Update README.md
Browse files# testing image
inputs = processor(images=img1, return_tensors="pt")
pixel_values = inputs.pixel_values
generated_ids = model.generate(pixel_values=pixel_values, max_length=50)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_caption)
README.md
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@@ -8,21 +8,19 @@ pipeline_tag: image-to-text
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from transformers import AutoProcessor, BlipForConditionalGeneration
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processor = AutoProcessor.from_pretrained("trunks/blip-image-captioning-base")
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model = BlipForConditionalGeneration.from_pretrained("trunks/blip-image-captioning-base")
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# prepare image for model
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from PIL import Image
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from IPython.display import display
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img1 = Image.open("imagepath/img.jpeg")
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width, height = img1.size
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img1_resized = img1.resize((int(0.3 * width), int(0.3 * height))
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display(img1_resized)
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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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from transformers import AutoProcessor, BlipForConditionalGeneration
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processor = AutoProcessor.from_pretrained("trunks/blip-image-captioning-base")
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model = BlipForConditionalGeneration.from_pretrained("trunks/blip-image-captioning-base")
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# prepare image for model
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from PIL import Image
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from IPython.display import display
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img1 = Image.open("imagepath/img.jpeg")
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width, height = img1.size
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img1_resized = img1.resize((int(0.3 * width), int(0.3 * height))
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display(img1_resized)
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