22 citations · 27 across the 3 of their papers we have counts for
4 papers · 1 filter
Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results
Tal Ridnik, Hussam Lawen, Emanuel Ben-Baruch +1
ImageNet serves as the primary dataset for evaluating the quality of computer-vision models. The common practice today is training each architecture with a tailor-made scheme, desi…
Multi-label Classification with Partial Annotations using Class-aware Selective Loss
Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman +4
Large-scale multi-label classification datasets are commonly, and perhaps inevitably, partially annotated. That is, only a small subset of labels are annotated per sample. Differen…
ImageNet-21K Pretraining for the Masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy +1
ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which is bigger and more diverse, is used less frequ…
TResNet: High Performance GPU-Dedicated Architecture
Tal Ridnik, Hussam Lawen, Asaf Noy +3
Many deep learning models, developed in recent years, reach higher ImageNet accuracy than ResNet50, with fewer or comparable FLOPS count. While FLOPs are often seen as a proxy for…