ResNet-101 (ImageNet)

Training compute

7×10¹⁸ FLOP

Parameters

44.5M

Published

Dec 10, 2015

ResNet-101 (ImageNet) is an AI model developed by Microsoft (United States), first published in December 2015. It works in the vision domain, on tasks such as image classification.

Training it took an estimated 7×10¹⁸ FLOP of compute (estimation method: operation counting). The model has 44,500,000 parameters. It was trained on roughly 1.3M datapoints.

Access: Open weights (unrestricted). Its weights are openly available. The reference paper has 228,517 citations. Epoch AI rates the confidence of this record as confident.

Full record

Organization: Microsoft
Country of organization: United States
Domain: Vision
Task: Image classification
Training compute: 7×10¹⁸ FLOP
Compute estimation method: Operation counting
Parameters: 44,500,000
Dataset size: 1.3M
Model accessibility: Open weights (unrestricted)
Open weights: Yes
Citations: 228,517
Epoch confidence: Confident