https://github.com/astrocoding/model-ai-bird-classification-tflite
This repository provides a TensorFlow Lite (TFLite) model for bird classification, leveraging MobileNet V2 for efficient and accurate predictions. 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.
https://github.com/astrocoding/model-ai-bird-classification-tflite
deep-learning image-classification machine-learning mobilenetv2 python tensorflow tensorflow-lite
Last synced: 6 months ago
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This repository provides a TensorFlow Lite (TFLite) model for bird classification, leveraging MobileNet V2 for efficient and accurate predictions. 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.
- Host: GitHub
- URL: https://github.com/astrocoding/model-ai-bird-classification-tflite
- Owner: astrocoding
- Created: 2024-08-01T04:08:09.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-08-01T04:46:56.000Z (over 1 year ago)
- Last Synced: 2025-06-01T07:06:16.039Z (9 months ago)
- Topics: deep-learning, image-classification, machine-learning, mobilenetv2, python, tensorflow, tensorflow-lite
- Language: Jupyter Notebook
- Homepage:
- Size: 11.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# model-ai-bird-classification-tflite
## Overview
This repository provides a TensorFlow Lite (TFLite) model for bird classification, leveraging MobileNet V2 for efficient and accurate predictions. 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.
## Features
- **Pre-trained Model:** Includes a TFLite model optimized for bird classification using MobileNet V2.
- **MobileNet V2 Architecture:** Utilizes MobileNet V2 for efficient computation and reduced model size while maintaining high accuracy.
- **Real-Time Inference:** Designed for on-device deployment with low-latency predictions.
- **High Accuracy:** Achieved through training on a comprehensive dataset of bird images.
- **Easy Integration:** Sample code and instructions provided for integrating the model into mobile applications.
## MobileNet V2
The 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.
## Dataset
The 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).
## Getting Started
1. Clone the repository
```bash
git clone https://github.com/astrocoding/model-ai-bird-classification-tflite.git
```
2. Navigate to the project directory
```bash
cd model-ai-bird-classification-tflite
```
3. Use the deployed tflite model at the folder assets/
## Acknowledgments
- TensorFlow and TensorFlow Lite for the framework and tools.
- MobileNet V2 for its efficient architecture.
- Contributors and researchers who provided datasets and feedback.
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For further information, questions, or issues, please open an issue on this repository or contact me at [gmail](mailto:zaenalalfian20@gmail.com)