Comments (3)
I loaded resnet50's imagenet weight and started training.
from yolo-digit-detector.
From the config file, I can see that you have 20 epoch for the first training phase, and 100 epoch for the second training phase. Is that what you total trained epoch for your real problem?
from yolo-digit-detector.
The yolo v2 used in this project is an early model in the deep learning period, and it is difficult to train. So it needs some elaborate tuning and some tricks. In the case of me, I trained the way it is listed at 2-training-from-scratch
In the case of retinanet or yolo v3 that came out after that, learning was done well without such tricks.
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
- 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
- 'str' object has no attribute 'decode' HOT 3
- 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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from yolo-digit-detector.