đ Bird Species Classifier
The "Bird Species Classifier" is a cutting - edge image classification model. It can identify different bird species from images. By leveraging the EfficientNet architecture and fine - tuning, it achieves high accuracy in recognizing a wide variety of bird species.
đ Quick Start
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")
⨠Features
- Educational and Research Use: Ideal for bird species identification in educational or ecological research.
- Biodiversity Support: Assists in biodiversity monitoring and conservation efforts.
- Enhanced User Experience: Improves the user experience in nature apps and platforms.
đĻ Installation
The installation is mainly about setting up the Python environment and using the transformers library. You can use the code in the "Quick Start" section to load the model.
đģ Usage Examples
Basic Usage
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
extractor = AutoFeatureExtractor.from_pretrained("chriamue/bird-species-classifier")
model = AutoModelForImageClassification.from_pretrained("chriamue/bird-species-classifier")
đ Documentation
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:
| Property |
Details |
| 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:
| Property |
Details |
| 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
â ī¸ Important Note
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
â ī¸ Important Note
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
đ License
This project is licensed under the MIT license.
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