Instructions to use OpenGVLab/InternViT-300M-448px with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternViT-300M-448px with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="OpenGVLab/InternViT-300M-448px", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenGVLab/InternViT-300M-448px", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from OpenGVLab/InternViT-300M-448px: direct link, hf CLI and curl.
- Browser
- Download file 287 Bytes
-
https://huggingface.co/OpenGVLab/InternViT-300M-448px/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://OpenGVLab/InternViT-300M-448px/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/OpenGVLab/InternViT-300M-448px/resolve/main/preprocessor_config.json
287 Bytes
| { | |
| "crop_size": 448, | |
| "do_center_crop": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "resample": 3, | |
| "size": 448 | |
| } | |