Image Feature Extraction
Transformers
Safetensors
English
intern_vit_6b
feature-extraction
vision
image-classification
vit
ViTP
InternVL
domain-adaptation
general
custom_code
Instructions to use BiliSakura/ViTP-InternVL-1B-General with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BiliSakura/ViTP-InternVL-1B-General with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="BiliSakura/ViTP-InternVL-1B-General", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BiliSakura/ViTP-InternVL-1B-General", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from BiliSakura/ViTP-InternVL-1B-General: direct link, hf CLI and curl.
- Browser
- Download file 266 Bytes
-
https://huggingface.co/BiliSakura/ViTP-InternVL-1B-General/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://BiliSakura/ViTP-InternVL-1B-General/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/BiliSakura/ViTP-InternVL-1B-General/resolve/main/preprocessor_config.json
266 Bytes
| { | |
| "do_resize": true, | |
| "size": 448, | |
| "do_center_crop": true, | |
| "crop_size": 448, | |
| "do_normalize": true, | |
| "image_mean": [0.485, 0.456, 0.406], | |
| "image_std": [0.229, 0.224, 0.225], | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "resample": 3 | |
| } | |