darknet-visual
yolov7_d2
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| darknet-visual | yolov7_d2 | |
|---|---|---|
| 1 | 4 | |
| - | 3,130 | |
| - | - | |
| - | 0.0 | |
| - | 5 months ago | |
| Python | ||
| - | GNU General Public License v3.0 only |
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darknet-visual
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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
yolov7_d2
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YOLOv7: Trainable Bag-of-Freebies
Especially hilarious considering some other people ALSO jumped on the "we made an object detector so let's call it YOLOvX" wagon and released...
Something called YOLOv7.
https://github.com/jinfagang/yolov7
- YOLOv7: YOLO with Transformers and Instance Segmentation
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How to Train YOLOv6 on a Custom Dataset
You're 9 months late https://github.com/jinfagang/yolov7
- YOLOv6: Redefine state-of-the-art for object detection
What are some alternatives?
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
YOLOv6 - YOLOv6: a single-stage object detection framework dedicated to industrial applications.
edgetpu-yolo - Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU
edgetpu - Coral issue tracker (and legacy Edge TPU API source)
yolact - A simple, fully convolutional model for real-time instance segmentation.
YOLOv4 - Port of YOLOv4 to C# + TensorFlow
BCNet - Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/