Is tensorflow object detection api still working on new models?

TensorFlow General Discussion
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Hi all,
I was just wondering if TensorFlow object detection API still working on new models? The reason for this being that for a long time I haven’t seen any new model added to the repo. On the other hand, mmdetection keeps on adding new models. So, is this the end of tensorflow object detection api ?

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@Vedanshu Welcome to Tensorflow Forum!

While there hasn’t been a recent addition of brand-new models to the TensorFlow Object Detection API (TFOD) model zoo, it’s not an indication that the API is being abandoned. Here are key points to consider:

1. Focus Shift:

  • TFOD development has prioritized optimizing existing models for efficiency and deployability , particularly for edge devices and mobile platforms.
  • This includes exploring model compression techniques, quantization, and model pruning to reduce model size and computational requirements while maintaining accuracy.

2. Integration with TensorFlow Hub:

  • Google has directed efforts towards integrating pre-trained object detection models into TensorFlow Hub , a central repository for sharing reusable model components.
  • This integration offers a more streamlined way to access and experiment with various models within your projects.

3. Incorporation of New Ideas:

  • TFOD continuously integrates advancements from the broader object detection research community , even if not always through adding entirely new models.
  • For example, recent updates have incorporated techniques like Sparse R-CNN and EfficientDet for improved performance and efficiency.

Considerations for Choosing Object Detection Frameworks:

Factor TensorFlow Object Detection API MMDetection
Focus Model optimization, production-ready deployment Cutting-edge research, model diversity
Ease of Use Generally user-friendly API, good documentation Steeper learning curve, more active community
Model Zoo Size Variety of well-established models Wider range of models, including newer architectures
Performance Strong performance on common object detection tasks May offer advantages for specific tasks or datasets
Integration Well-integrated with TensorFlow ecosystem More flexible integration with different backends

Choosing the Right Framework:

  • For production-focused projects: TFOD often excels due to its emphasis on efficiency, ease of use, and deployment.
  • For cutting-edge research or exploring diverse model architectures: MMDetection might be a better fit.

Key Takeaways:

  • TFOD’s development hasn’t ceased, but its focus has shifted towards model optimization and deployment rather than solely adding new models.
  • MMDetection offers a broader range of model choices and emphasizes research.
  • The optimal choice depends on your specific project requirements and priorities.

Consider these factors when selecting the framework that best aligns with your needs.

Let us know if this helps!