How to increase fps in tensorflow model object detection model

We have merged media pipeline model and tensor flow object detection model, but after merging fps has reduced in tensor flow object detection model.

  1. How to increase fps in tensor flow object detection model?
  2. How to increase accuracy of the model and decrease the log error after training the model?? Any methods to reduce log errors?
    Currently it is 2fps we need to increase the fps

@VIVEK_JAIN Welcome to Tensorflow Forum!

Increasing FPS and improving accuracy in your merged media pipeline and TensorFlow object detection model requires a multi-pronged approach:

Increasing FPS:

  • Model Optimization:
    • Quantization: Convert your model from FP32 to int8 for efficient on-device inference.
    • Model pruning: Remove unnecessary weights and connections.
    • Model architecture selection: Choose a lightweight model like MobileNet or EfficientNet.
  • Hardware Acceleration:
    • Utilize GPUs or dedicated AI accelerators like TPUs or Coral Edge TPU.
    • Leverage TensorRT for optimized inference on NVIDIA GPUs.
  • Data Preprocessing:
    • Reduce input image resolution while maintaining sufficient information for object detection.
    • Batch processing multiple images together can improve efficiency.
  • Code Optimization:
    • Use efficient data structures and algorithms in your media pipeline.
    • Avoid unnecessary loops and calculations.

Improving Accuracy and Reducing Log Errors:

  • Training Data:
    • Expand your training dataset with diverse examples and realistic scenarios.
    • Ensure proper data labeling and address any annotation errors.
    • Consider data augmentation techniques for improvedgeneralizability.
  • Training Process:
    • Adjust hyperparameters like learning rate, batch size, and optimizer.
    • Utilize early stopping to prevent overfitting.
    • Analyze the loss function and gradient values to identify training issues.
  • Model Architecture:
    • Fine-tune a pre-trained model on your specific data.
    • Experiment with different model architectures and hyperparameters.
    • Implement attention mechanisms or other advanced techniques for better feature extraction.

Specific Tips for Current Scenario:

  • Analyze the log errors to understand their source (e.g., missing data, invalid annotations, model errors).
  • Measure FPS for both the media pipeline and the object detection model separately to identify the bottleneck.
  • Start with small improvements and gradually test their impact on FPS and accuracy to avoid regressions.
  • Consider using profiling tools like TensorBoard or NVIDIA Nsight to identify performance bottlenecks.

Reaching 2 FPS:

Achieving 2 FPS might require significant optimization depending on your hardware, model complexity, and data size. Start with the above methods and consider exploring advanced techniques like model distillation or knowledge transfer to further improve performance.

Let us know if this helps!