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: 1,259 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # Tracked in camstack/server as hf/camstack-trainer/; edit it there, not on the Hub.
[build-system]
requires = ["setuptools>=75", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "camstack-train"
version = "0.1.0"
description = "CamStack detector trainer: fine-tunes the YOLOv9 root on a hub's annotated export"
readme = "README.md"
license = "AGPL-3.0-only"
requires-python = ">=3.10"
# The light core: spec, dataset conversion, split, floors, manifest and the
# vendored evaluation harness. Enough to run the unit tests.
dependencies = [
"numpy==2.5.1",
"pillow==12.3.0",
"pydantic==2.13.4",
"onnxruntime==1.27.0",
]
[project.optional-dependencies]
# torch comes from the base image (pytorch/pytorch:2.13.0-cuda12.6-cudnn9-runtime);
# it is deliberately NOT pinned here so pip never replaces the CUDA build.
train = [
"ultralytics==8.4.98",
]
export = [
"onnx==1.22.0",
"onnxslim==0.1.94",
"openvino==2026.2.1",
"nncf==3.2.0",
"coremltools==9.0",
]
test = [
"pytest==9.1.1",
]
[project.scripts]
camstack-train = "camstack_train.cli:main"
[tool.setuptools.packages.find]
include = ["camstack_train*"]
namespaces = true
[tool.setuptools.package-data]
"*" = ["*.py"]
[tool.pytest.ini_options]
testpaths = ["tests"]
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