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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("deepklarity/poster2plot") model = AutoModelForMultimodalLM.from_pretrained("deepklarity/poster2plot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from deepklarity/poster2plot: direct link, hf CLI and curl.
- Browser
- Download file 982 MB
-
https://huggingface.co/deepklarity/poster2plot/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://deepklarity/poster2plot/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepklarity/poster2plot/resolve/main/pytorch_model.bin
982 MB
- Xet hash:
- 6490ebad7cabac5ffd4c828ae9dd0ea6d0839bc89887647a0cf6200b9c696ee3
- Size of remote file:
- 982 MB
- SHA256:
- 6a736baeaf505c1d2722a27c7e28c6c10b89fabcad48e7a14a8bd253d355ce94
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