Instructions to use trunks/blip-image-captioning-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trunks/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="trunks/blip-image-captioning-base")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("trunks/blip-image-captioning-base") model = AutoModelForImageTextToText.from_pretrained("trunks/blip-image-captioning-base") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -24,3 +24,13 @@ img1_resized = img1.resize((int(0.3 * width), int(0.3 * height))
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display(img1_resized)
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display(img1_resized)
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# testing image
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inputs = processor(images=img1, return_tensors="pt")
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pixel_values = inputs.pixel_values
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generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
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generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(generated_caption)
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