{"id":23090112,"url":"https://github.com/astrocoding/model-ai-bird-classification-tflite","last_synced_at":"2025-09-23T11:21:20.532Z","repository":{"id":251147406,"uuid":"836537227","full_name":"astrocoding/model-ai-bird-classification-tflite","owner":"astrocoding","description":"This repository provides a TensorFlow Lite (TFLite) model for bird classification, leveraging MobileNet V2 for efficient and accurate predictions. 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MobileNet V2 is known for its lightweight architecture, which makes it ideal for on-device inference, allowing the model to perform real-time classification on mobile and edge devices.\n\n## Features\n - **Pre-trained Model:** Includes a TFLite model optimized for bird classification using MobileNet V2.\n - **MobileNet V2 Architecture:** Utilizes MobileNet V2 for efficient computation and reduced model size while maintaining high accuracy.\n - **Real-Time Inference:** Designed for on-device deployment with low-latency predictions.\n - **High Accuracy:** Achieved through training on a comprehensive dataset of bird images.\n - **Easy Integration:** Sample code and instructions provided for integrating the model into mobile applications.\n\n## MobileNet V2\nThe model uses MobileNet V2 as its backbone architecture. MobileNet V2 is a depthwise separable convolutional network that provides a good balance between performance and computational efficiency, making it well-suited for mobile and embedded applications.\n\n## Dataset\nThe model has been trained on a diverse dataset of bird images, covering various species to ensure robust classification performance. For details on the dataset used, please refer to the [kaggle](https://www.kaggle.com/datasets/gpiosenka/100-bird-species) (525 birds species).\n\n## Getting Started\n1. Clone the repository\n    ```bash\n      git clone https://github.com/astrocoding/model-ai-bird-classification-tflite.git\n    ```\n2. Navigate to the project directory\n    ```bash\n      cd model-ai-bird-classification-tflite\n    ```\n3. Use the deployed tflite model at the folder assets/\n\n## Acknowledgments\n- TensorFlow and TensorFlow Lite for the framework and tools.\n- MobileNet V2 for its efficient architecture.\n- Contributors and researchers who provided datasets and feedback.\n\n---\n\nFor further information, questions, or issues, please open an issue on this repository or contact me at [gmail](mailto:zaenalalfian20@gmail.com)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastrocoding%2Fmodel-ai-bird-classification-tflite","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fastrocoding%2Fmodel-ai-bird-classification-tflite","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fastrocoding%2Fmodel-ai-bird-classification-tflite/lists"}