Instructions to use aaa12963337/msi-resnet-18-pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aaa12963337/msi-resnet-18-pretrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="aaa12963337/msi-resnet-18-pretrain") 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("aaa12963337/msi-resnet-18-pretrain") model = AutoModelForImageClassification.from_pretrained("aaa12963337/msi-resnet-18-pretrain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4b5d43c0acdc0b271296ae1227f9d1f114f65fe12e63650d99eeb80d10a11fc1
- Size of remote file:
- 44.8 MB
- SHA256:
- f846819b6d30be6b510dbda3e42b31cb291224b57f2d12fae6135e6a8d830ca6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.