merging

README.md

@@ -1,17 +1,17 @@
 ---
 license: mit
 datasets:
-- chriamue/bird-species-dataset
+  - chriamue/bird-species-dataset
 language:
-- en
+  - en
 metrics:
-- accuracy
+  - accuracy
 library_name: transformers
 pipeline_tag: image-classification
 tags:
-- biology
-- image-classification
-- vision
+  - biology
+  - image-classification
+  - vision
 model-index:
   - name: bird-species-classifier
     results:
@@ -32,9 +32,11 @@ model-index:
 # 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
@@ -45,11 +47,13 @@ model = AutoModelForImageClassification.from_pretrained("chriamue/bird-species-c
 ```
 
 ### 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.
@@ -60,6 +64,7 @@ The model was trained on the "Bird Species" dataset, which is a comprehensive co
 - **Source**: Sourced from Kaggle.
 
 ## Training Results
+
 The model achieved impressive results after 6 epochs of training:
 
 - **Accuracy**: 96.8%
@@ -72,14 +77,17 @@ The model achieved impressive results after 6 epochs of training:
 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. 
+
+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