| ### **🌺 ResNet-50 Flowers Classification Model** | |
| This repository hosts a fine-tuned **ResNet-50-based** model optimized for **flower classification** using the **Flowers-102 dataset**. The model classifies images into **102 different flower categories**. | |
| --- | |
| ## **📚 Model Details** | |
| - **Model Architecture**: ResNet-50 | |
| - **Task**: Multi-class Flower Classification | |
| - **Dataset**: Flowers-102 ([Oxford Dataset](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/)) | |
| - **Framework**: PyTorch | |
| - **Input Image Size**: 224x224 | |
| - **Number of Classes**: 102 (Different Flower Categories) | |
| - **Quantization**: FP16 (for efficiency) | |
| --- | |
| ## **🚀 Usage** | |
| ### **Installation** | |
| ```bash | |
| pip install torch torchvision pillow | |
| ``` | |
| ### **Loading the Model** | |
| ```python | |
| import torch | |
| import torchvision.models as models | |
| # Step 1: Define the model architecture (Must match the trained model) | |
| model = models.resnet50(pretrained=False) | |
| model.fc = torch.nn.Linear(in_features=2048, out_features=102) # Ensure output matches 102 classes | |
| # Step 2: Load the fine-tuned model weights | |
| model_path = "/content/resnet50_flowers_model.pth" # Ensure the file is in the correct directory | |
| model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu"))) | |
| # Step 3: Set model to evaluation mode | |
| model.eval() | |
| print("✅ Model loaded successfully and ready for inference!") | |
| ``` | |
| --- | |
| ### **📰 Perform Flower Classification** | |
| ```python | |
| from PIL import Image | |
| import torchvision.transforms as transforms | |
| # Load the image | |
| image_path = "/content/sample_flower.jpg" # Replace with your test image | |
| image = Image.open(image_path).convert("RGB") # Ensure 3-channel format | |
| # Define preprocessing (same as used during training) | |
| transform = transforms.Compose([ | |
| transforms.Resize((224, 224)), # Resize to match model input | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |
| # Apply transformations | |
| image = transform(image).unsqueeze(0) # Add batch dimension | |
| # Perform inference | |
| with torch.no_grad(): | |
| output = model(image) | |
| # Convert output to class prediction | |
| predicted_class = torch.argmax(output, dim=1).item() | |
| print(f"✅ Predicted Flower Label: {predicted_class}") | |
| ``` | |
| --- | |
| ## **📊 Evaluation Results** | |
| After fine-tuning, the model was evaluated on the **Flowers-102 Dataset**, achieving the following performance: | |
| | **Metric** | **Score** | | |
| |------------------|----------| | |
| | **Accuracy** | 92.8% | | |
| | **Precision** | 91.5% | | |
| | **Recall** | 90.9% | | |
| | **F1-Score** | 91.2% | | |
| | **Inference Speed** | Fast (Optimized with FP16) | | |
| --- | |
| ## **🛠️ Fine-Tuning Details** | |
| ### **Dataset** | |
| The model was trained on the **Flowers-102 dataset**, which contains **8,189 flower images** classified into **102 categories**. | |
| ### **Training Configuration** | |
| - **Number of epochs**: 20 | |
| - **Batch size**: 16 | |
| - **Optimizer**: Adam | |
| - **Learning rate**: 1e-4 | |
| - **Loss Function**: Cross-Entropy | |
| - **Evaluation Strategy**: Validation at each epoch | |
| ### **Quantization** | |
| The model was quantized using **FP16 precision**, reducing latency and memory usage while maintaining high accuracy. | |
| --- | |
| ## **⚠️ Limitations** | |
| - **Misclassification risk**: The model may incorrectly classify similar-looking flowers. | |
| - **Dataset bias**: Performance may vary based on background, lighting, and image quality. | |
| - **Generalization**: The model was trained on a specific dataset and may not generalize well to unseen flower species. | |
| --- | |
| ✅ **Use this fine-tuned ResNet-50 model for accurate and efficient flower classification!** 🌺🚀 | |