initial commit

.gitattributes

@@ -33,3 +33,5 @@ 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
+effnetb2_feature_extractor_20_percent_10_epochs.pth.pth filter=lfs diff=lfs merge=lfs -text
+effnetb2_feature_extractor_20_percent_10_epochs.pth filter=lfs diff=lfs merge=lfs -text

app.py

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+
+### 1. Imports and class names setup ### 
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ["pizza", "steak", "sushi"]
+
+### 2. Model and transforms preparation ###
+
+# Create EffNetB2 model
+effnetb2, effnetb2_transforms = create_effnetb2_model(
+    num_classes=3, # len(class_names) would also work
+)
+
+# Load saved weights
+effnetb2.load_state_dict(
+    torch.load(
+        f="effnetb2_feature_extractor_20_percent_10_epochs.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+# Create predict function
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+    
+    # Transform the target image and add a batch dimension
+    img = effnetb2_transforms(img).unsqueeze(0)
+    
+    # Put model into evaluation mode and turn on inference mode
+    effnetb2.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+    
+    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    
+    # Calculate the prediction time
+    pred_time = round(timer() - start_time, 5)
+    
+    # Return the prediction dictionary and prediction time 
+    return pred_labels_and_probs, pred_time
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = r"FoodVision Mini 🍕🍗🥗"
+description = """
+# FoodVision Mini
+
+FoodVision Mini is a hands-on project inspired by Daniel Bourke's PyTorch tutorial series, focusing on food image classification. Throughout this project, I developed essential skills in **deep learning**, particularly in utilizing **transfer learning** techniques with the **EfficientNetB2** architecture to optimize model performance.
+
+## Key Skills Acquired
+
+- **Data Preprocessing**: Learning how to prepare and augment datasets for improved model training.
+- **Model Training and Evaluation**: Gaining experience in training neural networks, monitoring performance metrics, and fine-tuning hyperparameters.
+- **Deployment Techniques**: Understanding how to deploy machine learning models using frameworks like Gradio for user interaction.
+
+The project emphasizes practical application by building a lightweight model capable of classifying images of **Pizza**, **Steak**, and **Sushi**, showcasing the effectiveness of EfficientNetB2 in achieving high accuracy with limited computational resources. Overall, FoodVision Mini serves as a comprehensive introduction to real-world applications of machine learning in food recognition, leveraging advanced techniques learned from the tutorial series.
+"""
+article = "This is an amazing project that aims to help learn the Basics of Model Deployment (MLOPS)"
+
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    # Create examples list from "examples/" directory
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch()

effnetb2_feature_extractor_20_percent_10_epochs.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:75b6fb7079a80d838ec11d46a46a8df95f016dc3b7838c453a54f69da1b72306
+size 31298170

model.py

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+
+import torch
+import torchvision
+from torch import nn
+
+def create_effnetb2_model(num_classes:int=3, 
+                          seed:int=42):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of classes in the classifier head. 
+            Defaults to 3.
+        seed (int, optional): random seed value. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): EffNetB2 feature extractor model. 
+        transforms (torchvision.transforms): EffNetB2 image transforms.
+    """
+    # Create EffNetB2 pretrained weights, transforms and model
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    # Freeze all layers in base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1408, out_features=num_classes),
+    )
+    
+    return model, transforms

requirements.txt

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+torch==2.4.0
+torchvision==0.19.0
+gradio=5.0.2