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--- |
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license: mit |
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datasets: |
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- chriamue/bird-species-dataset |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: image-classification |
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tags: |
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- biology |
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- image-classification |
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- vision |
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model-index: |
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- name: bird-species-classifier |
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results: |
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- task: |
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type: ImageClassification |
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dataset: |
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type: chriamue/bird-species-dataset |
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name: Bird Species |
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config: default |
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split: validation |
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metrics: |
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- type: accuracy |
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value: 96.8 |
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- type: loss |
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value: 0.1379 |
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--- |
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# Model Card for "Bird Species Classifier" |
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## Model Description |
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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. |
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### How to Use |
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You can easily use the model in your Python environment with the following code: |
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```python |
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from transformers import AutoFeatureExtractor, AutoModelForImageClassification |
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extractor = AutoFeatureExtractor.from_pretrained("chriamue/bird-species-classifier") |
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model = AutoModelForImageClassification.from_pretrained("chriamue/bird-species-classifier") |
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``` |
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### Applications |
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- Bird species identification for educational or ecological research. |
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- Assistance in biodiversity monitoring and conservation efforts. |
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- Enhancing user experience in nature apps and platforms. |
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## Training Data |
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The model was trained on the "Bird Species" dataset, which is a comprehensive collection of bird images. Key features of this dataset include: |
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- **Total Species**: 525 bird species. |
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- **Training Images**: 84,635 images. |
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- **Validation Images**: 2,625 images. |
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- **Test Images**: 2,625 images. |
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- **Image Format**: Color images (224x224x3) in JPG format. |
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- **Source**: Sourced from Kaggle. |
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## Training Results |
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The model achieved impressive results after 6 epochs of training: |
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- **Accuracy**: 96.8% |
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- **Loss**: 0.1379 |
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- **Runtime**: 136.81 seconds |
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- **Samples per Second**: 19.188 |
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- **Steps per Second**: 1.206 |
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- **Total Training Steps**: 31,740 |
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These metrics indicate a high level of performance, making the model reliable for practical applications. |
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## Limitations and Bias |
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- The performance of the model might vary under different lighting conditions or image qualities. |
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- 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. |
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## Ethical Considerations |
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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. |
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## Acknowledgements |
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We would like to acknowledge the creators of the dataset on Kaggle for providing a rich source of data that made this model possible. |
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## See also |
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- [Bird Species Dataset](https://huggingface.co/datasets/chriamue/bird-species-dataset) |
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- [Kaggle Dataset](https://www.kaggle.com/datasets/gpiosenka/100-bird-species/data) |
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- [Bird Species Classifier](https://huggingface.co/dennisjooo/Birds-Classifier-EfficientNetB2) |
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