Instructions to use ProbeX/Model-J__ResNet__model_idx_0983 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_0983 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_0983") 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_0983") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0983", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9bf4fd9e98eb5a76d3ac2e47ca6bd67f7a19c9035719843c9a5c5d0e4e47252
- Size of remote file:
- 171 MB
- SHA256:
- 2f23e064f07312e065ef90bda7a56a91ccb4efa0c5160f83e02afa2c2624ae90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.