Noha90 commited on
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cacd37b
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1 Parent(s): 925a7e9

deploy the compressed model file

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Files changed (2) hide show
  1. README.md +27 -1
  2. predict.py +1 -1
README.md CHANGED
@@ -12,5 +12,31 @@ pinned: false
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  # AML 16
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- This is a Demo using Gradio app for AML 16.
 
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  # AML 16
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+ This is a demo application for the best-performing model (Swin-Large) created for the AML 16 project.
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+ The app uses Gradio to provide an interactive interface where users can upload an image, view the top-1 predicted scene category, see a reference image from the predicted class, and explore the top-5 prediction probabilities in a bar chart.
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+ The model was trained for scene classification and deployed using Hugging Face Spaces.
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+ - predict.py
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+ This file handles loading the trained Swin-Large model and making predictions.
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+ It loads the model weights from Hugging Face Hub, applies the correct image preprocessing, and outputs:
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+ The uploaded image,
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+ A reference image from the predicted class,
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+ The Top-5 prediction probabilities.
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+ The model was customized with an updated classifier head, and class labels are loaded from a labels.json file. A random sample image from the predicted class folder is also shown for better visualization.
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+ - app.py
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+ This file builds the Gradio interface.
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+ It lets users upload an image, runs the prediction using predict.py, and displays:
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+ The uploaded image,
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+ An image for the top-1 predicted class,
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+ The predicted class label,
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+ A bar chart showing the Top-5 prediction probabilities.
predict.py CHANGED
@@ -36,7 +36,7 @@ class SwinCustom(nn.Module):
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  outputs = self.model(images)
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  return outputs.logits
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- model_path = hf_hub_download(repo_id="Noha90/AML_16", filename="large_swin_best_model.pth")
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  print("Model path:", model_path)
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  model = SwinCustom(model_name=MODEL_NAME, num_classes=40)
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  state_dict = torch.load(model_path, map_location="cpu")
 
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  outputs = self.model(images)
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  return outputs.logits
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+ model_path = hf_hub_download(repo_id="Noha90/AML_16", filename="swin_large_quantised.pth")
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  print("Model path:", model_path)
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  model = SwinCustom(model_name=MODEL_NAME, num_classes=40)
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  state_dict = torch.load(model_path, map_location="cpu")