Instructions to use ProbeX/Model-J__ResNet__model_idx_0629 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_0629 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_0629") 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_0629") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0629", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b7b746a5cdb3d07233d83fee072d790763a191b16ff48949365085f6ef93452
- Size of remote file:
- 171 MB
- SHA256:
- f1e5272bb39b21a8eae3584b040dc2348a2862c88762839bd1ef2f3698716061
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.