keras-yolo3
ultralytics
| keras-yolo3 | ultralytics | |
|---|---|---|
| 1 | 27 | |
| 1,606 | 22,973 | |
| - | 7.1% | |
| 0.0 | 9.8 | |
| 8 months ago | 1 day ago | |
| Python | Python | |
| MIT License | 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.
keras-yolo3
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What’s Destroying My Yard? Pest Detection with Raspberry Pi
I've always been facinated by the usage of the Pi. I think of the methods used when you have a pi, versus not having one. It looks like a fun project if you have all the parts, but I don't so I thought of this alternative!
A method I thought of was just a camera that will utilize yolo3 https://github.com/experiencor/keras-yolo3 or just an always on security camera that you can just skip through the video to see all the activity with less setup, and seeing what animals cause issues. The jetson for faster 'edgey' visual ML models might be an option for those who want a stronger NPU, but using online GPU/NPU tokens and GPUs on computers if you don't need live feedback is very effective.
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?
tensorflow-yolo-v3 - Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)
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.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
super-gradients - Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
yolo_tracking - BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
GroundingDINO - Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Auto-GPT - An experimental open-source attempt to make GPT-4 fully autonomous. [Moved to: https://github.com/Significant-Gravitas/Auto-GPT]
yolov8_onnx_python - YOLOv8 inference using Python
Detic - Code release for "Detecting Twenty-thousand Classes using Image-level Supervision".