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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = AutoModelForMultimodalLM.from_pretrained("Salesforce/blip-image-captioning-base") - Notebooks
- Google Colab
- Kaggle
Commit ·
588a7b6
1
Parent(s): 62ef7fd
Add TF weights
Browse filesModel converted by the [`transformers`' `pt_to_tf` CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). All converted model outputs and hidden layers were validated against its PyTorch counterpart.
Maximum crossload output difference=5.970e-03; Maximum crossload hidden layer difference=1.053e-01;
Maximum conversion output difference=5.970e-03; Maximum conversion hidden layer difference=1.053e-01;
CAUTION: The maximum admissible error was manually increased to 0.2!
- tf_model.h5 +3 -0
tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:d0aaa4c0e003f599d8baa53a9dee85af14eef20554cf2f8113a2673e25a59f8c
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size 990275136
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