Image Segmentation
Transformers
Safetensors
fc_clip
panoptic-segmentation
open-vocabulary
zero-shot
clip
convnext
mask2former
fiftyone
custom_code
Instructions to use Voxel51/fc-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Voxel51/fc-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Voxel51/fc-clip", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Voxel51/fc-clip", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Voxel51/fc-clip: direct link, hf CLI and curl.
- Browser
- Download file 309 Bytes
-
https://huggingface.co/Voxel51/fc-clip/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Voxel51/fc-clip/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Voxel51/fc-clip/resolve/main/preprocessor_config.json
309 Bytes
| { | |
| "image_processor_type": "BaseImageProcessor", | |
| "do_normalize": true, | |
| "image_mean": [0.48145466, 0.4578275, 0.40821073], | |
| "image_std": [0.26862954, 0.26130258, 0.27577711], | |
| "do_rescale": true, | |
| "rescale_factor": 0.00392156862745098, | |
| "do_resize": false, | |
| "size": {"height": null, "width": null} | |
| } | |