Model Zoo Results¶

Results obtained on NVIDIA’s A100 GPU and TensorRT 8.4.

ResNet ¶

ResNet50-v1¶

Model	Accuracy (%)	Latency (ms, bs=1)
Baseline (TensorFlow)	75.05	7.95
PTQ (TensorRT)	74.96	0.46
QAT (TensorRT)	75.12	0.45

ResNet50-v2¶

Model	Accuracy (%)	Latency (ms, bs=1)
Baseline (TensorFlow)	75.36	6.16
PTQ (TensorRT)	75.48	0.57
QAT (TensorRT)	75.65	0.57

ResNet101-v1¶

Model	Accuracy (%)	Latency (ms, bs=1)
Baseline (TensorFlow)	76.47	15.92
PTQ (TensorRT)	76.32	0.84
QAT (TensorRT)	76.26	0.84

ResNet101-v2¶

Model	Accuracy (%)	Latency (ms, bs=1)
Baseline (TensorFlow)	76.89	14.13
PTQ (TensorRT)	76.94	1.05
QAT (TensorRT)	77.15	1.05

QAT fine-tuning hyper-parameters: bs=32 (bs=64 was OOM).

MobileNet ¶

MobileNet-v1¶

Model	Accuracy (%)	Latency (ms, bs=1)
Baseline (TensorFlow)	70.60	1.99
PTQ (TensorRT)	69.31	0.16
QAT (TensorRT)	70.43	0.16

MobileNet-v2¶

Model	Accuracy (%)	Latency (ms, bs=1)
Baseline (TensorFlow)	71.77	3.71
PTQ (TensorRT)	70.87	0.30
QAT (TensorRT)	71.62	0.30

EfficientNet ¶

EfficientNet-B0¶

Model	Accuracy (%)	Latency (ms, bs=1)
Baseline (TensorFlow)	76.97	6.77
PTQ (TensorRT)	71.71	0.67
QAT (TensorRT)	75.82	0.68

QAT fine-tuning hyper-parameters: bs=64, ep=10, lr=0.001, steps_per_epoch=None.

EfficientNet-B3¶

Model	Accuracy (%)	Latency (ms, bs=1)
Baseline (TensorFlow)	81.36	10.33
PTQ (TensorRT)	78.88	1.24
QAT (TensorRT)	79.48	1.23

QAT fine-tuning hyper-parameters: bs=32, ep20, lr=0.0001, steps_per_epoch=None.

Note

The results here were obtained with NVIDIA's A100 GPU and TensorRT 8.4.

Accuracy metric: Top-1 validation accuracy with the full ImageNet dataset.

Hyper-parameters

QAT fine-tuning: bs=64, ep=10, lr=0.001 (unless otherwise stated).
PTQ calibration: bs=64.