83 results for “topic:sam2”
Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything (SAM+SAM2/2.1+SAM3), MobileSAM!!
Labeling tool with SAM(segment anything model),supports SAM, SAM2, SAM3, sam-hq, MobileSAM EdgeSAM etc.交互式半自动图像标注工具
Tailor是一款视频智能裁剪、视频生成和视频优化的视频剪辑工具。目前的目标是通过人工智能技术减少视频剪辑的繁琐操作，让普通人也能简单实现专业剪辑人的水准！长远目标是让视频剪辑实现真正的AIGC！
[CVPR 2025] Official PyTorch implementation of "EdgeTAM: On-Device Track Anything Model"
[CVPR 2025] Code for Segment Any Motion in Videos
SimpleAICV:pytorch training examples.
The code for PixelRefer & VideoRefer
Video-Inpaint-Anything: This is the inference code for our paper CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility.
Grounded Tracking for Streaming Videos
Playground Web UI using segment-anything-2 models from the Meta.
An open-source studio for prompt-driven video segmentation. Powered by SAM2 & Grounding DINO with a hybrid Cloud-Local architecture.
go-vision 基于 Golang + ONNX 构建的视觉库，支持 SAM2、YOLOv11-Det、YOLOv11-Seg、YOLOv11-Cls、YOLOv11-Pose、YOLOv11-OBB、YOLO26-Det、YOLO26-Seg、YOLO26-Cls、YOLO26-Pose、YOLO26-OBB 等模型
A gradio based webui for meta segment-anything-model 2 (SAM2), both image and video are supported
🎨 Add text overlays to segmented objects in your images using AI. Powered by Meta's SAM2 for segmentation, running entirely in your browser. Perfect for creating memes, social media content, and creative image editing. No backend required!
A cutting-edge deep learning project that combines YOLOv11 (for real-time object detection) with SAM2 (Segment Anything Model) to accurately detect and segment tumors in medical images. Designed for high precision in healthcare diagnostics and research applications.
SAM2 Track implementation with TensorRT & OnnxRuntime
Ultralytics VSCode snippets plugin to provide quick examples and templates of boilerplate code to accelerate your code development and learning.
No description provided.
SASVi - Segment Any Surgical Video (IPCAI 2025)
Run Segment Anything 2 (SAM 2) on macOS using Core ML models
Simple Video Summarization using Text-to-Segment Anything (Florence2 + SAM2) This project provides a video processing tool that utilizes advanced AI models, specifically Florence2 and SAM2, to detect and segment specific objects or activities in a video based on textual descriptions.
Automated batch processing of cell and tissue microscopy image data in Napari
Implementation of CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation.
Simple video editor wrapping ffmpeg and SAM2
TensorRT in Practice: Model Conversion, Extension, and Advanced Inference Optimization
Image segmentation application that utilizes the SAM2 (Segment Anything Model) via API to perform object detection and segmentation on uploaded images.
(SegmentedOWLv2) is a powerful command-line tool for text-prompted object segmentation for video and images.
This repository demonstrates the use of **SAM2 (Segment Anything Model 2)** to automatically generate object masks for images. SAM2 efficiently processes prompts to generate masks by sampling over the entire image and predicting multiple masks from single-point input prompts.
A system for barcode detection and decoding using YOLO, SAM2, and pyzbar designed to extract barcode data from images.
PEANUT (Prompt-Enhanced Ablation with Optical Flow-Based Neural Unit) designed to enhance video restoration by combining spatial and temporal consistency with clarity optimization. The core innovation lies in Prompt-Guided Mask Self-Generation and leveraging optical flow-based neural units to generate high-fidelity video sequences