yolo_tracking
segment-anything
yolo_tracking | segment-anything | |
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8 | 56 | |
6,126 | 44,158 | |
- | 1.8% | |
9.9 | 0.0 | |
7 days ago | 19 days ago | |
Python | Jupyter Notebook | |
GNU Affero General Public License v3.0 | 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.
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.
yolo_tracking
- FLiPN-FLaNK Stack Weekly for 17 April 2023
- Person head count
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[P] Vehicle detection with pytorch
You can use YOLOv5 with the StrongSORT. We have been using it for human detection and tracking. It works really well and YOLOv5 in general really easy to use and implement out of the box. here is the repo that we are using.
- ID Swap issue in multi-object tracking.
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tracking-by-detection, multiple object tracking algorithm
Try looking into DeepSort, which uses a deep association metric in addition to the traditional SORT algorithm to kind of improve upon the ID reassignment issue. However, I suspect you would have to come up with your own re-id model since you have a unique object you're trying to detect. Here's the paper . I've had decent results using https://github.com/mikel-brostrom/Yolov5_DeepSort_OSNet as an out of the box implementation for coco object. It's written in PyTorch.
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Object tracking in videos?
https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch I see this combination mentioned a decent amount
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Deepsort stuck in tentative
https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch/blob/master/deep_sort_pytorch/deep_sort/sort/tracker.py.
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?
yolact - A simple, fully convolutional model for real-time instance segmentation.
Segment-Everything-Everywhere-All-At-Once - [NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
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.
FairMOT - [IJCV-2021] FairMOT: On the Fairness of Detection and Re-Identification in Multi-Object Tracking
ComfyUI-extension-tutorials
classy-sort-yolov5 - Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
stable-diffusion-webui-Layer-Divider - Layer-Divider, an extension for stable-diffusion-webui using the segment-anything model (SAM)
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Grounded-Segment-Anything - Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
crop - Character Recognition Of Plates using yolov5
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"