Image Segmentation
Transformers
TensorBoard
English
Instance_Segmentation
CPU_friendly
Transformers
rfdetr
supervision
roboflow
Eval Results (legacy)
Instructions to use Subh775/Seg-Basil-rfdetr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Subh775/Seg-Basil-rfdetr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Subh775/Seg-Basil-rfdetr")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Subh775/Seg-Basil-rfdetr", dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 92f67f884f554647f0846738f6a2b1af90726d1c655a2d002cc506b78d799bdd
- Size of remote file:
- 158 kB
- SHA256:
- 413c48ab1f7fe894c67fea74dd4fa30b32051f58858b96239ee5a61a5f50f31b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.