first commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+09_pretrained_vit_feature_extractor_cifar_60_percent.pth filter=lfs diff=lfs merge=lfs -text

09_pretrained_vit_feature_extractor_cifar_60_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e7470fb73a7052e71ad0c8eb70352270591c619085984996e6d5b8cc6ebe072
+size 343292910

app.py

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+### 1. Imports and class names setup ###
+from model import create_vitB16_model
+
+import torch
+from typing import Tuple, Dict
+from timeit import default_timer as timer
+import gradio as gr
+import os
+
+class_names = []
+
+# Open Food101 class names file and read each line into a list
+with open(class_names_file, 'r') as f:
+    for idx, class_name in enumerate(f):
+        class_names[idx] = class_name.strip() 
+
+### 2. Model and transforms perparation ###
+model, model_transforms = create_vitB16_model(num_classes=len(class_names))
+
+# Load save weights
+model.load_state_dict(torch.load(f='09_pretrained_vit_feature_extractor_food101_20_percent.pth',
+                                 map_location='cpu'))
+
+# 3. Predict Function
+
+def predict(img) -> Tuple[Dict, float]:
+  # Start a timer
+  start_time = timer()
+
+  # Transform the input image for use with vitB16
+  img = model_transforms(img).unsqueeze(dim=0)
+
+  # Put model into eval mode, make prediction
+  model.eval()
+  with torch.inference_mode():
+    # Pass transformed image through the model and turn the prediction logits into probabilities
+    pred_logit = model(img)
+    pred_prob = torch.softmax(pred_logit, dim=1)
+
+  # Create a prediction label and prediction probability dictionary
+  pred_labels_and_probs = {class_names[i]: float(pred_prob[0][i]) for i in range(len(class_names))}
+
+  # Calculate pred time
+  end_time = timer()
+  pred_time = round(end_time - start_time, 4)
+
+  # Return pred dict and pred time
+  return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+
+
+# Create title, description and article
+title = "Object Detection 🤖"
+description = "A [vision Transformer B16 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_16.html) computer vision model to classify 10 classes of Objects from the Cifar10 dataset. [Cifar10](https://pytorch.org/vision/stable/generated/torchvision.datasets.CIFAR10.html?highlight=cifar)"
+article = "Created with 🤎 (and a mixture of mathematics, statistics, and tons of calculations 👩🏽‍🔬) by Arpit Vaghela [GitHub](https://github.com/magnifiques)"
+
+# Create example list
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+demo = gr.Interface(fn=predict,
+                    inputs=gr.Image(type='pil'),
+                    outputs=[gr.Label(num_top_classes=10, label='Predictions'),
+                             gr.Number(label="Prediction time (s)")],
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch(debug=False, # print errors locally?
+            share=True) # generate a publically shareable URL

class_names.txt

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+airplane
+automobile
+bird
+cat
+deer
+dog
+frog
+horse
+ship
+truck

model.py

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+import torch, torchvision
+def create_vitB16_model(num_classes: int=3, seeds: int = 42):
+
+  # 1. Setup pretrained viT Weights
+  weights = torchvision.models.ViT_B_16_Weights.DEFAULT
+
+  # 2. Get transforms
+  transforms = weights.transforms()
+
+  # 3. Setup pretrained instance
+  model = torchvision.models.vit_b_16(weights=weights)
+
+  # 4. Freeze the base layers in the model (this will stop all layers from training)
+  for params in model.parameters():
+    params.requires_grad = False
+
+  # Set seeds for reproducibility
+  torch.manual_seed(seeds)
+
+  # 5. Modify the number of output layers
+  model.heads = torch.nn.Sequential(
+      torch.nn.Linear(in_features=768, out_features=num_classes, bias=True)
+  )
+
+  return model, transforms

requirements.txt

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+torch==2.4.0
+torchvision==0.19.0
+gradio==4.44.0