# Reproduce the 100-image experiment

```bash
uv run ai-vision collect --count 100 --seed 20261010
uv run ai-vision benchmark --degrade --output outputs/pretrained
uv run ai-vision validate --output runs/pretrained
uv run ai-vision train --epochs 3 --output runs/finetune
uv run ai-vision --model runs/finetune/run/weights/best.pt validate --output runs/finetuned-validation
uv run ai-vision --ocr --pose --face --depth --segmentation benchmark --output outputs/full-perception
uv run ai-vision film --source outputs/full-perception --seconds 50 --output outputs/detections.mp4
```

Collection downloads COCO's annotation archive (about 242 MiB), then 100 individual images rather than the entire image dataset. It randomly samples images with vehicle, bicycle, traffic-light, or stop-sign annotations and CC BY, CC BY-SA, or government-work metadata. This filter creates diverse street-context examples but also some indoor/transport contexts; it does not guarantee all images are streets.

The originals remain unchanged. Each manifest entry records original Flickr URL, download URL, source license, resolution, timestamp, and SHA256. Respect attribution and share-alike terms for image derivatives. `ATTRIBUTION.md` provides per-image source links and credit identifiers from COCO metadata.

Real COCO annotations are converted to YOLO labels, excluding crowd regions. The seeded order assigns 80 training and 20 validation images. The short run freezes the first 10 layers, uses AdamW, and retains the best validation checkpoint. This is fine-tuning a pretrained model, not training from scratch. **The pretrained baseline performed better, so it remains the default and powers the film.** The fine-tuned checkpoint is included as an experiment in the release.

COCO validation data contributed to development of the upstream pretrained model. The holdout is separate from this fine-tuning run, but it is not an independent generalization benchmark. Only detection has labeled ground truth; OCR, pose, face, depth, and segmentation outputs are qualitative. Confidence is not accuracy.