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darknet
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| barcode-qrcode-images | darknet | |
|---|---|---|
| 3 | 52 | |
| 4 | 19,615 | |
| - | - | |
| 5.6 | 6.6 | |
| 7 months ago | 2 days ago | |
| Java | C | |
| MIT License | GNU General Public License v3.0 or later |
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darknet
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[D] DarkNet YOLOv4 with CUDA 11.7 in Windows?
I looked around online but I only found this post discussing a related issue, leading me to think there seems to be some sort of compatibility issue going on here. And I think this is the most recent version of the file I am trying to compile located on the exact same folder where my copy is and when I opened it it shows CUDA 11.1 in line 307.
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Pretrained model on aerial object detection
(As an aside, it isn't just that YOLOv5 is evil, the problem is YOLOv5 is both slower and less precise than YOLOv4, and even then, no-one can reproduce the performance results they claim to get.) Source: https://github.com/AlexeyAB/darknet/issues/5920
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[D][P] YOLOv6: state-of-the-art object detection at 1242 FPS
For a quick start on YOLOv3 / 4 follow the instructions here: https://github.com/AlexeyAB/darknet
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Show HN: Computer Vision Models for Developers
Hi,
Almost All models are based on the Darknet[0] configurations which would come under this License[1]. We further trained/finetuned those architectures on additional datasets collected by us.
Whole framework actual used for final products is completely written in Nim.
- Does Multi Object Tracking work better (precision/recall) on videos than jury rigging a SOTA image object detection to work on videos?
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Which model is best for detecting small objects? Yolov3? MaskRCNN, Faster-RCNN?
YOLOv4-tiny-3L is what I use when I'm doing projects that require finding small objects. And for really tiny objects in large images where the object would be resized to zero (or near zero) due to network dimensions, then I use DarkHelp with tiling, combined with YOLOv4-tiny-3L. Depending on how precise the bounding boxes need to be, I also tend to use YOLOv4-tiny, which is faster but less precise than YOLO-v4-tiny-3L.
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Yolov5 vs Yolov4 tiny training time
...and since I'm here, be aware of the issues before you use YOLOv5: https://github.com/AlexeyAB/darknet/issues/5920
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When to use YOLOv5 and when not to use the model?
What the Darknet repo actually says is I think a step beyond a "gimmick". YOLOv5 is both slower and less precise than YOLOv4. And the way they did it and released v5 immediately after AlexeyAB released v4 was also very questionable. The exact issue where this was tested and discussed is here: https://github.com/AlexeyAB/darknet/issues/5920
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CSPDarknet-53 or not?
Hi, I want to train a custom object detector using YOLOv4, but I want to make sure of what kind of Darknet is the backbone in this cfg, is it CSPDarknet-53 or Darknet-53? I tried to understand the file to the best of my ability but got stuck :(
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bike classification advice sought for used bike aggregator
yolov4 GitHub
What are some alternatives?
yolov5 - YOLOv5 š in PyTorch > ONNX > CoreML > TFLite
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
efficientdet-pytorch - A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
tensorflow-yolo-v3 - Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
tensorflow-lite-YOLOv3 - YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving
darknet_ros - YOLO ROS: Real-Time Object Detection for ROS
Yet-Another-EfficientDet-Pytorch - The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.
AlexNet - implement AlexNet with C / convolutional nerual network / machine learning / computer vision
darknet - Convolutional Neural Networks
Alturos.Yolo - C# Yolo Darknet Wrapper (real-time object detection)
wgpu - Safe and portable GPU abstraction in Rust, implementing WebGPU API.