Instructions to use ProbeX/Model-J__ResNet__model_idx_0475 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_0475 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_0475") 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_0475") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0475", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2be1846fa3a9af66ff8b10bfe42ccb98270a6e0be68b86ad8088c0103c8a13ee
- Size of remote file:
- 171 MB
- SHA256:
- c3e941aee8b481bddf9cb335b93c23c81f90d6947e179ac4ef3df95a6c09784a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.