Instructions to use ProbeX/Model-J__ResNet__model_idx_0608 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_0608 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_0608") 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_0608") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0608", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e387611fba10a136ab9fec3378fc6c4ea98b28394da54ccfd3ca8cbb21ae105
- Size of remote file:
- 171 MB
- SHA256:
- 9257496e3200c4c813e187cf93a38996e98cbad81bea9adfc85800f44c0c9630
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.