Instructions to use Salesforce/blip-itm-base-coco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Salesforce/blip-itm-base-coco with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, BlipForImageTextRetrieval processor = AutoProcessor.from_pretrained("Salesforce/blip-itm-base-coco") model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add TF weights
Model 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=1.669e-06; Maximum crossload hidden layer difference=1.544e-01; Maximum conversion output difference=1.669e-06; Maximum conversion hidden layer difference=1.544e-01; CAUTION: The maximum admissible error was manually increased to 0.2!154d7e6
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Update README.md
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Create README.md
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Upload BlipForImageTextRetrieval
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Upload processor
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initial commit
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