Not detecting the custom object

have trained the model on about 126 images, 30% images contain two objects in a image

from detecto import core, utils, visualize


dataset = core.Dataset('../custom_dataset/')
model = core.Model(['stamp'])

model.fit(dataset)
model.save('model_weights_3.pth')
print(f'[INFO] Model Saved successfully!')
image = utils.read_image('../custom_dataset/page-15.jpg')
predictions = model.predict(image)
print(predictions)

Output:

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2854: UserWarning: The default behavior for interpolate/upsample with float scale_factor will change in 1.6.0 to align with other frameworks/libraries, and use scale_factor directly, instead of relying on the computed output size. If you wish to keep the old behavior, please set recompute_scale_factor=True. See the documentation of nn.Upsample for details. 
  warnings.warn("The default behavior for interpolate/upsample with float scale_factor will change "
[INFO] Model Saved successfully!
([], tensor([], size=(0, 4)), tensor([]))

[prasad01dalavi]

you were absolutely right! there is a problem in dataset. one of the xml file filename was not matching with image name. i removed them, rechecked the dataset and file names.

continued with pip install detecto

and boom, it predicted my object with 0.96 confidence…thanks alot

[alankbi]

@prasad01dalavi so you’re saying the error goes away when you run it with pip install detecto? If so, can you try the suggestions in my first comment again regarding setting verbose=True to see what the model loss is while training? Blank tensors are a sign that your model might not be trained well enough.

@jagilley spent some time going through your Drive folder to see if I could get it to work (I created a Colab file which you can browse through, but it’s very messy). It seems like there’s some issue with your dataset for some reason, which is causing nan losses to come up every time during training:

{'loss_classifier': tensor(nan, device='cuda:0', grad_fn=<NllLossBackward>), 'loss_box_reg': tensor(nan, device='cuda:0', grad_fn=<DivBackward0>), 'loss_objectness': tensor(nan, device='cuda:0', grad_fn=<BinaryCrossEntropyWithLogitsBackward>), 'loss_rpn_box_reg': tensor(inf, device='cuda:0', grad_fn=<DivBackward0>)}

I’m not sure why this is the case, but I can try looking deeper into it over the next few days. In the meantime though, hopefully this helps a bit in clearing up what exactly is happening.