bird-species-classifier

Model Card for "Bird Species Classifier"

This model came from chiramue/bird-species-classifier. This has been retrained using ResNet50 in hopes to get it running using Transformers JS.

Model Description

The "Bird Species Classifier" is a state-of-the-art image classification model designed to identify various bird species from images. It uses the EfficientNet architecture and has been fine-tuned to achieve high accuracy in recognizing a wide range of bird species.

How to Use

You can easily use the model in your Python environment with the following code:

from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("chriamue/bird-species-classifier")
model = AutoModelForImageClassification.from_pretrained("chriamue/bird-species-classifier")

Applications

• Bird species identification for educational or ecological research.
• Assistance in biodiversity monitoring and conservation efforts.
• Enhancing user experience in nature apps and platforms.

Training Data

The model was trained on the "Bird Species" dataset, which is a comprehensive collection of bird images. Key features of this dataset include:

• Total Species: 525 bird species.
• Training Images: 84,635 images.
• Validation Images: 2,625 images.
• Test Images: 2,625 images.
• Image Format: Color images (224x224x3) in JPG format.
• Source: Sourced from Kaggle.

Training Results

The model achieved impressive results after 6 epochs of training:

• Accuracy: 96.8%
• Loss: 0.1379
• Runtime: 136.81 seconds
• Samples per Second: 19.188
• Steps per Second: 1.206
• Total Training Steps: 31,740

These metrics indicate a high level of performance, making the model reliable for practical applications.

Limitations and Bias

• The performance of the model might vary under different lighting conditions or image qualities.
• The model's accuracy is dependent on the diversity and representation in the training dataset. It may perform less effectively on bird species not well represented in the dataset.

Ethical Considerations

This model should be used responsibly, considering privacy and environmental impacts. It should not be used for harmful purposes such as targeting endangered species or violating wildlife protection laws.

Acknowledgements

We would like to acknowledge the creators of the dataset on Kaggle for providing a rich source of data that made this model possible.

See also

• Bird Species Dataset
• Kaggle Dataset
• Bird Species Classifier