Instructions to use Salesforce/blip-image-captioning-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/blip-image-captioning-base 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-base")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = AutoModelForImageTextToText.from_pretrained("Salesforce/blip-image-captioning-base") - Notebooks
- Google Colab
- Kaggle
Info about the pre-trained checkpoint
#15
by nicolafan - opened
Hi! In the model card, it is specified that this model is pre-trained on COCO, but I don't see this configuration in the official repo:
The pre-trained checkpoints are computed on a 14M images dataset (according to the paper made of images from COCO, VG, and Conceptual Captions), or on a 129M images dataset (previous dataset + LAION).
Is the HF checkpoint a new configuration computed only on COCO, or is it one of the fine-tuned checkpoints?
Thank you for the amazing work.
