Add training guide and align text detection model training with recognition model #8
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This is needed for ONNX export to work.
This is the current default behavior, but PyTorch warns that this value is changing in future. Setting `antialias=True` might produce better results, but currently leads to out-of-range errors during loss computation which needs to be resolved first.
Align the text detection model training script with the recognition model training by: - Adding a `--export` option to export a checkpoint to ONNX after loading it - Adding a `--max-epochs` flag to trigger automatic termination of the training process after a fixed number of epochs - Adding wandb integration to allow tracking training progress
Model export is now implement in `train_detection` instead.
When using `antialias=True` with the `Resize` transform on target masks, the resulting values could sometimes be slightly above 1.0. The same thing happened when training with CUDA even without this.
- Use `pin_memory` for data loaders. This was already used in the recognition training script. - Move prediction / target masks to CPU once per batch, instead of separately per item
These need to be installed separately as the dependencies will vary by platform and GPU.
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Replace precise intersection test with a cheap bounding box intersection test.
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Add a guide with steps to train the text detection and recognition models from scratch. Along the way various improvements to the text detection training tool and other things were needed.
torch,torchvisionfrom the Pipfile and install them separately. This is needed because the dependencies vary by platform and GPUantialiassetting inResizetransform by setting it explicitlyFixes #6