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: 1,353 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 | """result.json validates against the published model; the schemas are current."""
from __future__ import annotations
import io
import json
import subprocess
import sys
from pathlib import Path
from camstack_train.events import Emitter
from camstack_train.manifest import failed_result
from camstack_train.result_model import TrainingResult
from camstack_train.run import Deps, run_job
from .fakes import FakeToolchain, detector_factory, fake_fetch, fake_trainer
from .test_run import setup
ROOT = Path(__file__).resolve().parents[1]
def test_succeeded_result_matches_the_model(tmp_path, make_export, job_dict):
job, paths = setup(tmp_path, make_export, job_dict)
deps = Deps(fetch=fake_fetch, trainer=fake_trainer, toolchain=FakeToolchain(fail=("coreml",)),
detector=detector_factory(), emitter=Emitter(io.StringIO()))
assert run_job(job, paths, deps) == 0
TrainingResult.model_validate(json.loads((paths.out / "result.json").read_text()))
def test_failed_result_matches_the_model():
TrainingResult.model_validate(failed_result("k", {"stage": "prepare", "code": "bad-spec", "message": "m"}))
def test_checked_in_schemas_are_current():
proc = subprocess.run([sys.executable, str(ROOT / "tools" / "gen_schemas.py"), "--check"], capture_output=True, text=True)
assert proc.returncode == 0, proc.stdout
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