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 pytorch_model.bin from Oncosoft-Research/RepConTUR: direct link, hf CLI and curl.
- Browser
- Download file 3.12 GB
-
https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Oncosoft-Research/RepConTUR@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/refs%2Fpr%2F1/pytorch_model.bin
3.12 GB
- Xet hash:
- 3938e247601490db51e72d71cd7a601cf80422176421408a565763dbacdd2deb
- Size of remote file:
- 3.12 GB
- SHA256:
- 7f171cdcee2af5805677f32acc4950afb527cb6acff95b764bc9838cf15e0d7e
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