OpenMMLab Detection Toolbox and Benchmark
-
Updated
Jun 27, 2024 - Python
OpenMMLab Detection Toolbox and Benchmark
Images to inference with no labeling (use foundation models to train supervised models).
Must-have resource for anyone who wants to experiment with and build on the OpenAI vision API 🔥
API for Grounding DINO 1.5: IDEA Research's Most Capable Open-World Object Detection Model Series
👁️ + 💬 + 🎧 = 🤖 Curated list of top foundation and multimodal models! [Paper + Code + Examples + Tutorials]
A tab for sd-webui for replacing objects in pictures or videos using detection prompt
GroundedSAM Base Model plugin for Autodistill
Generative AI based image editing/inpainting made super easy to work with.
SegMate: A Segmentation Toolkit
Grounding DINO module for use with Autodistill.
Dashboard for Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
Low-latency ONNX and TensorRT image segmentation with contrastive language-image pre-training based prompts
Synthetic dataset generation with Stable Diffusion and generating of segmentation mask using Grounding DINO and Segment Anythin Model
AI Image Cutout Maker is a project that uses artificial intelligence to automatically create cutouts from images. This project is designed to simplify the process of creating cutouts, which can be a time-consuming task if done manually. This project utilizes the power of Segment Anything and Grounding Dino AI models to detect subjects in an image
Explore the cutting edge of computer vision with this comprehensive repository, showcasing a spectrum from classical machine learning to state-of-the-art transformer models.
A Step-by-Step Guide to Augmenting Digitized Historical Images, Semester project @ CVlab & EPFL+ECAL Lab
A project to combine Grounding-DINO with Meta AI's Segment Anything Model (SAM) and Stable Diffusion for image manipulation using prompts. The plan is to integrate these techniques and deploy the model on Hugging Face with a Gradio interface for users to detect, segment regions and inpaint them in images.
Autodectify: Detect and Export Objects with Zero-Shot Object Detection Models
This project explores the intersection of NLP and CV, showcasing the potential of leveraging three powerful models – SAM, Stable Diffusion, and Grounding DINO – to edit manipulate images through textual commands.
Combining three computer vision foundation models, Segment Anything Model (SAM), Stable Diffusion, and Grounding DINO, to edit and manipulate images.
Add a description, image, and links to the grounding-dino topic page so that developers can more easily learn about it.
To associate your repository with the grounding-dino topic, visit your repo's landing page and select "manage topics."