Instructions to use ProbeX/Model-J__ResNet__model_idx_0044 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_0044 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_0044") 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_0044") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0044") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59384abf3751cd3b0077a7c6e86bb00cbe786f1622fe6ddd45472b75c445853d
- Size of remote file:
- 5.37 kB
- SHA256:
- 072618ee70a017f9f24a7342500375ca54f1f912941562192200cba0a07963f2
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