Comments (5)
Hi, the images used in pre-trained model are generated by me and they all look very well. Just try to make more training data, it will work!
from crnn-pytorch.
Thanks for the reply.
It really work after with more data and with more patient :)
I have another question :
While in training , all my input image size is (120,32) and in validation I saw there has a code
with "dataset.resizeNormalize(params.imgW,params.imgH)" . And in params, I set keep_ratio =True ,and imgW=120 , imgH=32. After several epochs training, my val acc comes to 0.96.
But when I put images which from CTPN model (different size of images) and send them to demo.py, the acc are terrible.
So, it means I have to put my training data with different size? (e.g: (300.32), (150,32), (500,32)..... )
But the code "dataset.resizeNormalize(params.imgW,params.imgH)" will resize and image will become distortion. What dataset did you make ?
from crnn-pytorch.
Hi, the images from CTPN should look like your training data. If your training data is generated by yourself and looks very nice, while the images out from CTPN are more real, the net will do bad
from crnn-pytorch.
So, you mean if I train with generated images and test with CTPN's crop image, then the result will be very bad.... ?
I have to train with self crop real images and test with TPN's crop images?
So the 3600 thousand dataset is useless?
from crnn-pytorch.
Hello, I encountered the same problem, I used 6 million pieces of data for training. However, during verification, the predicted data is empty, and the previously generated data does not have this problem.
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Related Issues (20)
- KeyError : ' ' HOT 1
- Problem loading checkpointed model
- inference accuracy HOT 10
- Output indicates "PAD" char for all columns HOT 3
- Train problem HOT 1
- A question about pre-trained model HOT 3
- Problem about ctc_loss variable input_length while training HOT 1
- Mean vals and norm vals
- No predictions when training or testing the net !!!
- create image Tensor HOT 2
- Traanning Question HOT 3
- number images train
- Training Problems HOT 2
- 梯度爆炸,loss显示持续显示为inf HOT 1
- img.sub_(0.5).div_(0.5)
- 运行demo用cenn.pth预训练模型显示Expected 512, got 64
- val loss:nan, accuray:0 HOT 1
- [Friendly reminder] About the accuracy of demo.py
- val gpu slow HOT 1
- size mismatch for rnn.1.embedding.weight
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