---

name: intern-image description: "Guides InternImage vision foundation-model workflows across image classification, object detection, semantic segmentation, autonomous driving, DCNv3 operators, and TensorRT deployment." disable-model-invocation: true metadata: disco-role: operating license: MIT

InternImage Repo Skill

Use this skill when a task mentions InternImage, OpenGVLab InternImage, DCNv3-based vision foundation models, InternImage classification/detection/segmentation configs, InternImage autonomous-driving baselines, or InternImage TensorRT/mmdeploy export.

InternImage is a large-scale vision backbone family built around DCNv3 deformable convolution. The repository is not a single installable Python package: it is a multi-workflow research codebase with separate classification, MMDetection, MMSegmentation, autonomous-driving, and deployment/operator surfaces. Route aggressively before giving commands.

First steps

1. Identify the user's workflow family, dataset, model size, config/checkpoint, execution backend, and whether they want a command plan, debugging help, or an actual run.
2. Read references/model-and-config-overview.md when the model family, config name, dataset, or checkpoint pairing is unclear.
3. Use scripts/check_internimage_environment.py for a safe dependency/backend diagnostic before any GPU-heavy run.
4. Route to the nearest sub-skill and use that sub-skill's bundled command builder instead of copying long source launch snippets by hand.
5. For CUDA, DCNv3, mmdeploy, or TensorRT blockers, route to sub-skills/deployment/ even when the original task is classification, detection, or segmentation.

Route map

User task signal	Read this next	Use when
ImageNet, iNaturalist, classification config YAML, main.py, DeepSpeed, Accelerate, feature extraction, Hugging Face OpenGVLab/internimage_*	sub-skills/classification/SKILL.md	The task is image classification, backbone hidden states, classification training/evaluation, or Transformers usage.
COCO, LVIS, OpenImages, VOC, CrowdHuman, MMDetection, Mask R-CNN, Cascade, DINO, bbox/mask mAP, detection image demo, SAM-prompted masks	sub-skills/detection/SKILL.md	The task is object detection or instance segmentation with the MMDetection 2.x InternImage stack.
ADE20K, Cityscapes, COCO-Stuff, Mapillary, NYU Depth V2, Pascal Context, MMSegmentation, UperNet, SegFormer, Mask2Former, mIoU, segmentation image demo	sub-skills/segmentation/SKILL.md	The task is semantic segmentation with the MMSegmentation 0.x InternImage stack.
Occupancy prediction, BEVFormerOcc, Online HD Map Construction, VectorMapNet, OpenLane-V2, topology metrics, lane centerlines, traffic elements, OpenLane submission validation	sub-skills/autonomous-driving/SKILL.md	The task is an InternImage autonomous-driving challenge baseline or OpenLane-V2 devkit/schema/metric workflow.
DCNv3, Cuda is not availabel, nvcc, CUDA toolkit, custom operators, ONNX, TensorRT, mmdeploy, export, deployment	sub-skills/deployment/SKILL.md	The task is backend readiness, operator build, ONNX/TensorRT export, or cross-workflow deployment.

Install and backend posture

• The repository's documented runtime stacks are older OpenMMLab-era stacks. Avoid silently upgrading to MMDetection 3.x, MMSegmentation 1.x/2.x, or NumPy 2.x unless the user is intentionally porting the code.
• Common pins from repo evidence include PyTorch 1.10-1.12 with CUDA, timm==0.6.11, mmcv-full==1.5.x, mmdet==2.28.1, mmsegmentation==0.27.0 or 0.29.1 for autonomous OpenLane, numpy<2, pydantic==1.10.13, and yapf==0.40.1 for detection config formatting.
• Full model execution normally requires CUDA and the DCNv3 operator. A CPU-only command-builder check is not proof of GPU/model runtime.
• Source DCNv3 builds require a PyTorch CUDA wheel, visible CUDA devices, CUDA_HOME, and nvcc/toolkit compatibility. A GPU driver alone is not enough.
• TensorRT export additionally requires mmdeploy, TensorRT, CUDNN, and the DCNv3 TensorRT custom backend operator.

Run a safe environment summary from the root skill directory:

python scripts/check_internimage_environment.py --profile detection --profile deployment

For machine-readable output:

python scripts/check_internimage_environment.py --profile autonomous --json

Runtime guardrails

• Do not claim that this generated skill verified full training/evaluation/export. The verified scope covered static self-containment, helper parser/dry-run checks, and a CPU OpenLane-V2 schema-validation environment; model-scale GPU work remains a user-approved runtime action.
• Do not tell users to reopen this skill's source evidence. If a command is needed, use the bundled helper in the relevant sub-skill and fill in the user's checkout, data root, checkpoints, and output paths.
• Keep checkpoint/config/data families aligned. A classification checkpoint is not a full detector/segmentor checkpoint unless the selected config explicitly uses it as a pretrained backbone.
• Treat network downloads, challenge datasets, pretrained weights, cluster launches, TensorRT builds, and long training as explicit user-controlled actions.

Cross-cutting references

• references/model-and-config-overview.md - model families, task/config families, checkpoint pairing, and routing hints.
• references/troubleshooting.md - install/import/backend/config/data/checkpoint failures that span multiple workflows.
• references/repo-provenance.md - source repository snapshot and refresh baseline.
• references/repo-routing-metadata.json - structured router metadata for managed repo-skill import.