AventIQ-AI/Resnet-50-flower-classification

🌺 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.

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📚 Model Details

• Model Architecture: ResNet-50
• Task: Multi-class Flower Classification
• Dataset: Flowers-102 (Oxford Dataset)
• Framework: PyTorch
• Input Image Size: 224x224
• Number of Classes: 102 (Different Flower Categories)
• Quantization: FP16 (for efficiency)

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🚀 Usage

Installation

pip install torch torchvision pillow

Loading the Model

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!")

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📰 Perform Flower Classification

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}")

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📊 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)

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🛠️ 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.

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⚠️ 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.

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✅ Use this fine-tuned ResNet-50 model for accurate and efficient flower classification! 🌺🚀