Instructions to use ProbeX/Model-J__ResNet__model_idx_0948 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0948 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0948") 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__ResNet__model_idx_0948") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0948", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e6ce6540e179a9b919db5b7cd799d7012e1dedce0fc666fea2f7e4667bbe623
- Size of remote file:
- 171 MB
- SHA256:
- 9385ace47da644b9ff9599e48a97ddecaa0ee237bd63e9a1c9113d73ad1f77c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.