Image Classification
Transformers
PyTorch
English
vision-encoder-decoder
image-text-to-text
image-captioning
Instructions to use deepklarity/poster2plot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepklarity/poster2plot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="deepklarity/poster2plot") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("deepklarity/poster2plot") model = AutoModelForImageTextToText.from_pretrained("deepklarity/poster2plot") - Notebooks
- Google Colab
- Kaggle
Deepak Singh Rawat commited on
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Parent(s): 47962d5
Add Huggingface Spaces link
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README.md
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An image captioning model to generate movie/t.v show plot from poster. It generates decent plots but is no way perfect. We are still working on improving the model.
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# Model Details
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The base model uses a Vision Transformer (ViT) model as an image encoder and GPT-2 as a decoder.
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An image captioning model to generate movie/t.v show plot from poster. It generates decent plots but is no way perfect. We are still working on improving the model.
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## Live demo on Hugging Face Spaces: https://huggingface.co/spaces/deepklarity/poster2plot
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# Model Details
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The base model uses a Vision Transformer (ViT) model as an image encoder and GPT-2 as a decoder.
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