Instructions to use nlpconnect/vit-gpt2-image-captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpconnect/vit-gpt2-image-captioning 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="nlpconnect/vit-gpt2-image-captioning")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") model = AutoModelForImageTextToText.from_pretrained("nlpconnect/vit-gpt2-image-captioning") - Notebooks
- Google Colab
- Kaggle
Update README.md
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by vishank97 - opened
README.md
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@@ -54,7 +54,7 @@ def predict_step(image_paths):
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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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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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