Default to optimal confidence from model-eval

[leeclemnet]

What does this PR do?

Model eval calculates F1-optimal confidence thresholds but they aren't currently used for model inference. This PR together with https://github.com/roboflow/roboflow/pull/11053 makes it so. This feature applies only to the inference_models pathway. The legacy inference pathways keep their existing default confidence.

The key changes are

Wire recommendedParameters from model_eval through to inference

• New RecommendedParameters pydantic model in inference_models/weights_providers/entities.py (confidence, per_class_confidence); parsed from Roboflow API in weights_providers/roboflow.py and threaded through auto_loaders/core.py → initialize_model
• Auto-loader injects it onto the model instance via hasattr(type(model), "recommended_parameters"), so model classes opt in by declaring a class-level recommended_parameters: Optional[RecommendedParameters] = None

New ConfidenceFilter and post_process_with_confidence_filter() wrapper (inference_models/models/base/)

• Encodes the 4-tier priority chain: explicit user → per-class optimal → global optimal → hardcoded default
• Wrapper rewrites the confidence kwarg to the filter's floor before calling post_process (so NMS keeps boxes any class might still want), then refines per-class on the way out
• Added on ObjectDetectionModel, InstanceSegmentationModel, KeypointsDetectionModel, SemanticSegmentationModel, MultiLabelClassificationModel. Single-label classification deliberately opts out (top-1 always wins)

Inference adapters route through the new wrapper (inference/core/models/inference_models_adapters.py)

• OD/IS/KP/multi-label classification adapters call post_process_with_confidence_filter instead of post_process
• Multi-label response builder now reads prediction.class_ids directly instead of re-thresholding the full confidence vector — the model's per-class decision survives to the API response

API request schema (inference/core/entities/requests/inference.py)

• ObjectDetectionInferenceRequest.confidence default flipped from 0.4 → None so model-eval recommendations can take effect; explicit user values still win. Description updated to document the fallback chain

OLD inference path compatibility (inference/core/models/{object_detection,instance_segmentation,classification}_base.py)

• Coalesce confidence is None → existing per-class default at the entry of infer() / make_response(), so the USE_INFERENCE_MODELS=false matrix variant still works after the request default flipped to None

Plumbing

• BackendType moved from auto_loaders/entities.py → weights_providers/entities.py to break a circular import introduced by the new schema
• CI workflow integration_tests_workflows_x86.yml overlays a local source build of inference_models (pip install --force-reinstall --no-deps ./inference_models) so adapter changes are actually exercised against pinned PyPI requirements

Dependencies:

• https://github.com/roboflow/roboflow/pull/11053 <- non-blocking, inference will fall back to existing 0.4 default confidence for model docs without recommendedParameters

Type of Change

• New feature (non-breaking change that adds functionality)

Testing

• I have tested this change locally
• I have added/updated tests for this change

Test details:

• New: test_confidence_filter.py, test_confidence_filter_attribute.py, test_post_process_filter.py, test_recommended_parameters.py, plus expanded test_roboflow.py and test_core.py coverage

Checklist

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code where necessary, particularly in hard-to-understand areas
• My changes generate no new warnings or errors
• I have updated the documentation accordingly (if applicable)

Additional Context