Bird Species Classifier using TensorFlow Lite
Budget: $30 – $250 USD
I need an experienced machine learning engineer or data scientist to train a TensorFlow Lite (TFLite) model for bird species classification using the CUB-200 dataset. The model will be integrated into a Flutter app for real-time or offline bird identification.
Scope of Work:
Dataset Preparation:
Utilize the CUB-200 dataset (provided or downloaded).
Preprocess the images (resizing, normalization, augmentation as needed).
Model Requirements:
Use a pre-trained MobileNetV2 or similar lightweight model for transfer learning.
Fine-tune the model to classify all 200 bird species.
Optimize the model for mobile deployment with TensorFlow Lite.
Deliverables:
The trained TFLite model file (.tflite).
Training and validation accuracy report (aiming for at least 85% validation accuracy).
Clear, commented codebase (preferably in Python with TensorFlow/Keras).
Instructions to retrain the model, if needed.
Additional Requirements:
Incorporate regularization and augmentation to minimize overfitting.
Save the best model during training based on validation accuracy.
Skills Required:
TensorFlow / Keras
Machine Learning and Deep Learning
Python Programming
Experience with TFLite conversion and optimization
Notes:
I’ll provide access to the dataset if needed.
Tag Relevant Skills: TensorFlow, Machine Learning, Python, Keras, TensorFlow Lite.
Ask for Portfolio: Request links to similar projects the freelancer has worked on.
Purpose of the Project:
The trained TFLite model will be integrated into my Flutter application to allow users to upload photos of birds and receive instant identification of the bird species. The model must be optimized for mobile use to ensure quick predictions while maintaining high accuracy.
Scope of Work:
Dataset Preparation:
Utilize the CUB-200 dataset (provided or downloaded).
Preprocess the images (resizing, normalization, augmentation as needed).
Model Requirements:
Use a pre-trained MobileNetV2 or similar lightweight model for transfer learning.
Fine-tune the model to classify all 200 bird species.
Optimize the model for mobile deployment with TensorFlow Lite.
Deliverables:
The trained TFLite model file (.tflite).
Training and validation accuracy report (aiming for at least 85% validation accuracy).
Clear, commented codebase (preferably in Python with TensorFlow/Keras).
Instructions to retrain the model, if needed.
Additional Requirements:
Incorporate regularization and augmentation to minimize overfitting.
Save the best model during training based on validation accuracy.
Skills Required:
TensorFlow / Keras
Machine Learning and Deep Learning
Python Programming
Experience with TFLite conversion and optimization
Notes:
I’ll provide access to the dataset if needed.
Tag Relevant Skills: TensorFlow, Machine Learning, Python, Keras, TensorFlow Lite.
Ask for Portfolio: Request links to similar projects the freelancer has worked on.
Purpose of the Project:
The trained TFLite model will be integrated into my Flutter application to allow users to upload photos of birds and receive instant identification of the bird species. The model must be optimized for mobile use to ensure quick predictions while maintaining high accuracy.