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 tokenizer.json from Oncosoft-Research/RepConTUR: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/main/tokenizer.json
- Command line
-
hf download hf://Oncosoft-Research/RepConTUR/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Oncosoft-Research/RepConTUR/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 151862ae9aacd028d62300da295bf37c7b9910ed1793864200ef3c925f9aa4a1
- Size of remote file:
- 11.4 MB
- SHA256:
- df0d7f9e6eb61370303d1ce48ae92c1b06eff296a11df215329624905dab141c
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