How to improve accuracy of Tensorflow camera demo on iOS for retrained graph

Improving Tensorflow Camera Demo Accuracy on iOS for Retrained Graph

TensorFlow’s camera demo on iOS provides a powerful platform for deploying custom object detection models. However, achieving optimal accuracy with a retrained graph often requires careful optimization and understanding of various factors. This guide explores practical techniques to enhance accuracy and streamline the process.

1. Retraining and Model Optimization

1.1 Dataset Quality and Quantity

• Use high-quality images for training.
• Ensure sufficient data for each class to prevent overfitting.
• Augment your dataset with techniques like rotation, cropping, and color variations to improve model generalization.

1.2 Model Architecture and Hyperparameters

• Choose a suitable model architecture, considering complexity and computational resources.
• Fine-tune hyperparameters like learning rate, batch size, and epochs through experimentation.
• Employ techniques like transfer learning to leverage pre-trained models and expedite training.

2. TensorFlow Lite Conversion and Optimization

2.1 Converting to TensorFlow Lite

tflite_convert \
  --saved_model_dir=${MODEL_DIR} \
  --output_file=${OUTPUT_FILE}.tflite \
  --inference_type=FLOAT \
  --input_shape=1,${INPUT_HEIGHT},${INPUT_WIDTH},3

2.2 Optimization Flags

• Use --inference_type=FLOAT for high accuracy.
• Experiment with --quantize for reduced model size, but potential accuracy loss.

3. Camera Demo Integration and Configuration

3.1 Camera Settings

• Optimize camera resolution for optimal performance and accuracy.
• Adjust image resizing and cropping for efficient input to the model.

3.2 Preprocessing and Post-processing

• Apply image normalization to ensure consistent input range.
• Implement non-maximum suppression (NMS) to filter overlapping bounding boxes.

4. Accuracy Evaluation and Validation

4.1 Test Set Evaluation

• Use a separate test set to evaluate model performance objectively.
• Calculate metrics like mean average precision (mAP) and precision-recall curves.

4.2 Real-World Validation

• Deploy the model on a real device and assess its performance in various environments.
• Gather feedback from users and iterate on improvements based on real-world usage.

5. Conclusion

By carefully retraining, optimizing, and integrating your model with the TensorFlow camera demo, you can achieve significantly improved accuracy. Continuously evaluating and iterating on your model based on real-world data is crucial for maximizing performance and achieving reliable object detection in your iOS applications.