darknet
tensorflow-yolo-v3
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darknet | tensorflow-yolo-v3 | |
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62 | 3 | |
21,418 | 895 | |
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7.0 | 0.0 | |
25 days ago | 11 months ago | |
C | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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darknet
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Anybody building ML models in C++?
YoloV3/4 is C based if that counts: https://github.com/AlexeyAB/darknet
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[D] Fixing the angle of Skewed Paintings, see comments
This is all well-known information, see any (and all!) previous discussions when YOLOv5 comes up. For details: https://github.com/AlexeyAB/darknet/issues/5920
- Viseron 2.0.0 - Self-hosted, local only NVR and AI Computer Vision software.
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How do I train YOLO5 to detect small objects (arial imagery). something like 20-20 pixels or maybe little more? How do I increase resolution and apply augmentation and tiling? Or maybe the YOLO5 is not he best choice for that?
2) YOLOv5 is both slower and less precise than YOLOv4. Why use YOLOv5? Source: https://github.com/AlexeyAB/darknet/issues/5920
- Machine learning Library in C?
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I just realized yolov5 is GPL-3
So my recommendation is you stuck with Darknet/YOLO and use v4 of YOLO. The Darknet framework license is definitely suitable for commercial use: https://github.com/AlexeyAB/darknet/blob/master/LICENSE
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GPL vs MIT.
Still to long. Here's my favourite license: https://github.com/AlexeyAB/darknet/blob/master/LICENSE
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I was excited about YOLOv7, so I built a sharable object detection application with VDP and Streamlit.
When YOLOv7 was out, I built a web app to test it against the classic YOLOv4 and shared it with my team, then deployed it online to share with the community.
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Does reducing the number of classes on YOLOv5 make it faster at inference?
If you're worried about performance, you shouldn't be using YOLOv5 since it is slower (and less accurate!) than YOLOv4. Source: https://github.com/AlexeyAB/darknet/issues/5920
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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.
tensorflow-yolo-v3
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How to use custom model for flutter app?
I used yolov4 and trained model on my classes, then saved weights in file.weights. Now, I want to integrate that model into my flutter app. On firebase, there is an option to use a custom model but that requires uploading .tflite file. My question is how can I convert my trained model weights and upload them as .tflite so could be used in my app. I have tried following this https://github.com/mystic123/tensorflow-yolo-v3 but not success. I would appreciate your help in the conversion of .weights to .tflite or suggest of there is any other way round
- “ValueError: cannot reshape array of size 278540 into shape (256,128,3,3)” Conversion YOLOv3 .weights to .pb
What are some alternatives?
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
edge-tpu-tiny-yolo - Run Tiny YOLO-v3 on Google's Edge TPU USB Accelerator.
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
keras-yolo3 - Training and Detecting Objects with YOLO3
efficientdet-pytorch - A PyTorch impl of EfficientDet faithful to the original Google impl w/ ported weights
yolov3-tf2 - YoloV3 Implemented in Tensorflow 2.0
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
yolov3 - YOLOv3 in PyTorch > ONNX > CoreML > TFLite
darknet_ros - YOLO ROS: Real-Time Object Detection for ROS
tensorflow-lite-YOLOv3 - YOLOv3: convert .weights to .tflite format for tensorflow lite. Convert .weights to .pb format for tensorflow serving
YOLOv3 - YOLOv3 Implementation in TensorFlow 1.1X