CNN v2: Patch-based training as default (like CNN v1)

Salient point detection on original images with patch extraction.

Changes:
- Added PatchDataset class (harris/fast/shi-tomasi/gradient detectors)
- Detects salient points on ORIGINAL images (no resize)
- Extracts 32×32 patches around salient points
- Default: 64 patches/image, harris detector
- Batch size: 16 (512 patches per batch)

Training modes:
1. Patch-based (default): --patch-size 32 --patches-per-image 64 --detector harris
2. Full-image (option): --full-image --image-size 256

Benefits:
- Focuses training on interesting regions
- Handles variable image sizes naturally
- Matches CNN v1 workflow
- Better convergence with limited data (8 images → 512 patches)

Script updated:
- train_cnn_v2_full.sh: Patch-based by default
- Configuration exposed for easy switching

Example:
  ./scripts/train_cnn_v2_full.sh  # Patch-based
  # Edit script: uncomment FULL_IMAGE for resize mode

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

0 files changed, 0 insertions, 0 deletions