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README.md

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+---
+license: mit
+tags:
+- image-classification
+- cheese
+- texture
+- computer-vision
+- pytorch
+- transfer-learning
+- automl
+datasets:
+- aslan-ng/cheese-image
+metrics:
+- accuracy
+model-index:
+- name: Cheese Texture Classifier (AutoML)
+  results:
+  - task:
+      type: image-classification
+      name: Cheese Texture Classification
+    dataset:
+      type: aslan-ng/cheese-image
+      name: Cheese Image Dataset
+    metrics:
+    - type: accuracy
+      value: 80.00
+      name: Test Accuracy
+---
+
+# Cheese Texture Classifier (AutoML)
+
+This model performs 4-class texture classification on cheese images using AutoML-optimized transfer learning.
+
+## Model Description
+
+- **Architecture**: Transfer Learning with resnet34
+- **Task**: 4-class texture classification (Low, Medium-Low, Medium-High, High texture)
+- **Input**: 224x224 RGB images
+- **Output**: 4-class probability distribution
+
+## Training Details
+
+- **Dataset**: [aslan-ng/cheese-image](https://huggingface.co/datasets/aslan-ng/cheese-image)
+- **AutoML Method**: Optuna with 20 trials
+- **Transfer Learning**: Pre-trained resnet34 backbone
+- **Early Stopping**: Yes (patience=10)
+- **Max Epochs**: 50
+
+## Performance
+
+- **Test Accuracy**: 80.00%
+- **Validation Accuracy**: 50.00%
+- **Test Loss**: 1.6345
+
+## Best Hyperparameters (AutoML Optimized)
+
+```json
+{
+  "model_name": "resnet34",
+  "dropout_rate": 0.4682019316470914,
+  "learning_rate": 0.00027817005315620047,
+  "weight_decay": 1.4677013775851028e-05,
+  "batch_size": 2
+}
+```
+
+## Usage
+
+```python
+import torch
+import torch.nn as nn
+from PIL import Image
+import torchvision.transforms as transforms
+import torchvision.models as models
+
+# Load model (define TransferLearningModel class first)
+model = TransferLearningModel(num_classes=4, dropout_rate=0.4682019316470914, model_name='resnet34')
+model.load_state_dict(torch.load('pytorch_model.bin'))
+model.eval()
+
+# Preprocess image
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+# Load and preprocess image
+image = Image.open('cheese_image.jpg').convert('RGB')
+input_tensor = transform(image).unsqueeze(0)
+
+# Make prediction
+with torch.no_grad():
+    output = model(input_tensor)
+    probabilities = torch.softmax(output, dim=1)
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+
+class_names = ["Low Texture", "Medium-Low Texture", "Medium-High Texture", "High Texture"]
+print(f"Predicted class: {class_names[predicted_class]}")
+```
+
+## Class Definitions
+
+- **Class 0 (Low Texture)**: Texture values <= 0.425
+- **Class 1 (Medium-Low Texture)**: Texture values 0.425 < x <= 0.600
+- **Class 2 (Medium-High Texture)**: Texture values 0.600 < x <= 0.775
+- **Class 3 (High Texture)**: Texture values > 0.775
+
+## AutoML Features
+
+- **Hyperparameter Optimization**: Optuna with 20 trials
+- **Architecture Search**: ResNet18 vs ResNet34
+- **Transfer Learning**: Pre-trained ImageNet weights
+- **Early Stopping**: Prevents overfitting
+- **Fixed Budget**: 20 trials, 10-minute timeout
+
+## Limitations
+
+- Trained on a very small dataset (30 images)
+- Texture classification may not generalize to all cheese types
+- Performance may vary with different lighting conditions or image quality
+
+## Citation
+
+If you use this model, please cite the original dataset:
+
+```bibtex
+@dataset{aslan-ng/cheese-image,
+  title={Cheese Image Dataset},
+  author={Aslan Noorghasemi},
+  year={2024},
+  url={https://huggingface.co/datasets/aslan-ng/cheese-image}
+}
+```

config.json

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+{
+  "architecture": "TransferLearning_resnet34",
+  "num_classes": 4,
+  "dropout_rate": 0.4682019316470914,
+  "model_name": "resnet34",
+  "input_size": [
+    3,
+    224,
+    224
+  ],
+  "class_names": [
+    "Low Texture",
+    "Medium-Low Texture",
+    "Medium-High Texture",
+    "High Texture"
+  ],
+  "texture_bins": [
+    0.42500000447034836,
+    0.6000000238418579,
+    0.7750000059604645
+  ],
+  "best_hyperparameters": {
+    "model_name": "resnet34",
+    "dropout_rate": 0.4682019316470914,
+    "learning_rate": 0.00027817005315620047,
+    "weight_decay": 1.4677013775851028e-05,
+    "batch_size": 2
+  },
+  "test_accuracy": 80.0,
+  "validation_accuracy": 50.0,
+  "automl_trials": 20,
+  "transfer_learning": true
+}

pytorch_model.bin

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+size 85810707

training_results.json

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+    "test_acc": 80.0,
+    "test_loss": 1.6345435877641041
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+  "test_accuracy": 80.0,
+  "test_loss": 1.6345435877641041,
+  "best_hyperparameters": {
+    "model_name": "resnet34",
+    "dropout_rate": 0.4682019316470914,
+    "learning_rate": 0.00027817005315620047,
+    "weight_decay": 1.4677013775851028e-05,
+    "batch_size": 2
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+  "automl_trials": 20,
+  "transfer_learning": true
+}