Instructions to use ProbeX/Model-J__ResNet__model_idx_0428 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_0428 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_0428") 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_0428") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0428", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25f0ba0c447f6be47880f17630ae112fa03cb2a7dbfe99e6138c918638003465
- Size of remote file:
- 5.37 kB
- SHA256:
- bbeab6a665b71d2c1b149b9d4542b600b9f5fd80718d917d2a7be1bd3b4a6be0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.