Instructions to use ProbeX/Model-J__MAE__model_idx_0212 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_0212 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_0212") 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_0212") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__MAE__model_idx_0212", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3a53423416a65bef6db410cf7255add402609262b71b2fab43743622949fe01
- Size of remote file:
- 5.37 kB
- SHA256:
- 76a73135857619a534bde04e008ae2b9e29f7f8e3cc2a368970621f3bce1bca2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.