Instructions to use rchan26/dit_base_binary_task with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rchan26/dit_base_binary_task with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rchan26/dit_base_binary_task") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("rchan26/dit_base_binary_task") model = AutoModelForImageClassification.from_pretrained("rchan26/dit_base_binary_task") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:aa064d65e7ce272817f457e5ea98012012077fdbd1c8813eb4a082541b287610
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size 343265176
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