Comments (6)
@yukai0406 Hello, I have updated batches of updates in my repo, and fixed bugs include your above issue, just have a try :)
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I will try
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@yukai0406 suggest you pay some attention to quantization, model sparsity will be replaced by NAS in my opinion, and NCNN of Tencent is a better framework if you want to run your model in embedded device instead of darknet
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Thank you for the recommendation.
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Did you have a plan to add winograd algorithm like ncnn?
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@yukai0406 As you seen, I have changed darknet to c++ version and it's convenient to port code of other framwork such as ncnn to darknet. you can have a try. Maybe I will start this work in 5.1 festival if I have free time :)
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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
- Is this implementation for YOLOV2 or YOLOV3? HOT 7
- `Hash Compress` `Google Quantization` `Huffman Compress`
- how to get output_scale and output_zero_point in the cfg file.
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