yolo_tracking
ultralytics
| yolo_tracking | ultralytics | |
|---|---|---|
| 8 | 27 | |
| 6,126 | 22,973 | |
| - | 7.1% | |
| 9.9 | 9.8 | |
| 7 days ago | 6 days ago | |
| Python | Python | |
| GNU Affero General Public License v3.0 | GNU Affero General Public License v3.0 |
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.
ultralytics
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The CEO of Ultralytics (yolov8) using LLMs to engage with commenters on GitHub
Yep, I noticed this a while ago. It posts easily identifiable ChatGPT responses. It also posts garbage wrong answers which makes it worse than useless. Totally disrespectful to the userbase.
https://github.com/ultralytics/ultralytics/issues/5748#issue...
- FLaNK Weekly 08 Jan 2024
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My kid sounds like ChatGPT, and soon yours might, too
There are obvious places it is being used that I have noticed organically. For instance, check out the answers in this repo:
https://github.com/ultralytics/ultralytics/issues/5748#issue...
If you read the answers there, the style of answering is always to repeat the question in a very specific way. Once you see it you can’t in-see it.
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
When browsing the state-of-the-art in object detection on Papers with Code, I found the YOLO model to be one of the most popular, accurate, and fastest. That being said, I would recommend having a look at Ultralytics, which provides the tools to evaluate, predict, and export the latest versions of YOLO models with only a few lines of code.
- Instance segmentation of small objects in grainy drone imagery
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Breaking the Myth: Object Detection Isn't Hard as Thought
YOLOv8 (You Only Look Once) is an open-source Computer Vision AI model released on January 10th, 2023. It’s called YOLO because it detects everything inside an image in a single pass. The new version can perform image detection, classification, instance segmentation, tracking, and pose estimation tasks.
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How I use "AI" to entertain my cat
Next, I needed to figure out, how can I access the stream, recognize an animal, then let Max know? There are tons of examples of recognizing an object via camera frames, but I ultimately found this python library called ultralytics that supports RTSP streams and classifying objects in the video frames using pre-built models. The docs looked like it would be pretty low effort, so after some experimentation, I was successful in having the ultralytics library recognize objects from my cheap camera!
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How to load the optimizer state_dicts in yolov8?
I have created an issue in their Github as well but so far not much help has been recieved. You can check that here
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Autodistill: A new way to create CV models
And the target models include: * YOLOv8 (You Only Look Once) * YOLO-NAS * YOLOv5 * and DETR
What are some alternatives?
yolact - A simple, fully convolutional model for real-time instance segmentation.
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.
ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
FairMOT - [IJCV-2021] FairMOT: On the Fairness of Detection and Re-Identification in Multi-Object Tracking
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
classy-sort-yolov5 - Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
yolov8_onnx_python - YOLOv8 inference using Python
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Detic - Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".