Instructions to use sunbv56/vit-gpt2-imagecaptioningfood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sunbv56/vit-gpt2-imagecaptioningfood 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="sunbv56/vit-gpt2-imagecaptioningfood")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("sunbv56/vit-gpt2-imagecaptioningfood") model = AutoModelForMultimodalLM.from_pretrained("sunbv56/vit-gpt2-imagecaptioningfood") - Notebooks
- Google Colab
- Kaggle
About model
The model fined tuning with large data of API from bbcgoodfood.com
How to Get Started with the Model
from transformers import AutoTokenizer, VisionEncoderDecoderModel, ViTFeatureExtractor
tokenizer = AutoTokenizer.from_pretrained("gpt2") # for text
feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224") # for image
model = VisionEncoderDecoderModel.from_pretrained("sunbv56/vit-gpt2-imagecaptioningfood") # load model
Example code here
https://www.kaggle.com/code/thuntrngbnh/test-model-vit-gpt2-icf/notebook
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