Instructions to use ProbeX/Model-J__ResNet__model_idx_0988 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_0988 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_0988") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0988") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0988", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ProbeX/Model-J__ResNet__model_idx_0988: direct link, hf CLI and curl.
- Browser
- Download file 171 MB
-
https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0988/resolve/main/model.safetensors
- Command line
-
hf download hf://ProbeX/Model-J__ResNet__model_idx_0988/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ProbeX/Model-J__ResNet__model_idx_0988/resolve/main/model.safetensors
171 MB
- Xet hash:
- aafe96fd1a10844c06f12b4acef9d16dfa644278005ec0d4376818e2461f6caf
- Size of remote file:
- 171 MB
- SHA256:
- 78681a10ca90334f077fae9e4f15923f78213c44d66eb332b06a4e4b8f00241c
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