Image Segmentation
Transformers
PyTorch
repcontur
feature-extraction
medical
radiology
ct
3d
tumor-segmentation
referring-segmentation
report-generation
visual-question-answering
custom_code
Instructions to use Oncosoft-Research/RepConTUR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Oncosoft-Research/RepConTUR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Oncosoft-Research/RepConTUR", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Oncosoft-Research/RepConTUR", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download _compat.py from Oncosoft-Research/RepConTUR: direct link, hf CLI and curl.
- Browser
- Download file 299 Bytes
-
https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/refs%2Fpr%2F1/_compat.py
- Command line
-
hf download hf://Oncosoft-Research/RepConTUR@refs/pr/1/_compat.py
-
curl -L -o _compat.py https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/refs%2Fpr%2F1/_compat.py
299 Bytes
| """Minimal shims so vendored modules need no mmengine/xtuner at inference.""" | |
| import logging | |
| _logger = logging.getLogger("repcontur") | |
| def print_log(message, logger=None, level=logging.INFO): | |
| """Drop-in for mmengine.print_log: logs to the repcontur logger.""" | |
| _logger.log(level, message) | |