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--- |
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tags: |
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- image-to-text |
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- image-captioning |
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license: apache-2.0 |
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widget: |
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- src: >- |
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https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg |
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example_title: Savanna |
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- src: >- |
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https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg |
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example_title: Football Match |
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- src: >- |
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https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg |
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example_title: Airport |
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duplicated_from: nlpconnect/vit-gpt2-image-captioning |
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--- |
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# nlpconnect/vit-gpt2-image-captioning |
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This is an image captioning model trained by @ydshieh in [flax ](https://github.com/huggingface/transformers/tree/main/examples/flax/image-captioning) this is pytorch version of [this](https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts). |
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# The Illustrated Image Captioning using transformers |
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* https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/ |
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# Sample running code |
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```python |
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from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer |
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import torch |
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from PIL import Image |
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model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") |
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feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") |
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tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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max_length = 16 |
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num_beams = 4 |
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gen_kwargs = {"max_length": max_length, "num_beams": num_beams} |
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def predict_step(image_paths): |
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images = [] |
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for image_path in image_paths: |
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i_image = Image.open(image_path) |
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if i_image.mode != "RGB": |
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i_image = i_image.convert(mode="RGB") |
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images.append(i_image) |
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pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values |
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pixel_values = pixel_values.to(device) |
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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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preds = [pred.strip() for pred in preds] |
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return preds |
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predict_step(['doctor.e16ba4e4.jpg']) # ['a woman in a hospital bed with a woman in a hospital bed'] |
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``` |
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# Sample running code using transformers pipeline |
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```python |
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from transformers import pipeline |
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image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning") |
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image_to_text("https://ankur3107.github.io/assets/images/image-captioning-example.png") |
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# [{'generated_text': 'a soccer game with a player jumping to catch the ball '}] |
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``` |
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# Contact for any help |
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* https://huggingface.co/ankur310794 |
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* https://twitter.com/ankur310794 |
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* http://github.com/ankur3107 |
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* https://www.linkedin.com/in/ankur310794 |
