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name: inference-evaluation description: "Route for SSD Keras inference, decoding, visualization, and evaluation." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Inference and Evaluation

Use this route when the task is about loading a trained SSD model, decoding detections, visualizing boxes, or measuring performance on VOC / COCO-style data.

What this route covers

• building inference or inference-fast models
• loading saved SSD models with custom objects
• decoding raw predictions
• interpreting or visualizing boxes on images
• computing VOC-style average precision and mean AP
• exporting COCO predictions to JSON
• remapping predictions back to the original image frame after transforms

What this route excludes

• training loops
• class-count adaptation and weight sampling
• low-level dataset parsing details beyond what evaluation needs

Send those tasks to training or data-preparation.

First things to check

1. Open references/model-architecture.md to confirm the decode / model-mode contract.
2. Open references/data-formats.md if the evaluation data needs a parser or category mapping.
3. Open references/workflows.md for the notebook-derived inference and evaluation steps.
4. Run scripts/smoke.py for a tiny synthetic decode and evaluation check.

Typical workflow

1. Load the model

• Build the model in inference or inference_fast mode when you want the decoder inside the graph.
• Load a saved model with the custom layers in custom_objects.
• If you only have raw predictions, decode them with the NumPy helpers instead of rebuilding the graph.

2. Prepare images

• Resize and convert channels with the same image-size assumptions used during training.
• Use DataGenerator plus inverse transforms when predictions need to be mapped back to the original image coordinates.

3. Decode and filter predictions

• Use decode_detections when you want the original per-class NMS behavior.
• Use decode_detections_fast when a faster global-NMS path is acceptable.
• Keep an eye on confidence_thresh, iou_threshold, top_k, and normalize_coords when the output looks empty or shifted.

4. Evaluate

• Use Evaluator for VOC-style metrics.
• Choose the AP mode that matches the benchmark you want to reproduce.
• For COCO-style export, first build the category map and then write the predictions to JSON.

Useful source objects

• ssd_300
• ssd_512
• build_model
• DecodeDetections
• DecodeDetectionsFast
• decode_detections
• decode_detections_fast
• Evaluator
• get_coco_category_maps
• predict_all_to_json
• apply_inverse_transforms

Script path

• scripts/smoke.py — builds a tiny inference model, decodes a synthetic prediction, and runs a toy evaluator / COCO-export check.

Quick decision guide

• Need per-class average precision or mean AP? Use Evaluator.
• Need a JSON file for COCO evaluation? Use predict_all_to_json.
• Need a simple confidence-thresholded box list? Use decode_detections or decode_detections_fast.