Large models are not supported yet

[godisme1220]

`---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
Cell In[15], line 2
1 # Generates a directory with masks
----> 2 segmenter = GlassesSegmenter(kind="lenses", size="l")
3 segmenter.process_dir(
4 input_path=r"C:/python_project/glasses-detector/data", # output dir defaults to path/to/dir_preds
5 output_path="./output",
(...)
10 output_size=None, # set to None to keep the same size as image
11 )

File :7, in init(self, kind, size, weights, device)

File c:\Users\EugeneLin\AppData\Local\miniconda3\envs\GLASSES-DETECTOR\Lib\site-packages\glasses_detector\components\base_model.py:189, in BaseGlassesModel.post_init(self)
185 self.device = torch.device("cpu")
187 if self.weights:
188 # Load weights if True or path is specified
--> 189 self.load_weights(path_or_url=self.weights)
191 # Cast to device
192 self.model.to(self.device)

File c:\Users\EugeneLin\AppData\Local\miniconda3\envs\GLASSES-DETECTOR\Lib\site-packages\glasses_detector\components\base_model.py:547, in BaseGlassesModel.load_weights(self, path_or_url)
544 return
546 if self.size.lower() in self.ALLOWED_SIZE_ALIASES["large"]:
--> 547 raise NotImplementedError("Large models are not supported yet")
549 # Construct weights URL from base URL and model info
550 weights_name = f"{self.task}{self.kind}{name}.pth"

NotImplementedError: Large models are not supported yet
`

Dear glasses-detector developer, thanks for this amazing tool, helping me a lot! While I'm using segmenter, I input size argument with "large" or "l". In the API document page, it does support the large argument, but when I input the large argument, it returns upper error, how can I fix it?

Thanks for reply, Eugene Lin.

[mantasu]

Hi @godisme1220, yeah, unfortunately, weights for large models are unavailable but it is only indicated in Performance Section. Thanks for pointing this out, I should make documentation clearer. It is only possible to instantiate a large model without pretrained weights:

segmenter = GlassesSegmenter(size="large", weights=False)

If you specifically need lenses segmentation, then you could check #13 where I provided large model weights that may work slightly better.

In general, I tried training large models for all tasks but their performance did not outperform medium models, thus I didn't include them. I think these could be the reasons:

• Large models are typically used for more complex tasks, here we are dealing with binary segmentation/classification
• Large models would perform better if they were trained on much larger datasets (I couldn't find more :/)
• Large models could perform better if the model architecture, as well as hyperparameters, were specifically tuned for each task