Comments (1)
tylertroy
commented on May 13, 2024
Hopefully the PR above is merged but in the mean time I found that the AssertionError is caused by the --n_classes (-n) arg being used in two places. The first to convert the original yolov4.weights to .h5 which requires -n=80 and the second for the custom model which will be -n=N_CUSTOM_CLASSES By the running the program twice, once for each N, you can get generate the required weights. Just note that when you run with n=80 the first time you raise a ValueError of ValueError: cannot reshape array of size 4559937 into shape (1024,512,3,3) Just ignore this and run again with the correct -n for your custom weights.
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Related Issues (20)
- Download darknet weights if not available locally
- Colab example for inference with visualization
- Pretrained Weights management
- Add an All-Contributor section to the README.md HOT 1
- Improve Darknet conversion script
- Add COCO classes and display them in Colab notebook
- Can not instanciate model for certain input shapes
- Better management of __init__ files
- tensorflow.python.framework.errors_impl.InvalidArgumentError: Paddings must be non-negative
- Metrics on official datasets
- Error in colab notebook, cannot reshape array of size 687 into shape (32,3,3,3) HOT 3
- Inference failing with exported tfLite models
- Custom model w/ AlexeyAB darknet : export fine, load_weights issue HOT 5
- Import custom model error `ValueError: No such layer: conv2d_32` HOT 4
- Error Running colab: "!convert-darknet-weights yolov4.weights -o yolov4.h5" HOT 3
- Converting tiny YOLOv4 to h5 format HOT 1
- Loading weight file containing 217 layers into a model with 3 layers HOT 1
- Cannot convert-darknet-weights from darknet to h5 format HOT 2
- Performance comparison of tf2-yolov4 and AlexeyAB darkenet
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