YOLOv6
quickai
Our great sponsors
| YOLOv6 | quickai | |
|---|---|---|
| 11 | 7 | |
| 5,526 | 163 | |
| 1.2% | - | |
| 6.7 | 3.7 | |
| about 1 month ago | 17 days ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 only | MIT License |
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.
YOLOv6
-
I want to make a Class monitoring system. is it possible in the conditions I'm in ??
Some resources to get you started...https://towardsdatascience.com/object-detection-with-10-lines-of-code-d6cb4d86f606https://github.com/OlafenwaMoses/ImageAIhttps://towardsdatascience.com/yolo-object-detection-with-opencv-and-python-21e50ac599e9https://github.com/meituan/YOLOv6
- [P] Any object detection library
-
DeepSort with PyTorch(support yolo series)
meituan/YOLOv6
-
Tried to install requirements.txt with pip for YOLOv6.
Have you looked at this open github issue? It might be that you do not need to/should not install it using pip.
- A single-stage object detection framework dedicated to industrial applications
-
YOLOv6: Redefine state-of-the-art for object detection
https://github.com/meituan/YOLOv6/blob/main/docs/About_namin...
> P.S. We are contacting the authors of YOLO series about the naming of YOLOv6.
You should ask _before_ publishing, not _after_.
They claim it runs faster and is more accurate than YOLOv5, yet requires 3x as much computation (GFLOPs)? Something doesn't add up here.
There is unbelievably little information about the architecture too. Unfortunately it's not in a format I can easily throw the cfg in as visualize it: https://gitlab.com/danbarry16/darknet-visual
This appears to be on purpose to advertise DagsHub: https://dagshub.com/pricing
-
[D][P] YOLOv6: state-of-the-art object detection at 1242 FPS
Saved you the time: https://github.com/meituan/YOLOv6
- Is YOLOv6 actually a significant improvement over YOLOv5?
- YOLOv6 is out
quickai
- Show HN: QuickAI Version 2 Released
-
QuickAI version 2 released!
I originally released QuickAI here. I am very excited to announce version 2 of QuickAI
-
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
GitHub: https://github.com/geekjr/quickai
- Show HN: Quickai – Quickly experiment with state-of-the-art ML models
-
quickai - A Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
Yeah, totally agree. https://github.com/geekjr/quickai/blob/main/quickai/image_classification.py does really need some reworking. Dicts are the way to go. But once that's done, I think it could actually be a practical lib!
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
detoxify - Trained models & code to predict toxic comments on all 3 Jigsaw Toxic Comment Challenges. Built using ⚡ Pytorch Lightning and 🤗 Transformers. For access to our API, please email us at [email protected].
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
gpt-neo_dungeon - Colab notebooks to run a basic AI Dungeon clone using gpt-neo-2.7B
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
segyio - Fast Python library for SEGY files.
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
chappie.ai - Generalized AI to perform a multitude of tasks written in python3
keras-yolo3 - Training and Detecting Objects with YOLO3
Note - Easily implement parallel training and distributed training. Machine learning library. Note.neuralnetwork.tf package include Llama2, CLIP, ViT, ConvNeXt, SwiftFormer, etc, these models built with Note are compatible with TensorFlow and can be trained with TensorFlow.
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.