Birds-Classifier-EfficientNetB2
| Property | Value |
|---|---|
| Model Type | Image Classification |
| Base Architecture | EfficientNetB2 |
| Dataset Size | 84,635 training images |
| Test Accuracy | 99.12% |
| Author | dennisjooo |
| Model URL | Hugging Face |
What is Birds-Classifier-EfficientNetB2?
Birds-Classifier-EfficientNetB2 is a specialized image classification model designed to identify 525 different bird species. Built on Google's EfficientNetB2 architecture and fine-tuned on the comprehensive gpiosenka/100-bird-species dataset, this model achieves remarkable accuracy in bird species identification with 99.12% test accuracy.
Implementation Details
The model leverages advanced image preprocessing and augmentation techniques, including random rotation and horizontal flipping. Training was conducted using PyTorch on a P100 GPU, implementing Lightning and Torchmetrics libraries for optimal performance.
- Training regime: fp32 with Adam optimizer
- Learning rate: 1e-3 with reduce-on-plateau scheduling
- Batch size: 64 images
- Data augmentation: 10° rotation and horizontal flips
- Early stopping with 10-epoch patience
Core Capabilities
- Accurate identification of 525 bird species
- Robust performance with 98.59% validation accuracy
- Easy integration with Hugging Face transformers
- Support for both direct model usage and pipeline implementation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional accuracy in bird species classification, achieving 99.12% test accuracy across 525 species. The implementation of EfficientNetB2 architecture combined with careful data augmentation makes it particularly robust for real-world applications.
Q: What are the recommended use cases?
The model is ideal for bird watching applications, wildlife monitoring systems, ecological research, and educational tools. It can be easily integrated into both web and mobile applications using the Hugging Face transformers library.
