Image-to-Text
Transformers
ONNX
Transformers.js
PyTorch
English
vision-encoder-decoder
image-text-to-text
image-captioning
vision-language
onnxruntime
vit
gpt2
Instructions to use anmol-unitmole/image-caption-generation-vision-encoder-decoder-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anmol-unitmole/image-caption-generation-vision-encoder-decoder-model 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="anmol-unitmole/image-caption-generation-vision-encoder-decoder-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("anmol-unitmole/image-caption-generation-vision-encoder-decoder-model") model = AutoModelForMultimodalLM.from_pretrained("anmol-unitmole/image-caption-generation-vision-encoder-decoder-model", device_map="auto") - Transformers.js
How to use anmol-unitmole/image-caption-generation-vision-encoder-decoder-model with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-to-text', 'anmol-unitmole/image-caption-generation-vision-encoder-decoder-model'); - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_convert_rgb": null, | |
| "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 | |
| } | |
| } | |