CI: data flywheel (fork verification)#1
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Add opt-in active-learning collector to capture uncertain/failed samples: - New collection/ module with DataCollector (writes tiles/failures to collect_dir) - SolverConfig.collect_data flag (default False, zero I/O overhead) - Integration in RecaptchaSolver and AsyncRecaptchaSolver solve() pipelines - Collector records uncertain tiles (confidence between thresholds) and failures - tests/ scaffold: test_collector_scaffold.py, test_data_collector.py
- CI build job asserts the wheel ships collection/ but not training/ tooling - Document the 4-phase data flywheel implementation + the cv2.error/OSError lesson
…ll 4x4 classification fallback - Add YOLODetector.is_supported() to classify challenges into supported (COCO) vs. unsupported (fallback-capable) groups; skip unsupported challenges with minimal latency using short _reload_challenge delay - Refactor solve loop (sync+async symmetric) with separate attempts/skips budgets and solved flag; on classification failure, drop to short token-wait instead of hanging full timeout - Remove _get_target_class; move logic inline to clarify control flow - Implement per-cell 4x4 classification fallback in SquareCaptchaHandler: when COCO model lacks target (e.g., stairs, bridges), classify each cell independently using the 57k classification model as a last resort - Add test_is_supported.py (device/model coverage); test_square_handler_fallback.py (cell classification, missing-class recovery) - Add integration test retry-until-solvable (bounded N=3, wall-clock 180s, still asserts token); now PASSES in 279s (vs. previous fail after 578s) - Update codebase-summary.md with solve robustness + square handler fallback notes - Fix test_class_mapping.py SIM300 ruff violation - Add executed plan + brainstorm report to plans/ Verified: 117 unit tests pass; integration test now stable. Sync+async symmetric; public API unchanged.
Implement full-image collection, cell-bbox annotation, dataset builder, and 3-tier 4x4 detection priority (COCO → custom detection → per-cell fallback). Phase 3 (GPU training) deferred. Custom model gated behind optional config; default off preserves existing behavior. - DataCollector.record_challenge_image: save 4x4 full images to collected/full/ - SquareCaptchaHandler: hook full-image collection (detector.collector injection) - YOLODetector: load/verify custom detection model (SHA256), detection+mapping - SolverConfig: custom_detection_model_path parameter - annotate_detection_cli.py: interactive cell→bbox annotation CLI - prepare_detection_dataset.py: YOLO detection dataset builder (images/labels/data.yaml) - CUSTOM_DETECTION_CLASSES/MAPPINGS: runtime detection class sync - DETECTION_CLASSES: training/class_mapping dataset builder class registry - 3-tier priority solve handler with per-cell fallback - 13 new tests; ruff/mypy clean; 138 tests passing - docs/training-and-flywheel.md: Tier B documentation Public API unchanged. No confidential data or credentials.
- training/device_utils.resolve_device: CUDA > MPS > CPU auto-detect - train.py: --device defaults to auto, add --amp/--no-amp (use --no-amp on flaky MPS) - Apple Silicon (M-series) trains via Metal/MPS; no cloud GPU required for small datasets - docs + Tier B plan notes: Mac MPS option vs Colab; train_detection.py to reuse device_utils
- train_detection.py: YOLO detect trainer (auto CUDA/MPS/CPU, --amp/--no-amp, --resume) - write_model_card.py: model_card.json sidecar (date/task/classes/sha256) - collect.py: loop driver to accumulate per-cell + full-4x4 data with progress counts - class_mapping: enforce DETECTION_CLASSES == types.CUSTOM_DETECTION_CLASSES contract - pyproject: add onnx/onnxslim to dev extras (export-time only; runtime keeps onnxruntime) - smoke-verified train_detection on MPS (synthetic data -> best.pt, task=detect) Real model still needs human-annotated 4x4 data (no auto pseudo-label).
- auto_annotate_capmonster.py: label collected 4x4 images via CapMonster image mode (ComplexImageTask/recaptcha, ~$0.04/1000) -> annotations.jsonl, 0->1-indexed cells - only the 7 detection classes labeled; resilient per-image (network errors skipped) - API key via --api-key or env CAPMONSTER_API_KEY (no secret in repo) - docs: manual vs auto annotation; cap.guru can't do image mode (token only)
solution.answer is a 16-element bool mask (True=cell has target), not a cell-index list. Previous code coerced bools to ints producing wrong cells ([1,2] for all). Now map True flags -> 0-indexed cells. Verified end-to-end: collected images -> auto-labels -> YOLO detection dataset. Caught by spot-checking identical outputs.
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Intra-fork PR to run CI (lint / test 3.10-3.12 / typecheck / build + wheel-exclusion assertion) on smowtion's fork. Mirror of upstream PR DannyLuna17#8.