Instructions to use ProbeX/Model-J__ResNet__model_idx_0145 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_0145 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_0145") 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_0145") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0145", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b38f300472bcb2c6576a56b757f1f85b695b548d965e9ad3c49fea6af443edab
- Size of remote file:
- 171 MB
- SHA256:
- d2cba67e5a77d19ab96463637af66a000c27bf6296dc52b9bbe682106a5a025b
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