Instructions to use ProbeX/Model-J__ResNet__model_idx_0359 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_0359 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_0359") 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_0359") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0359") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c5eb476e4ef3ab84d1201d13799958ba66b506f97c40c6afbf49e774dfd4cf5
- Size of remote file:
- 171 MB
- SHA256:
- 958c5e55bd81fd0d509adedbf594fa52157873b5c3bd8151f861e8af71236ddb
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