Instructions to use nlpconnect/vit-gpt2-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpconnect/vit-gpt2-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="nlpconnect/vit-gpt2-image-captioning")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") model = AutoModelForMultimodalLM.from_pretrained("nlpconnect/vit-gpt2-image-captioning") - Notebooks
- Google Colab
- Kaggle
How to train and fine tune this model on a new dataset
#2
by zahram - opened
I am a keras developer, how I can I train this model on a new dataset, also can I fine tune this model. Thank You
you may refer to this blog https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/
replace pytorch trainer with keras training from https://github.com/huggingface/transformers/blob/main/examples/tensorflow/summarization/run_summarization.py
ankur310794 changed discussion status to closed