Image should be first transformed and only then used for the prediction

[tugot17]

Describe the bug
Before conducting the prediction the image should be Resized to the original training size and then Normalized the data comes from the same distribution as training data

To Reproduce
In app.py go to the line 223

eval_ds = Dataset.from_images([image])
batch, samples = faster_rcnn.build_infer_batch(eval_ds)
preds = faster_rcnn.predict(
        model=model, batch=batch, detection_threshold=confidence_threshold)

How to fix

Add the transformation while creating the dataset

transformations = tfms.A.Adapter([tfms.A.Resize(384, 384), tfms.A.Normalize()])
eval_ds = Dataset.from_images([image], transformations)
batch, samples = faster_rcnn.build_infer_batch(eval_ds)
preds = faster_rcnn.predict(
model=model, batch=batch, detection_threshold=confidence_threshold)

[oke-aditya]

Yes, we can add these Test Time Transforms too.
Thought it should work fine without these. Since Faster RCNN uses Generalized RCNN transforms that works for image of any size.

Can you open a PR as well @tugot17 ? If not possible, don't worry I will fix it up.

[tugot17]

I think the "domain" for not Normalized images is different and it may cause the predictor to not work properly.
I opened the PR #4

[oke-aditya]

Can cause error, though it hasn't caused yet. But defensive programming is better.