Instructions to use Xenova/vit-gpt2-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Xenova/vit-gpt2-image-captioning with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-to-text', 'Xenova/vit-gpt2-image-captioning');
Download preprocessor_config.json from Xenova/vit-gpt2-image-captioning: direct link, hf CLI and curl.
- Browser
- Download file 378 Bytes
-
https://huggingface.co/Xenova/vit-gpt2-image-captioning/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Xenova/vit-gpt2-image-captioning/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Xenova/vit-gpt2-image-captioning/resolve/main/preprocessor_config.json
378 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ViTFeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ViTFeatureExtractor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } | |