An open-source library for training PyTorch-based computer vision models. Developed by Deci’s deep learning experts for the benefit of the AI community.
Easily load and fine-tune production-ready, pre-trained SOTA models that incorporate best practices and validated hyper-parameters for achieving best-in-class accuracy.
Integrate any custom dataset, losses, metrics to SuperGradients.
Easy plug and play your own PyTorch data loaders. Compatible with PyTorch dataset, losses, and metrics instead of PyTorch based data loader.
Save time with better insight into the most common training errors. Quickly understand how to overcome them with suggested remediations tips.
|Model||Dataset||Resolution||Paper Top-1 Accuracy||SG Top-1 Accuracy|
ResNet is an artificial neural network. It is a gateless or open-gated variant of the HighwayNet, the first working very deep feedforward neural network with hundreds of layers. In this walkthrough, Harpreet Sahota, DevRel Manager at Deci, demonstrates ResNet in action. You can check out the notebook here and follow along!
EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient. Join Harpreet Sahota, DevRel Manager at Deci, for a walkthrough of ResNet in action. You can check out the notebook and follow along!
RegNet is a highly flexible network design space defined by a quantized linear function. It can be specified and scaled for high efficiency or high accuracy. Join Harpreet Sahota, DevRel Manager at Deci, for a walkthrough of RegNet in action, using it to determine whether an image is Santa or not. You can check out the notebook and follow along!
from transformers import AutoFeatureExtractor, AutoModelForImageClassification extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50") model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")