Resnet-50-flower-classification
| Property | Value |
|---|---|
| Model Type | Image Classification |
| Architecture | ResNet-50 |
| Dataset | Flowers-102 (Oxford Dataset) |
| Input Size | 224x224 pixels |
| Classes | 102 |
| Accuracy | 92.8% |
| Model URL | HuggingFace |
What is Resnet-50-flower-classification?
This is a specialized computer vision model based on the ResNet-50 architecture, fine-tuned specifically for flower classification. The model has been optimized to identify 102 different flower categories with high accuracy, utilizing FP16 quantization for efficient inference while maintaining performance.
Implementation Details
The model implements a fine-tuned version of ResNet-50, adapted for the specific task of flower classification. It processes 224x224 RGB images and has been trained using the Adam optimizer with a learning rate of 1e-4 over 20 epochs. The implementation includes FP16 quantization for optimal performance and reduced memory footprint.
- Trained on 8,189 flower images across 102 categories
- Uses PyTorch framework with custom final classification layer
- Implements standard image preprocessing with normalization
- Optimized with FP16 quantization for efficient inference
Core Capabilities
- Multi-class classification of 102 flower species
- High accuracy (92.8%) with balanced precision (91.5%) and recall (90.9%)
- Fast inference speed due to FP16 optimization
- Robust performance across various image conditions
Frequently Asked Questions
Q: What makes this model unique?
This model combines the robust ResNet-50 architecture with specialized fine-tuning for flower classification, achieving high accuracy while maintaining efficient inference through FP16 quantization. Its balanced performance metrics make it particularly suitable for real-world applications.
Q: What are the recommended use cases?
The model is ideal for botanical applications, educational tools, garden planning software, and flower identification apps. However, users should be aware of potential limitations with similar-looking flowers and varying image conditions.
