Instructions to use AIXI-AIGC/VIT_448 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIXI-AIGC/VIT_448 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="AIXI-AIGC/VIT_448") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("AIXI-AIGC/VIT_448") model = AutoModelForZeroShotImageClassification.from_pretrained("AIXI-AIGC/VIT_448", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from AIXI-AIGC/VIT_448: direct link, hf CLI and curl.
- Browser
- Download file 315 Bytes
-
https://huggingface.co/AIXI-AIGC/VIT_448/resolve/refs%2Fpr%2F1/preprocessor_config.json
- Command line
-
hf download hf://AIXI-AIGC/VIT_448@refs/pr/1/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/AIXI-AIGC/VIT_448/resolve/refs%2Fpr%2F1/preprocessor_config.json
315 Bytes
| { | |
| "crop_size": 448, | |
| "do_center_crop": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "size": 448 | |
| } |