Instructions to use ProbeX/Model-J__ResNet__model_idx_0675 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_0675 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_0675") 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_0675") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0675", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50e797f2e7fc0e669b14905dd0a3452daff94326154bc61e08bd1b7797431e2c
- Size of remote file:
- 171 MB
- SHA256:
- 4e883d8ef28038932d40fe65c5e3571e3611855a0028cb2f0b9725925f2a2e11
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