Describe the feature
Currently, PictoPy requires users to manually select the YOLO model size (nano, small, or medium). While this offers flexibility, it requires users to understand performance–accuracy trade-offs and system limitations.
Introduce an AUTO mode that dynamically selects the most suitable YOLO model at runtime based on contextual information such as input characteristics and system resources.
Dynamic Decision Factors
When AUTO mode is enabled, the system should evaluate multiple signals before choosing the model:
- Image resolution (small images → lighter models)
- Estimated number of faces or objects
- User priority (speed vs accuracy)
- GPU availability and free memory
- Task type (face detection or object detection)
This allows the system to adapt model choice per request instead of relying on a static configuration.
Multi-Stage Selection Strategy
AUTO mode can follow a staged decision approach:
-
Pre-inference checks
Use lightweight models for small images or limited hardware resources.
-
Fast probe detection
Run a quick detection pass using a lightweight model to estimate face/object count.
-
Runtime fallback
Automatically downgrade the model if inference fails (e.g., GPU out-of-memory).
User Benefits
- Smarter default behavior without manual tuning
- Improved performance and resource utilization
- Reduced inference failures on constrained systems
- Better overall user experience, especially for new users
Backward Compatibility
- Existing manual model selection (nano, small, medium) remains unchanged
- AUTO mode is optional and opt-in
- No breaking changes to current workflows , raise an issue for gsoc style in github
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Describe the feature
Currently, PictoPy requires users to manually select the YOLO model size (nano, small, or medium). While this offers flexibility, it requires users to understand performance–accuracy trade-offs and system limitations.
Introduce an AUTO mode that dynamically selects the most suitable YOLO model at runtime based on contextual information such as input characteristics and system resources.
Dynamic Decision Factors
When AUTO mode is enabled, the system should evaluate multiple signals before choosing the model:
This allows the system to adapt model choice per request instead of relying on a static configuration.
Multi-Stage Selection Strategy
AUTO mode can follow a staged decision approach:
Pre-inference checks
Use lightweight models for small images or limited hardware resources.
Fast probe detection
Run a quick detection pass using a lightweight model to estimate face/object count.
Runtime fallback
Automatically downgrade the model if inference fails (e.g., GPU out-of-memory).
User Benefits
Backward Compatibility
Add ScreenShots
Record