Create README.md

README.md

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+---
+library_name: pytorch
+license: mit
+base_model: torchvision/resnet18
+tags:
+- image-classification
+- scene-classification
+- transfer-learning
+- pytorch
+- computer-vision
+metrics:
+- accuracy
+model-index:
+- name: scene-classifier-resnet18
+  results: []
+---
+
+# Scene Classifier - ResNet18
+
+This model is a fine-tuned version of ResNet-18 pretrained on ImageNet. It classifies images into 4 scene categories: cafe, gym, library, and outdoor.
+
+## Model description
+
+This model uses a dataset of video frames extracted from recordings of different indoor and outdoor locations. The ResNet-18 architecture was chosen for its balance of accuracy and computational efficiency, using transfer learning from ImageNet pretrained weights. Only the final fully-connected layer was retrained for the 4-class classification task.
+
+The model is part of a larger pipeline that generates contextual music based on scene classification combined with weather and temporal metadata.
+
+## Intended uses & limitations
+
+**Intended use:**
+- Scene classification for context-aware applications
+- Image-to-music generation pipelines
+- Indoor/outdoor scene detection
+- Educational demonstrations of transfer learning
+
+**Limitations:**
+- Limited to 4 specific scene categories (cafe, gym, library, outdoor)
+- Trained on relatively small dataset extracted from videos
+- May not generalize well to significantly different scene compositions
+- Performance may degrade on low-quality or heavily edited images
+- Indoor scenes may be confused if they share similar visual features
+
+## Training and evaluation data
+
+**Dataset:** Video frame extraction from 4 scene categories
+
+- Classes: cafe, gym, library, outdoor
+- Source: Personal video recordings of various locations
+- Extraction: Sampled every 10th frame from videos
+- Total frames: Approximately 2,400+ images
+- Format: JPEG, 224x224 resolution after preprocessing
+
+The dataset represents real-world indoor and outdoor environments with varying lighting conditions, angles, and compositions.
+
+## Training procedure
+
+### Data preprocessing
+
+Images were preprocessed with resize to 224x224 and converted to tensors.
+
+### Model architecture
+
+- Base model: ResNet-18 (ImageNet pretrained)
+- Modified layer: Final fully-connected layer changed from 1000 classes to 4 classes
+- Transfer learning: All layers except final FC layer retained pretrained weights
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+
+- learning_rate: 1e-4
+- train_batch_size: 32
+- optimizer: Adam with default betas=(0.9, 0.999)
+- loss_function: CrossEntropyLoss
+- num_epochs: 3
+- device: CUDA (GPU accelerated)
+
+### Training results
+
+Training was conducted over 3 epochs with consistent loss reduction:
+
+| Epoch | Training Loss | Status |
+|:-----:|:-------------:|:------:|
+| 1     | 0.4523        | ✓      |
+| 2     | 0.2156        | ✓      |
+| 3     | 0.1089        | ✓      |
+
+Note: Formal validation metrics were not computed during training. Model was validated qualitatively on held-out images.
+
+## Usage
+
+### Loading the model
+
+import torch
+from torchvision import models, transforms
+from PIL import Image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = models.resnet18(weights=None)
+model.fc = torch.nn.Linear(model.fc.in_features, 4)
+model.load_state_dict(torch.load("pytorch_model.pth", map_location=device))
+model = model.to(device)
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+class_labels = ["cafe", "gym", "library", "outdoor"]
+
+### Inference
+
+image = Image.open("your_image.jpg")
+if image.mode != 'RGB':
+    image = image.convert('RGB')
+
+input_tensor = transform(image).unsqueeze(0).to(device)
+
+with torch.no_grad():
+    outputs = model(input_tensor)
+    predicted_idx = outputs.argmax(dim=1).item()
+    predicted_class = class_labels[predicted_idx]
+    confidence = torch.softmax(outputs, dim=1)[0][predicted_idx].item()
+
+print(f"Predicted: {predicted_class} (confidence: {confidence:.2%})")
+
+## Framework versions
+
+- PyTorch: 2.0+
+- Torchvision: 0.15+
+- Python: 3.8+
+- Pillow: 9.0+
+
+## Model Architecture Details
+
+ResNet-18 Structure:
+- Input: 3x224x224 RGB image
+- Convolutional layers with residual connections
+- Global average pooling
+- Final FC layer: 512 to 4 classes
+- Total parameters: approximately 11.7M (only approximately 2K trainable in final layer)
+
+## Additional Information
+
+This model was developed as part of a course project (24-679) exploring multimodal AI systems. 
+It serves as the visual classification component in an image-to-music generation pipeline that combines scene recognition, 
+metadata extraction, weather context, and music synthesis.