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--- |
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library_name: transformers |
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license: mit |
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pipeline_tag: image-to-text |
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--- |
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# Blip Image Captioning Base BF16 |
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This model is a quantized version of the [Salesforce/blip-image-captioning-base](https://huggingface.co/Salesforce/blip-image-captioning-base), an image-to-text model. |
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From a memory footprint of 989 MBs -> 494 MBs by quantizing the percision of float32 to bfloat 16, reducing the model's memory size by 50 percent. |
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## Example |
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| <img src="https://huggingface.co/gospacedev/blip-image-captioning-base-bf16/resolve/main/cat%20in%20currents.png" width="316" height="316"> | |
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| a cat sitting on top of a purple and red striped carpet | |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import BlipForConditionalGeneration, BlipProcessor |
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import requests |
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from PIL import Image |
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model = BlipForConditionalGeneration.from_pretrained("gospacedev/blip-image-captioning-base-bf16") |
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processor = BlipProcessor.from_pretrained("gospacedev/blip-image-captioning-base-bf16") |
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# Load sample image |
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image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') |
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# Generate output |
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inputs = processor(image, return_tensors="pt") |
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output = model.generate(**inputs) |
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result = processor.decode(out[0], skip_special_tokens=True) |
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print(results) |
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``` |
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## Model Details |
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- **Developed by:** Grantley Cullar |
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- **Model type:** Image-to-Text |
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- **Language(s) (NLP):** English |
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- **License:** MIT License |
