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milkyroad/file2
file2 is a machine learning model from milkyroad. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as cc-by-sa-4.0.
Archive of the lab workspace: curated cystoscopy datasets, run code (srcsnapshot), final checkpoints (results / bestweights / models / weights), docs, KB, and the cascade-w34 inference server.
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Updated Sep 24, 2026
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From the Hugging Face model README
Archive of the lab workspace: curated cystoscopy datasets, run code
(src_snapshot), final checkpoints (results / best_weights / models /
weights), docs, KB, and the cascade-w34 inference server.
data_cystoscopy-core_<X>.tar — dataset subdirs (yolo_roi, B, C, D, H, I,
seg_v1, v1/v2/v3_build, usable_v1). Extract under data/cystoscopy-core/<X>/.cystoscopy-core_small/ — remaining dataset files (parquet manifests,
checksums, README).runs/ — per-run src_snapshot/ (code), results/, best_weights/,
models/, weights/ (final checkpoints). YOLO keeps best.pt + last.pt
only.inference/cascade_w34_server/ — FastAPI server. Set LAB to the extract
root before running.writing/, kb/, lib/, prepare/, reports/ — docs, notes, helpers.Download this repo (huggingface-cli download milkyroad/file2 --local-dir lab_backup).
mkdir -p lab/data/cystoscopy-core and extract each
data_cystoscopy-core_<X>.tar into lab/data/cystoscopy-core/<X>/.
Copy cystoscopy-core_small/ files into lab/data/cystoscopy-core/.
Copy runs/, inference/, lib/, kb/, writing/, prepare/ to lab/.
Point the server at that root:
export LAB=/path/to/lab
# or edit inference/cascade_w34_server/cascade_w34_server.py
# LAB = pathlib.Path("<LAB_ROOT>") → pathlib.Path("/path/to/lab")
Python deps (CUDA recommended): torch, opencv-python, numpy,
fastapi, uvicorn, pillow, pydantic, transformers, timm,
ultralytics. See inference/cascade_w34_server/requirements.txt.
Accept the gated encoder
google/medsiglip-448
(Health AI Developer Foundations terms) and huggingface-cli login.
Depth also pulls depth-anything/Depth-Anything-V2-Small-hf on first use.
python inference/cascade_w34_server/cascade_w34_server.py (port 8001).
google/medsiglip-448 and other hub snapshots).cache/, __pycache__/, per-run venv/.venv.data/hf/ (re-downloadable Hub datasets).epoch*.pt; 3D reconstruction frame/video dumps (LAN only).LAN sibling copy (full progress minus the excludes above) lives on the other Spark and is not part of this repo.