Instructions to use Salesforce/blip-image-captioning-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/blip-image-captioning-large 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="Salesforce/blip-image-captioning-large")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = AutoModelForImageTextToText.from_pretrained("Salesforce/blip-image-captioning-large") - Notebooks
- Google Colab
- Kaggle
Can existing large datasets be used to fine tune the blip'large_caption task?
#29
by shams123321 - opened
Can existing large datasets be used to fine tune the blip'large_caption task?
The dataset used is UFine6926 dataset, as its text description of images is very fine-grained, with an average of 80.8 words per image. Can this dataset be used to fine tune the caption task of blip?
Looking forward to your reply!
Thank you!