Instructions to use KMH158/resnet_152_neurofusion_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KMH158/resnet_152_neurofusion_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="KMH158/resnet_152_neurofusion_classification") 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("KMH158/resnet_152_neurofusion_classification") model = AutoModelForImageClassification.from_pretrained("KMH158/resnet_152_neurofusion_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from KMH158/resnet_152_neurofusion_classification: direct link, hf CLI and curl.
- Browser
- Download file 233 MB
-
https://huggingface.co/KMH158/resnet_152_neurofusion_classification/resolve/main/model.safetensors
- Command line
-
hf download hf://KMH158/resnet_152_neurofusion_classification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/KMH158/resnet_152_neurofusion_classification/resolve/main/model.safetensors
233 MB
- Xet hash:
- 80e55de393da9e2e9c03aafe682d9df197f06ec9335ab807bdbc7ff3d98a7f73
- Size of remote file:
- 233 MB
- SHA256:
- 72181f935ca1011bfde1f09fc8b6b45e2640430190f4c535e0993176fe57edb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.