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
- Xet hash:
- 2224d1a0fc9f7375ff48663f0e3b7fe6a332ca55c95999f0ef521b1d889f7e51
- Size of remote file:
- 614 MB
- SHA256:
- 36eb63aea2a27dadf5c1854bedaa28d0079ee90fe3906910d915d2a42f2aa9b7
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