Instructions to use ProbeX/Model-J__ResNet__model_idx_0747 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_0747 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_0747") 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_0747") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0747", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8764bf7c6c0a5a1a4f4004e0cf037dd8fa2e9708ae1a99059b44b9dc81db1e53
- Size of remote file:
- 171 MB
- SHA256:
- 03be7160e33db51e959cd7e7fbed0094bfa165e4242a98241b89cacc7d2ffa32
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.