Instructions to use nnpy/blip-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nnpy/blip-image-captioning with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="nnpy/blip-image-captioning")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("nnpy/blip-image-captioning") model = AutoModelForMultimodalLM.from_pretrained("nnpy/blip-image-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from nnpy/blip-image-captioning: direct link, hf CLI and curl.
- Browser
- Download file 287 Bytes
-
https://huggingface.co/nnpy/blip-image-captioning/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://nnpy/blip-image-captioning/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/nnpy/blip-image-captioning/resolve/main/preprocessor_config.json
287 Bytes
| { | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "BlipImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "processor_class": "BlipProcessor", | |
| "size": 384 | |
| } | |