Instructions to use Prem11100/Clustering-embeddings with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prem11100/Clustering-embeddings with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="Prem11100/Clustering-embeddings")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("Prem11100/Clustering-embeddings") model = AutoModel.from_pretrained("Prem11100/Clustering-embeddings", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from Prem11100/Clustering-embeddings: direct link, hf CLI and curl.
- Browser
- Download file 327 Bytes
-
https://huggingface.co/Prem11100/Clustering-embeddings/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Prem11100/Clustering-embeddings/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Prem11100/Clustering-embeddings/resolve/main/preprocessor_config.json
327 Bytes
| { | |
| "do_normalize": false, | |
| "do_rescale": false, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ViTImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } | |