Comments (7)
@sicarioakki
up to now, schade not. some new layers in v3 in not realized in v2, but most changes are in convolutional_layer.c and parser.c, you can transplantation them to v3, later maybe I'll do it when I'm free :)
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Okay! This is essentially for YOLOV2 then..
Thanks man!
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@ArtyZe so you have incroprated the changes of pruning in convolutional_layer.c and parser.c right ?
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@sicarioakki
up to now, schade not. some new layers in v3 in not realized in v2, but most changes are in convolutional_layer.c and parser.c, you can transplantation them to v3, later maybe I'll do it when I'm free :)
Yolo3 weight please send me
[email protected]
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@katagoni This repo is now support v3, you can train your train sets like the original yolov3, the only thing you need to do is pick which accelerate method and open it in makefile, I have no idea what weight you need?
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@katagoni This repo is now support v3, you can train your train sets like the original yolov3, the only thing you need to do is pick which accelerate method and open it in makefile, I have no idea what weight you need?
How to train yolo3 i need recently strongest my favor
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- download my repo and open MASK, GPU, PRUNE,MULTI_CORE in makefile
- start to train just like the original yolov3 :)
If you still don't know how to train, I can upload a txt file to explain how to use it (actually you should see the readme first)
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Related Issues (10)
- prune weights for support yolo-v3 HOT 5
- can the model file after training (just use google quantization) be correctly parsed by original darknet
- L1 scales regularization HOT 2
- Weights File for yolov2 HOT 10
- Predictions are not drawing after testing trained model HOT 8
- Usage of this repo HOT 2
- `Hash Compress` `Google Quantization` `Huffman Compress`
- AVX Problem HOT 6
- how to get output_scale and output_zero_point in the cfg file.
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