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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rchan26/dit_base_binary_task: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/rchan26/dit_base_binary_task/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://rchan26/dit_base_binary_task@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rchan26/dit_base_binary_task/resolve/refs%2Fpr%2F1/pytorch_model.bin
343 MB
- Xet hash:
- f92ffa955e670dbe6a6706f2500fa74cc6a51c8ffce851c3d5a4ebc49fb58812
- Size of remote file:
- 343 MB
- SHA256:
- 1dc031c224a9c05b9023967ff3d68576087cf11d2cfe266f68c7fcc5d2b82b0c
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