Track-Anything
segment-anything
Track-Anything | segment-anything | |
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16 | 58 | |
6,181 | 44,715 | |
- | 1.5% | |
1.0 | 0.0 | |
6 days ago | about 2 months ago | |
Python | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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.
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Track-Anything
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Keying/masking person on a footage
I was doing rotoscoping for a silhouette of a girl dancing in front of a building, then I saw this amazing tool: https://github.com/gaomingqi/Track-Anything
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Advice for multi-animal tracking for scientific research?
My question is, how can we modernize this pipeline? We've experimented a bit with the new SAM-based track-anything tool, and it seems promising, but we actually don't want to "track anything", we only want to track fishes. What would you do in 2023, to extract tracks of one specific class of object from long video datasets? I'm hoping for any advice at all.
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[D] Which open source models can replicate wonder dynamics's drag'n'drop cg characters?
The Track-Anything tool already implements this
- Tutorial for Track-Anything, an interactive tool to segment, track, and inpaint anything in videos.
- Github for Track Anything
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Segment Anything for Video - Track Anything! 🤖
Segment Anything for Video - Track Anything! 🤖 With this tool, you can automatically isolate objects, make edits using inpainting, and track objects with precision. It's a game-changer for creative projects. Even though it does not work well with the shadows yet, we expect a rapid evolution of these technologies. Github : https://github.com/gaomingqi/Track-Anything)
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How to adapt an existing Python project to my specific use case without bringing in unnecessary dependencies or reinventing the wheel
I would like to know if there are any guidelines to follow when adapting an existing Python project for my own use-case. Specifically, I want to customize the output of the Track-Anything project by incorporating my own processing steps. However, I do not want to import the entire codebase. Rather, I only want to import the minimum amount of code necessary to produce the same output with object tracking, without having to reimplement functions that are already available.
- Track-Anything should get implemented in kdelive
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SUSTech VIP Lab Proposes Track Anything Model (TAM) That Achieves High-Performance Interactive Tracking and Segmentation in Videos
Here is the GitHub: https://github.com/gaomingqi/track-anything
segment-anything
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What things are happening in ML that we can't hear oer the din of LLMs?
- segment anything: https://github.com/facebookresearch/segment-anything
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Zero-Shot Prediction Plugin for FiftyOne
In computer vision, this is known as zero-shot learning, or zero-shot prediction, because the goal is to generate predictions without explicitly being given any example predictions to learn from. With the advent of high quality multimodal models like CLIP and foundation models like Segment Anything, it is now possible to generate remarkably good zero-shot predictions for a variety of computer vision tasks, including:
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Generate new version of a living-room with specific furniture
Render a new living room using a controlnet model of your choice to keep the basic structure. Load the original living room image and look for the furniture you want to change with a Segment Anything Model to create a mask. Use that mask on the new living room to inpaint new furniture.
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How Do I read Github Pages? It is so exhausting, I always struggle, oh and I am on windows
Hello,So I am trying to run some programs, python scripts from this page: https://github.com/facebookresearch/segment-anything, and found myself spending hours without succeeding in even understanding what's is written on that page. And I think this is ultimately related to programming.
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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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How to Fine-Tune Foundation Models to Auto-Label Training Data
Webinar from last week on how to fine-tune VFMs, specifically Meta's Segment Anything Model (SAM).
What you'll need to follow along the fine-tuning walkthrough:
Images, ground-truth masks, and optionally, prompts from the Stamp Verification (StaVer) Dataset on Kaggle (https://www.kaggle.com/datasets/rtatman/stamp-verification-s...)
Download the model weights for SAM the official GitHub repo (https://github.com/facebookresearch/segment-anything)
Good understanding of the model architecture Segment Anything paper (https://ai.meta.com/research/publications/segment-anything/)
GPU infra the NVIDIA A100 should do for this fine-tuning.
Data curation and model evaluation tool Encord Active (https://github.com/encord-team/encord-active)
Colab walkthrough for fine-tuning: https://colab.research.google.com/github/encord-team/encord-...
I'd love to get your thoughts and feedback. Thank you.
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Deploying a ML model (segment-anything) to GCP - how would you do it?
I now want users to be able to use the segment-anything model (https://github.com/facebookresearch/segment-anything) in my app. It's in pytorch if that matters. How it should work is that
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The Mathematics of Training LLMs
Yeah, they are great and some of the reason (up the causal chain) for some of the work I've done! Seems really fun! <3 :))))
Facebook's Segment Anything Model I think has a lot of potentially really fun usecases. Plaintext description -> Network segmentation (https://github.com/facebookresearch/segment-anything/blob/ma...) Not sure if that's what you're looking for or not, but I love that impressing your kids is where your heart is. That kind of parenting makes me very, very, very, happy. :') <3
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How hard is it to "code" a tool based on segment-anything and Stable diffusion ?
There are some snippets of Python code on the segment-anything github readme that show how to do this. Once you have it installed you can import functions from the segment-anything module, load a segmentation model, and generate masks for input images that match the prompt of your choice. You don't need Stable Diffusion for this, but you could load it through diffusers to do things like inpaint your images using the masks.
- The less i know the better
What are some alternatives?
stable-diffusion-webui - Stable Diffusion web UI
Segment-Everything-Everywhere-All-At-Once - [NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"
sam-clip - Use Grounding DINO, Segment Anything, and CLIP to label objects in images.
backgroundremover - Background Remover lets you Remove Background from images and video using AI with a simple command line interface that is free and open source.
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
ComfyUI-extension-tutorials
sd-webui-segment-anything - Segment Anything for Stable Diffusion WebUI
stable-diffusion-webui-Layer-Divider - Layer-Divider, an extension for stable-diffusion-webui using the segment-anything model (SAM)
Grounded-Segment-Anything - Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
mmdetection - OpenMMLab Detection Toolbox and Benchmark