MobileNetV3Large-Bird-Classification-Kaggle
| Property | Value |
|---|---|
| Author | Daniel Glownia |
| Architecture | MobileNetV3Large with custom dense layers |
| Training Data | 85,085 images, 500 species |
| Model URL | Hugging Face |
What is MobileNetV3Large-Bird-Classification-Kaggle?
This is a specialized bird classification model capable of identifying 500 different bird species with high accuracy. Built on the efficient MobileNetV3Large architecture, it's been trained on a comprehensive dataset of over 85,000 images, achieving impressive validation accuracy of 93.36%.
Implementation Details
The model utilizes transfer learning with MobileNetV3 as the base architecture, enhanced with custom dense layers for classification. The implementation includes strategic dropout layers for regularization and is trained using Adam optimizer with Categorical Cross Entropy loss over 100 epochs.
- Input size: 224 x 224 x 3
- Dense layers: 256 → 128 → 64 → 500 (output)
- Dropout rate: 0.2 between dense layers
- Batch size: 256
Core Capabilities
- Classification of 500 distinct bird species
- 92.20% test accuracy
- Handles images with single birds occupying 50%+ of pixels
- Robust to image noise including watermarks
Frequently Asked Questions
Q: What makes this model unique?
The model combines the efficiency of MobileNetV3Large with a carefully designed dense layer architecture, achieving high accuracy while maintaining computational efficiency. It's trained on a diverse dataset with specific characteristics (80% male birds, consistent bird presence and size in images).
Q: What are the recommended use cases?
This model is ideal for bird species identification in controlled photography where a single bird is clearly visible and occupies a significant portion of the image. It's particularly effective for male bird identification, given the training data composition.
