Instructions to use thethinkmachine/student-resnet18-tiny-imagenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thethinkmachine/student-resnet18-tiny-imagenet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="thethinkmachine/student-resnet18-tiny-imagenet") 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("thethinkmachine/student-resnet18-tiny-imagenet") model = AutoModelForImageClassification.from_pretrained("thethinkmachine/student-resnet18-tiny-imagenet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f65bf98ca9c91b6ddd8cd37363505b6d8ff3755ae33a7daeeac8d6bba975ef85
- Size of remote file:
- 5.84 kB
- SHA256:
- 68091efc800d6c0790d161cd0ea5442255e2e93b59e53f55952a7a530594448a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.