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 ?
@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!