OFA
Official repository of OFA (ICML 2022). Paper: OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework (by OFA-Sys)
GroundingDINO
[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection" (by IDEA-Research)
| OFA | GroundingDINO | |
|---|---|---|
| 3 | 5 | |
| 2,557 | 10,063 | |
| 0.0% | 3.7% | |
| 2.8 | 6.2 | |
| about 2 years ago | almost 2 years ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
OFA
Posts with mentions or reviews of OFA.
We have used some of these posts to build our list of alternatives
and similar projects.
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[R][P] Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework + VQA Hugging Face Spaces Demo
github: https://github.com/OFA-Sys/OFA
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OFA: model that does text-to-image as well as other tasks
From this:
- [R] Paper: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework. Shocking performance in text-to-image synthesis and open-domain tasks.
GroundingDINO
Posts with mentions or reviews of GroundingDINO.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-09-30.
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Autodistill: A new way to create CV models
Some of the foundation/base models include: * GroundedSAM (Segment Anything Model) * DETIC * GroundingDINO
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Is there a way to do segmentation of a person's clothing?
While Segment Anything can detect objects based on text prompts, that's not its strong suite. To get best results, folks usually combine it with Grounding DINO, which is a great object detection model. You run Grounding DINO with text prompt "skirt", this gives you a bounding box that you pass to Segment Anything, which gives you a segmentation mask that you can then use for inpainting with SD.
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Searching for Guidance on Developing an AI Bot for SSBU Training
Now, let's delve into the technological aspects of this project. The combination of Facebook's Segment Anything and Grounding Dino tools will automate annotations for image processing, which is key to this AI endeavor. I'm also intrigued by Mojo, a new programming language designed specifically for AI developers, which will soon be open-source.
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[D] Object Detection Machine Learning
Right now we are trying out grouding dino on this but it is giving a lot of noise and detecting things that are not cracks.
- [D] Data Annotation Done by Machine Learning/AI?
What are some alternatives?
When comparing OFA and GroundingDINO you can also consider the following projects:
LMOps - General technology for enabling AI capabilities w/ LLMs and MLLMs
Detic - Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".
UPop - [ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
Grounded-Segment-Anything - Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
rtic-gcn-pytorch - Official PyTorch Implementation of RITC
segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.