR toolkit that runs locally, converts PDFs and images to Markdown or JSON, and supports multilingual text under Apache 2.0.
90.4KUpdated 2 weeks agoApache-2.0
Web#Multilingual#ONNX#Structured output
PaddleOCR is an open source OCR and document parsing toolkit for developers building document search, RAG systems and AI agents. It runs on your own hardware or a self-hosted server and turns PDFs and images into structured Markdown or JSON. The Python toolkit uses PaddlePaddle and carries the Apache 2.0 license.
Computer Vision ModelsDocument Parsing for LLMsOCR and Document ScanningVision-Language Models+0
Open-source computer vision library for local detection, segmentation and tracking, with AGPL-3.0 licensing and exports to ONNX, TensorRT and CoreML.
62.1KUpdated 23 hours agoAGPL-3.0
#ONNX
Ultralytics YOLO is an open-source Python computer vision library for developers building applications that analyze images and video on their own hardware. It supports local and edge deployment, including NVIDIA Jetson, Raspberry Pi and mobile phones. A separate hosted platform provides browser-based annotation, cloud GPU training and managed prediction endpoints.
Computer Vision ModelsEdge and Embedded AIInference Libraries and BindingsTraining Frameworks+0
