Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/mxslr/mlcraft/domain-computer-visionnpx skills add mxslr/mlcraft --skill domain-computer-visiongit clone --depth 1 https://github.com/mxslr/mlcraftWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mxslr/mlcraft/domain-computer-vision)<a href="https://agentmods.dev/skills/mxslr/mlcraft/domain-computer-vision"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-computer-vision.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00096 | $0.00673 |
| Opus 5 | $0.00048 | $0.00336 |
| Sonnet 5 | $0.00019 | $0.00135 |
| Haiku 4.5 | $0.00010 | $0.00067 |
Grade A, and why
domain-computer-vision scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 31 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computer Vision - Method Selection
Pick by sub-task, then apply training-optimization + rigorous-evaluation. Prefer pretrained backbones; train from scratch only with very large data.
Decision table
| Sub-task | Recommended (strong default, then heavier option) | Notes |
|---|---|---|
| Image classification | EfficientNet / ConvNeXt, then ViT/Swin (needs more data) | ConvNeXt/EffNet are strong, data-efficient. Match input resolution to the backbone. |
| Object detection | YOLO (v8/11) / RT-DETR, then DINO-DETR or Faster R-CNN | YOLO for speed/deploy; DETR-family for accuracy. Use mAP@IoU. |
| Face detection | RetinaFace / YOLO-face / MTCNN | Detection ≠ recognition. |
| Face recognition/verification | ArcFace / CosFace embeddings + similarity | Metric-learning, not softmax classification. Evaluate with verification (ROC/EER), gallery split by identity. |
| Semantic segmentation | U-Net / DeepLabv3+ / SegFormer | Dice/IoU. Watch class imbalance. |
| Instance segmentation | Mask R-CNN / YOLO-seg | |
| Promptable / zero-shot seg | SAM / SAM2 | Great for annotation & few-shot. |
| Keypoints / pose | HRNet / YOLO-pose / ViTPose | |
| OCR | detector + recognizer (DBNet + CRNN) / TrOCR / PaddleOCR |
Cross-cutting CV practice
- Transfer learning from ImageNet; two-phase fine-tune; discriminative LR (see
training-optimization). - Augmentation matched to the task: flips only if label-invariant (a horizontally-flipped digit/letter is different!); mild rotation/scale/color; MixUp/CutMix help classification but can hurt localization and fine textures.
- Resolution is a top accuracy lever - small objects need higher resolution or tiling/patching; don't downsample away the signal.
- Detection data: verify box format (xywh vs xyxy, normalized?), anchor/scale sizing, and NMS thresholds.
- Leakage: split by source/scene/identity, not by frame - consecutive frames or same-identity images must not cross splits (
data-rigor-and-leakage). - Explainability: Grad-CAM for classifiers; overlay predicted boxes/masks for detection/segmentation.
- Improve accuracy: use
accuracy-improvement-loop. For tiny objects in big images, consider tiling or weakly-supervised localization.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 31 lines · 96 tokens per session scan A e0a44e4fe613
domain-computer-vision is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 1mo ago), licensed MIT. It adds 96 tokens to every session and 673 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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