---

name: model-inference description: "Construct, inspect, and troubleshoot SSD300 model inference internals for the ssd.pytorch repository." metadata: disco-role: operating disable-model-invocation: true license: MIT

model-inference

Use this sub-skill when a task needs to construct or inspect the SSD300 model, its prior boxes, multibox heads, detection decode/NMS path, single-image inference preprocessing, or pretrained weight compatibility.

When to use

• Build or inspect build_ssd(phase, size=300, num_classes=21).
• Explain the VGG base, extra layers, L2 normalization, localization heads, confidence heads, prior boxes, decode, and NMS contracts.
• Diagnose model construction, state-dict loading, class-count/head mismatches, tensor shape/device issues, or the legacy Detect incompatibility on modern PyTorch.
• Plan safe single-image inference preprocessing and postprocessing without overclaiming that test-phase inference works unchanged on modern PyTorch.

Route elsewhere

• Dataset roots, VOC/COCO layout, annotation transforms, augmentation, dataloaders, and training commands: ../data-training/SKILL.md.
• eval.py, test.py, notebook demo, webcam demo, VOC result files, and runnable evaluation/demo command workflows: ../evaluation-demos/SKILL.md.

Start here

1. Read references/model-api.md for signatures, model components, config facts, tensor shapes, and output contracts.
2. Read references/inference-workflow.md before loading weights or performing single-image inference; it includes the safe construction path and the modern PyTorch detection-layer decision point.
3. Read references/troubleshooting.md when imports, phases, state dicts, devices, or test-phase forward passes fail.
4. Use the bundled scripts for focused, low-cost checks:

python scripts/inspect_model_shapes.py --num-classes 21
python scripts/inspect_model_shapes.py --num-classes 21 --phase train --run-forward
python scripts/check_box_utils.py

The scripts assume they are run from a checkout or environment where the repository source modules are importable. They do not download data or weights.

Do not overclaim

• build_ssd('train', 300, 21) constructs and train-phase zero forward has been observed to return (loc, conf, priors) with shapes (1, 8732, 4), (1, 8732, 21), (8732, 4) under a modern Torch runtime, aside from a Variable(..., volatile=True) warning.
• build_ssd('test', ...) constructs the legacy detection module, but a forward pass can fail on modern PyTorch because Detect subclasses torch.autograd.Function with an old-style instance forward. Patch Detect or use a legacy-compatible PyTorch before claiming end-to-end inference/eval works.
• Importing ssd or data may fail before model construction if the COCO label-map default path is absent; see troubleshooting for safe remedies.