Instructions to use ProbeX/Model-J__MAE__model_idx_0521 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__MAE__model_idx_0521 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__MAE__model_idx_0521") 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("ProbeX/Model-J__MAE__model_idx_0521") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0521") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ed91da82d4d280f3f809f9a7b31663ca3610dc1070e715bc7d9fcb08b85e062
- Size of remote file:
- 5.37 kB
- SHA256:
- c82d8ad349effa9cf0de47ef6286086f35743552937c4a0e80aba64a7a0725c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.