adds model card

README.md

@@ -12,4 +12,62 @@ tags:
 - biology
 - image-classification
 - vision
----
+---
+
+# Model Card for "Bird Species Classifier"
+
+## 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:
+
+```python
+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](https://huggingface.co/datasets/chriamue/bird-species-dataset)
+- [Kaggle Dataset](https://www.kaggle.com/datasets/gpiosenka/100-bird-species/data)
+- [Bird Species Classifier](https://huggingface.co/dennisjooo/Birds-Classifier-EfficientNetB2)