---

name: inference description: "Run DAMO-YOLO image, video, and camera inference with Torch, ONNX, or TensorRT engines." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

inference

Use this sub-skill when the task is to run or troubleshoot DAMO-YOLO demo inference on images, videos, or a live camera, or when the user needs to choose between Torch, ONNX, and TensorRT runtime engines.

Route here for

• Image, video, and camera demo workflows with a DAMO-YOLO config and an engine artifact.
• Choosing .pth/.pt, .onnx, or .trt inference and explaining required optional runtimes.
• Setting --device, --infer_size, --conf, --end2end, output/visualization behavior, and class labels.
• Debugging missing OpenCV, Pillow, torchvision, ONNX Runtime, TensorRT/CUDA Python, invalid input media, or class-name mismatch failures.

Route elsewhere

• Training, finetuning, optimizers, distributed launch, and checkpoint production: use the training sub-skill.
• COCO evaluation loops, metric reproduction, and multi-GPU dataset inference: use evaluation/training integration guidance rather than this demo route.
• ONNX/TensorRT export implementation, converter internals, TensorRT evaluation, and partial quantization: use the deployment sub-skill.

Start here

1. Read engine-and-data-flow.md to choose the runtime engine and understand preprocessing, postprocessing, visualization, class names, and device behavior.
2. Read demo-workflows.md for concrete image/video/camera commands using the bundled safe helper at scripts/damo_yolo_safe_demo.py.
3. If you need to understand why a source file was bundled, adapted, or kept reference-only, read source-decisions.md.
4. If anything fails, use troubleshooting.md before changing configs or reinstalling dependencies.

Fast command templates

Replace the config, engine, and media paths with user-owned files. Use the resolved path to the bundled scripts/damo_yolo_safe_demo.py helper. Keep the config matched to the engine checkpoint/export; if the config reads relative TinyNAS structure files, run from the directory that contains those config-relative assets or make paths absolute in the config.

# Torch checkpoint on an image; falls back to CPU if CUDA is unavailable.
python sub-skills/inference/scripts/damo_yolo_safe_demo.py image \
  -f /path/to/damoyolo_config.py \
  --engine /path/to/damoyolo_checkpoint.pth \
  --path /path/to/example.jpg \
  --infer-size 640 640 --device cuda --conf 0.6 --output-dir demo

# ONNX engine on a video; requires onnxruntime.
python sub-skills/inference/scripts/damo_yolo_safe_demo.py video \
  -f /path/to/damoyolo_config.py \
  --engine /path/to/damoyolo.onnx \
  --path /path/to/input.mp4 \
  --infer-size 640 640 --device cuda --conf 0.6 --output-dir demo

# TensorRT engine from a camera; requires TensorRT + CUDA Python and usually CUDA.
python sub-skills/inference/scripts/damo_yolo_safe_demo.py camera \
  -f /path/to/damoyolo_config.py \
  --engine /path/to/damoyolo_end2end_fp16_bs1.trt \
  --camid 0 --infer-size 640 640 --device cuda --conf 0.6 --end2end --output-dir demo

Option meanings to preserve

• --conf is the visualization/display threshold. NMS thresholds come from the config (model.head.nms_conf_thre, model.head.nms_iou_thre).
• --infer_size H W is the resize/pad target used by Torch and TensorRT engines. ONNX engines may derive the target from the ONNX input shape.
• --end2end is only for TensorRT engines exported with NMS included; using it on the wrong .trt layout changes the expected output tensors.
• Source-style --save_result saves visualizations by default. The bundled helper also supports --no-save-result and only opens GUI windows when --show-window is explicit.