Instructions to use microsoft/resnet-18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/resnet-18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/resnet-18") 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("microsoft/resnet-18") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-18") - Inference
- Notebooks
- Google Colab
- Kaggle
Details on training hyperparameters
#5
by egesko19 - opened
Is there any way I can find details on how such high accuracies in Imagenet-1k is reached? I am trying to replicate it by training it from scratch to reach similar metrics, but I usually end up around 60% accuracy before validation loss starts to increase, which is a massive difference compared to the pretrained model that reaches around 90% accuracy on validation dataset.