Comments (8)
ArtyZe
commented on July 18, 2024
@ashank-art
Natuerlich :) It's same with the normal Yolo, you can see the result in prediction.png
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ashank-art
commented on July 18, 2024
@ArtyZe ..thanks for your prompt reply!
The prediction.png image does not have any bounding box predictions. It is the same as the original image.
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ArtyZe
commented on July 18, 2024
@ashank-art
can you show me your train log? what is the loss value when you finish your train process?
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abhigoku10
commented on July 18, 2024
@ArtyZe even i am facing he same issue. Just wanted to know should we train the custom model from scratch with the darknet weights or unpruned weights
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ArtyZe
commented on July 18, 2024
Both is ok :) first can reach the final result faster, but result is all same
On 07/11/2019 19:44, abhigoku10 wrote:
@ArtyZe even i am facing he same issue. Just wanted to know should we train the custom model from scratch with the darknet weights or unpruned weights
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abhigoku10
commented on July 18, 2024
@ArtyZe the quantization applied is only for CPU or we could be using for GPU platform also ,
has the model size reduced ?
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ArtyZe
commented on July 18, 2024
as for I want to use it to cpu，but hash net and low rank convolutional and quantization is also fit for gpu，both are ok. But you need to know，it’s not easy to change source code of cuda
On 07/12/2019 16:44, abhigoku10 wrote:
@ArtyZe the quantization applied is only for CPU or we could be using for GPU platform also ,
has the model size reduced ?
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abhigoku10
commented on July 18, 2024
@ArtyZe thanks for instant response as always . I am training your code base with custom data, i shall just have a look into it if am able to run it on GPU, the intention is wanted to run the model with high fps in gpu also
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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
- Usage of this repo HOT 2
- Is this implementation for YOLOV2 or YOLOV3? HOT 7
- `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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