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: 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 Step…
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