0/1 Deep Neural Networks via Block Coordinate Descent

Benchmark Model Rank Results
fine-grained-image-classification-on-cub-200-1IELTAccuracy: 91.8
fracture-detection-on-grazpedwri-dxYOLOv5sFracture Sensitivity: 91.00
fracture-detection-on-grazpedwri-dxYOLOv6sFracture Sensitivity: 89.00
graph-classification-on-proteinsEff.resistance graph kernelAccuracy: 65.7% ± 4.2%
image-classification-on-imagenetHMAXTop 1 Accuracy: 38.3%
low-light-image-enhancement-on-lolrrBSQ-rate over MS-SSIM: 0.2
multimodal-emotion-recognition-on-iemocap-4bc-LSTMWeighted F1: 74.1
question-answering-on-multitqTimeR4Hits@1: 72.8
question-answering-on-newsqaOpenAI/o1-2024-12-17-highEM: 81.44F1: 88.72
real-time-object-detection-on-cocoD-FINE-L+box AP: 57.1FPS (V100, b=1): 124 (T4)
robot-manipulation-generalization-on-theRVTAverage decrease average across all perturbations: -14.5
text-to-3d-on-t-3-benchProlificDreamerAvg: 43.3
unsupervised-domain-adaptation-on-office-homeDisClusterDAAverage Accuracy: 71.4