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--- |
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language: |
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- en |
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tags: |
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- image-to-text |
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--- |
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## lokibots/vit-patch16-1280-gpt2-large-image-summary |
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This model generates a summary from a given chart image. The model accepts an image of size 1280x768 (or less) and generates a summary describing the contents of the image. **However, training is still required.** |
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## sample inference code |
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```{python} |
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from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, GPT2Tokenizer |
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from PIL import Image |
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model = VisionEncoderDecoderModel.from_pretrained("lokibots/vit-patch16-1280-gpt2-large-image-summary") |
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feature_extractor = ViTFeatureExtractor.from_pretrained("lokibots/vit-patch16-1280-gpt2-large-image-summary") |
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2-large') |
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image = Image.open("image_file").convert("RGB") |
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pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values |
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gen_kwargs = {"max_length": 1024, "num_beams": 4} |
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output_ids = model.generate(pixel_values, **gen_kwargs) |
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preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
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``` |
