Jinming Su's starred repositories
awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT better.
Grounded-Segment-Anything
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
diffusionbee-stable-diffusion-ui
Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
InternGPT
InternGPT (iGPT) is an open source demo platform where you can easily showcase your AI models. Now it supports DragGAN, ChatGPT, ImageBind, multimodal chat like GPT-4, SAM, interactive image editing, etc. Try it at igpt.opengvlab.com (支持DragGAN、ChatGPT、ImageBind、SAM的在线Demo系统)
learning-threejs
Code repository for the examples from the Packt book "Learning Threejs"
Segment-and-Track-Anything
An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) for key-frame segmentation and Associating Objects with Transformers (AOT) for efficient tracking and propagation purposes.
InternImage
[CVPR 2023 Highlight] InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
awesome-segment-anything
Tracking and collecting papers/projects/others related to Segment Anything.
Multimodal-GPT
Multimodal-GPT
awesome-multiple-object-tracking
Resources for Multiple Object Tracking (MOT)
SegmentAnythingin3D
Segment Anything in 3D with NeRFs (NeurIPS 2023)
ChatGPT-Web
使用GPT-3.5 API创建的ChatGPT聊天页面,支持云部署,多用户使用,多对话管理,公式显示,流式动态显示,windows和linux均可极简部署,网页版 html python flask
RelateAnything
Relate Anything Model is capable of taking an image as input and utilizing SAM to identify the corresponding mask within the image.
Video-K-Net
[CVPR-2022 (oral)]-Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation
VIPSeg-Dataset
CVPR2022: Large-scale Video Panoptic Segmentation in the Wild: A Benchmark