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
- Xet hash:
- 75b798c94a6add909123fa9b5a86b903a0f0169c402fdb3acca491dd9a7bf1b8
- Size of remote file:
- 3.44 kB
- SHA256:
- c86ed6f9b6e9724e93e5729c2bf3e675321677102e81a52c133edd5e93baf75c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.