Comments (3)
I dropped the decode('utf-8') part but that gave me another bug.
You can see the link that has solution: https://stackoverflow.com/questions/28583565/str-object-has-no-attribute-decode-python-3-error
Now I am getting ValueError: Cannot feed value of shape (30, 2048, 1, 1) for Tensor 'Placeholder_318:0', which has shape '(1, 1, 2048, 75)'
Thanks
from yolo-digit-detector.
Thanks for the help. Yes I have tried that too. But it gives that error code as you mentioned . But I think this will help for some extent.
https://www.kaggle.com/tustunkok/license-plate-detection
This repository describes a similar approach for localizing objects from regular cnn. ( I couldn't achieve this btw, hope you could figure this easily)
from yolo-digit-detector.
I've solved it, thanks to
keras-team/keras#14265 (comment)
from yolo-digit-detector.
Related Issues (20)
- Hey pretrained weights are overfitted. HOT 3
- pre-train model HOT 6
- SVHN labeling dataset HOT 4
- Digit Detection fails after training from scratch (or fine tuning) HOT 5
- Extraneous detection boxes on sample input using pre-trained weight file HOT 9
- Did you train your model from scrath without load any pretrained weight? HOT 3
- Yolo weights file HOT 3
- input size HOT 4
- train model by other labels HOT 1
- Is your notebook directly use weight.h5 from google? HOT 1
- How to train with non-square images without resizing HOT 1
- The Layer has never been called and thus has no defined output shape. HOT 2
- Detecting Handwritten Digits HOT 1
- Training model from scratch on custom dataset performing poorly
- Detecting more than 2 digits HOT 7
- RuntimeError: The layer has never been called and thus has no defined output shape HOT 3
- I'm not able to download weights.h5
- incorrect detection outputs by pre-trained model HOT 1
- Pre-trained model doesn't detect anything on the SVHN sample images
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