Instructions to use camstack/camstack-trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use camstack/camstack-trainer with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("camstack/camstack-trainer", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 1,272 Bytes
ebeed67 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | """Byte-identical copies of CamStack code the trainer reuses, never edited here.
- ``camstack_eval/`` is ``scripts/eval/camstack_eval/`` (the P5 evaluation
harness): the letterbox + decode + metrics + D739 floor rule the hub's own
measurements use.
- ``pipeline_python/`` is ``packages/addon-pipeline/python/`` (the inference
pool and its postprocessors, tests excluded): the harness imports the pool's
preprocess and decode from it, so the trainer evaluates exactly as a node runs.
``scripts/check-trainer-vendor.ts`` in camstack/server fails when either copy
drifts from its source. Change the source, then re-copy.
"""
from __future__ import annotations
import sys
from pathlib import Path
#: The folder holding ``inference_pool.py`` and ``postprocessors/``.
PIPELINE_PYTHON = Path(__file__).resolve().parent / "pipeline_python"
def ensure_pipeline_on_path() -> None:
"""Put the vendored pool first on ``sys.path``.
The harness's ``pipeline._pipeline()`` imports ``inference_pool`` and
``postprocessors`` as top-level modules from the repository path; with this
folder already on the path those imports resolve to the vendored copy.
"""
entry = str(PIPELINE_PYTHON)
if entry not in sys.path:
sys.path.insert(0, entry)
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