Mask Generation
Transformers
ONNX
sam3
sam-3
image-segmentation
text-promptable
open-vocabulary
concept-segmentation
Instructions to use danilobukvic/sam3-text-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danilobukvic/sam3-text-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="danilobukvic/sam3-text-onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("danilobukvic/sam3-text-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download text_encoder_int4.onnx from danilobukvic/sam3-text-onnx: direct link, hf CLI and curl.
- Browser
- Download file 2.82 MB
-
https://huggingface.co/danilobukvic/sam3-text-onnx/resolve/main/text_encoder_int4.onnx
- Command line
-
hf download hf://danilobukvic/sam3-text-onnx/text_encoder_int4.onnx
-
curl -L -o text_encoder_int4.onnx https://huggingface.co/danilobukvic/sam3-text-onnx/resolve/main/text_encoder_int4.onnx
2.82 MB
- Xet hash:
- fed07997cfd0f002079e223d874be706da97cd13c7fc27a0259bd8cee8173a59
- Size of remote file:
- 2.82 MB
- SHA256:
- 92f824a1841b787dc8dafa8cb8e8dce0c874f8d2d629f6b1c8de88399ede3806
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.