pip install -r requirements.txt can pull CPU-only torchvision via
transitive dependencies (timm, ultralytics). Adding --extra-index-url
with the CUDA wheel index ensures all torch packages stay on the
correct build. Applied to both Linux and Windows setup scripts.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
timm and ultralytics depend on torchvision. When pip install -r
requirements.txt resolves them, it pulls torchvision from PyPI (CPU
build) which is incompatible with CUDA torch, causing
"operator torchvision::nms does not exist" at import time.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- setup-windows.ps1 and setup_env.sh detect nvidia-smi for CUDA vs CPU PyTorch
- Startup logs Python version, venv path, PyTorch/CUDA/GPU, scikit-learn, librosa
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove legacy distance-mode scanning (build_profile, _similarity, etc.)
and hand-crafted intensity features — pipeline is now embedding-only
- Integrate Microsoft BEATs as embedding option alongside wav2vec2/HuBERT
- Add TrainDialog with positive class selector, model picker, video dir
fallback, and live training stats
- Add TrainWorker QThread with cancel support and proper lifecycle cleanup
- Add source_path column to DB for robust source video tracking
- Add get_export_folders/get_training_data/get_training_stats to DB
- Wire source_path in all export DB writes (_on_clip_done, _on_auto_clip_done)
- Cancel scan/train workers in closeEvent to prevent use-after-free crashes
- Add setup_env.sh supporting both conda and python venv (CUDA 12.8)
- Update requirements.txt with all actual dependencies
- Update 8cut_train.py with --positive flag for new DB-driven training
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Ethanfel