Image-to-Text
Transformers
Safetensors
English
vision-encoder-decoder
image-text-to-text
vit
bert
vision
caption
captioning
image
Instructions to use cnmoro/tiny-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cnmoro/tiny-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="cnmoro/tiny-image-captioning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("cnmoro/tiny-image-captioning") model = AutoModelForMultimodalLM.from_pretrained("cnmoro/tiny-image-captioning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from cnmoro/tiny-image-captioning: direct link, hf CLI and curl.
- Browser
- Download file 325 Bytes
-
https://huggingface.co/cnmoro/tiny-image-captioning/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://cnmoro/tiny-image-captioning/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/cnmoro/tiny-image-captioning/resolve/main/preprocessor_config.json
325 Bytes
| { | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_processor_type": "ViTImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 224, | |
| "width": 224 | |
| } | |
| } | |