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
File size: 374 Bytes
130d4de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"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
}
}
|