From 103e5767e3909a851b74a760430ecbef5b0119b1 Mon Sep 17 00:00:00 2001 From: Letong Han <106566639+letonghan@users.noreply.github.com> Date: Fri, 12 Aug 2022 17:43:11 +0800 Subject: [PATCH] Update release data (#1138) (cherry picked from commit 7e7f719a79fa7ad6476feb571314b06b7285fa59) --- README.md | 2 +- docs/imgs/release_data.png | Bin 165731 -> 99928 bytes docs/validated_model_list.md | 2263 ++++++++++++++++------------------ 3 files changed, 1034 insertions(+), 1231 deletions(-) diff --git a/README.md b/README.md index 00c12eed25f..8440843e552 100755 --- a/README.md +++ b/README.md @@ -135,7 +135,7 @@ IntelĀ® Neural Compressor validated 420+ [examples](./examples) for quantization More details for validated models are available [here](docs/validated_model_list.md). - Architecture + Architecture ## Documentation diff --git a/docs/imgs/release_data.png b/docs/imgs/release_data.png index edff619ea3900c6755c4887b56c81e61eee8ca47..f0f6a0451168dd39a68a8e59806800ee5b3f0029 100644 GIT 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Scalable processor, using 1 socket, 4 cores/instance, 10 instances and batch size 1. +Performance results test on ā€‹ā€‹07/26/2022 with Intel Xeon Platinum 8380 Scalable processor, using 1 socket, 4 cores/instance, 10 instances and batch size 1. Performance varies by use, configuration and other factors. See [platform configuration](./platform_configuration.md) for configuration details. For more complete information about performance and benchmark results, visit www.intel.com/benchmarks ### TensorFlow models with Intel TensorFlow 2.9.1 - +
- - - - + + + + - - - - - - + + + + + + - - - - - - - + + + + + + + - - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + + + + + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + + - - - - - - - + + + + + + +
ModelAccuracyPerformance
throughput (samples/sec)
ExampleModelAccuracyPerformance
Throughput(samples/sec)
Example
INT8FP32Accuracy Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]INT8FP32Accuracy Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]
BERT large SQuAD92.3992.99-0.64%25.3212.532.02xBERT large SQuAD92.4392.99-0.60%25.3712.552.02x pb
DenseNet12173.57%72.89%0.93%370.52329.741.12xDenseNet12173.57%72.89%0.93%368.05328.841.12x pb
DenseNet16176.24%76.29%-0.07%219.46180.751.21xDenseNet16176.24%76.29%-0.07%219.08179.21.22x pb
DenseNet16974.40%74.65%-0.33%301.33259.881.16xDenseNet16974.40%74.65%-0.33%295.26260.271.13x pb
Faster R-CNN Inception ResNet V237.98%38.33%-0.91%3.962.341.69xFaster R-CNN Inception ResNet V237.98%38.33%-0.91%3.972.341.70x pb
Faster R-CNN Inception ResNet V2 37.84%38.33%-1.28%3.982.311.72xFaster R-CNN Inception ResNet V2 37.84%38.33%-1.28%42.321.73x SavedModel
Faster R-CNN ResNet10130.28%30.39%-0.36%7019.983.50xFaster R-CNN ResNet10130.28%30.39%-0.36%70.3220.193.48x pb
Faster R-CNN ResNet10130.37%30.39%-0.07%70.2616.984.14x30.37%30.39%-0.07%70.317.14.11x SavedModel
Inception ResNet V280.44%80.40%0.05%281.79137.912.04xFaster R-CNN ResNet5026.57%26.59%-0.08%83.3224.383.42x pb
Inception V170.48%69.74%1.06%2193.17975.62.25xInception ResNet V280.28%80.40%-0.15%287.05136.782.10x pb
Inception V274.36%73.97%0.53%1835.35838.822.19xInception V170.48%69.74%1.06%2208.41977.762.26x pb
Inception V377.28%76.75%0.69%973.42376.32.59xInception V274.36%73.97%0.53%1847.6828.032.23xpb
Inception V376.71%76.75%-0.05%1036.18373.612.77x pb
Inception V480.40%80.27%0.16%575.9200.552.87xInception V480.20%80.27%-0.09%592.46206.962.86x pb
Mask R-CNN Inception V228.53%28.73%-0.70%132.5150.32.63xMask R-CNN Inception V228.53%28.73%-0.70%132.07512.59x pb
Mask R-CNN Inception V2 28.53%28.73%-0.70%132.8950.972.61xMask R-CNN Inception V2 28.53%28.73%-0.70%132.4150.942.60x ckpt
MobileNet V171.79%70.96%1.17%3545.791191.942.97xMobileNet V171.79%70.96%1.17%3603.941304.582.76x pb
MobileNet V271.89%71.76%0.18%2431.661420.111.71xMobileNet V271.89%71.76%0.18%2433.871446.11.68x pb
ResNet10177.50%76.45%1.37%877.91355.492.47xResNet10177.50%76.45%1.37%874.26356.842.45x pb
ResNet50 Fashion77.80%78.12%-0.41%3977.52150.681.85xResNet50 Fashion78.06%78.12%-0.08%3776.142160.521.75x pb
ResNet50 V1.074.11%74.27%-0.22%1509.64472.663.19xResNet50 V1.074.11%74.27%-0.22%1511.74459.433.29x pb
ResNet50 V1.576.82%76.46%0.47%1260.01415.833.03xResNet50 V1.576.22%76.46%-0.31%1355.03423.413.20x pb
ResNet V2 10172.67%71.87%1.11%436.52318.31.37xResNet V2 10172.67%71.87%1.11%436.34323.151.35x pb
ResNet V2 15273.03%72.37%0.91%306.82221.41.39xResNet V2 15273.03%72.37%0.91%311.93222.831.40x pb
ResNet V2 5070.33%69.64%0.99%749.85574.191.31xResNet V2 5070.33%69.64%0.99%766.83574.761.33x pb
SSD MobileNet V122.97%23.13%-0.69%952.9582.871.63xSSD MobileNet V122.97%23.13%-0.69%959.72586.211.64x pb
SSD MobileNet V122.99%23.13%-0.61%954.92413.242.31xSSD MobileNet V122.99%23.13%-0.61%953.4412.062.31x ckpt
SSD ResNet3421.69%22.09%-1.81%44.4611.813.76xSSD ResNet3421.69%22.09%-1.81%44.5311.863.75x pb
SSD ResNet50 V137.86%38.00%-0.37%69.526.042.67xSSD ResNet50 V137.86%38.00%-0.37%69.0925.932.66x pb
SSD ResNet50 V137.81%38.00%-0.50%69.2721.173.27xSSD ResNet50 V137.81%38.00%-0.50%69.0221.063.28x ckpt
VGG1672.66%70.89%2.50%660.46177.853.71xVGG1672.66%70.89%2.50%660.05177.233.72x pb
VGG1972.72%71.01%2.41%562.04147.613.81xVGG1972.72%71.01%2.41%560.39147.273.81x pb
Wide & Deep77.62%77.67%-0.07%21332.4719714.081.08xWide & Deep77.62%77.67%-0.07%23329.1820930.181.11x pb
-### PyTorch models with Torch 1.11.0+cpu in PTQ mode +### PyTorch models with Torch 1.12.0+cpu in PTQ mode - +
- - - - + + + + - - - - - - + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelAccuracyPerformance
throughput (samples/sec)
ExampleModelAccuracyPerformance
Throughput (samples/sec)
Example
INT8FP32Accuracy Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]INT8FP32Acc Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]
ALBERT base MRPC88.06%88.50%-0.50%34.2829.541.16xeager
Barthez MRPC82.99%83.81%-0.97%166.8489.561.86xeager
BERT base COLA58.80%58.84%-0.07%260126.472.06xfx
BERT base MRPC90.28%90.69%-0.45%251.79126.461.99xfx
BERT base RTE69.31%69.68%-0.52%252.14126.451.99xfx
BERT base SST291.97%91.86%0.12%258.98126.422.05xfx
BERT base STSB89.13%89.75%-0.68%249.57126.391.97xfx
BERT large COLA62.88%62.57%0.49%88.7536.72.42xfx
BERT large MRPC89.93%90.38%-0.49%89.4336.622.44xfx
BERT large QNLI90.96%91.82%-0.94%91.27372.47xfx
BERT large RTE71.84%72.56%-1.00%77.6236.012.16xfx
CamemBERT base MRPC86.56%86.82%-0.30%241.39124.771.93xeager
Deberta MRPC91.17%90.91%0.28%152.0985.131.79xeager
DistilBERT base MRPC88.66%89.16%-0.56%415.09246.91.68xeager
DistilBERT base MRPC88.74%89.16%-0.47%459.93245.331.87xfx
FlauBERT MRPC81.01%80.19%1.01%644.05457.321.41xeager
Inception V369.43%69.52%-0.13%454.3213.72.13xeager
Longformer MRPC90.59%91.46%-0.95%21.5117.451.23xeager
Mask R-CNN37.70%37.80%-0.26%17.615.763.06xeager
mBart WNLI56.34%56.34%0.00%65.0531.262.08xeager
MobileNet V270.54%71.84%-1.81%740.97535.541.38xeager
lvwerra/pegasus-samsum42.2142.67-1.09%3.891.143.41xeager
PeleeNet71.64%72.10%-0.64%502.01391.311.28xeager
ResNet18 69.57%69.76%-0.27%800.43381.272.10xeager
ResNet18 69.57%69.76%-0.28%811.09389.362.08xfx
ResNet5075.98%76.15%-0.21%507.55200.522.53xeager
ResNeXt101_32x8d79.08%79.31%-0.29%203.5473.852.76xeager
RNN-T92.4592.55-0.10%79.2120.473.87xeager
Roberta Base MRPC87.88%88.18%-0.34%250.21124.922.00xeager
Se_ResNeXt50_32x4d78.98%79.08%-0.13%358.63173.032.07xeager
SqueezeBERT MRPC87.77%87.65%0.14%249.89207.431.20xeager
Transfo-xl MRPC81.97%81.20%0.94%11.258.341.35xeager
YOLOv324.60%24.54%0.21%108.0940.022.70xeagerALBERT base MRPC88.85%88.50%0.40%34.4626.881.28xeager
Barthez MRPC83.92%83.81%0.14%161.0689.611.80xeager
BERT base COLA58.80%58.84%-0.07%262.88125.632.09xfx
BERT base MRPC89.90%90.69%-0.88%244.27125.281.95xfx
BERT base RTE69.31%69.68%-0.52%259.21125.722.06xfx
BERT base SST291.06%91.86%-0.87%262.73125.692.09xfx
BERT base STSB89.10%89.75%-0.72%254.36125.92.02xfx
BERT large COLA64.12%62.57%2.48%89.3636.472.45xfx
BERT large MRPC89.50%90.38%-0.97%88.9236.552.43xfx
BERT large QNLI90.90%91.82%-1.00%90.3936.632.47xfx
CamemBERT base MRPC86.70%86.82%-0.14%236.6121.811.94xfx
Deberta MRPC90.88%90.91%-0.04%149.7684.721.77xeager
DistilBERT base MRPC88.23%89.16%-1.05%426.4246.131.73xeager
FlauBERT MRPC79.87%80.19%-0.40%675.82437.721.54xeager
Inception V369.43%69.52%-0.13%490.32209.872.34xeager
Longformer MRPC91.01%91.46%-0.49%20.3616.651.22xeager
mBart WNLI56.34%56.34%0.00%66.2330.862.15xeager
lvwerra/pegasus-samsum42.3942.67-0.67%3.861.143.38xeager
PeleeNet71.64%72.10%-0.64%511.56387.91.32xeager
ResNet18 69.57%69.76%-0.27%823.22386.932.13xeager
ResNet18 69.57%69.76%-0.28%816.8385.232.12xfx
ResNet5075.98%76.15%-0.21%515.142042.53xeager
ResNeXt101_32x8d79.08%79.31%-0.29%210.3974.872.81xeager
RNNT92.4892.55-0.08%74.1720.383.64xeager
Roberta Base MRPC88.25%88.18%0.08%245.05123.531.98xeager
Se_ResNeXt50_32x4d78.98%79.08%-0.13%370.11172.452.15xeager
SqueezeBERT MRPC86.87%87.65%-0.89%241.25206.031.17xeager
Transfo-xl MRPC81.97%81.20%0.94%11.28.311.35xeager
xlm-roberta-base_MRPC88.03%88.62%-0.67%140.58122.291.15xeager
YOLOv324.60%24.54%0.21%110.5439.462.80xeager
-### PyTorch models with Torch 1.11.0+cpu in QAT mode - +### PyTorch models with Torch 1.12.0+cpu in QAT mode + +
- - - - + + + + - - - - - - + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelAccuracyPerformance
throughput (samples/sec)
ExampleModelAccuracyPerformance
Throughput (samples/sec)
Example
INT8FP32Accuracy Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]INT8FP32Acc Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]
ResNet1869.74%69.76%-0.03%804.76388.672.07xeager
ResNet1869.73%69.76%-0.04%806.44386.592.09xfx
BERT base MRPC QAT89.60%89.50%0.11%258.89125.792.06xfx
ResNet5076.04%76.15%-0.14%490.64203.492.41xeagerResNet1869.84%69.76%0.11%805.76389.142.07xeager
ResNet1869.74%69.76%-0.03%822.99391.822.10xfx
BERT base MRPC QAT89.70%89.50%0.22%173.83107.221.62xfx
ResNet5076.05%76.15%-0.13%500.54195.52.56xeager
-### PyTorch models with IPEX 1.11.0 +### PyTorch models with Torch 1.12.0+cpu IPEX - +
- - - - + + + + + - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + - - - - + + + + + + +
ModelAccuracyPerformance
throughput (samples/sec)
ExampleModelAccuracyPerformance
Throughput (samples/sec)
1s 10ins 4c/ins bs=8
Performance
Throughput (samples/sec)
1s 10ins 4c/ins bs=1
Example
INT8FP32Accuracy Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]INT8FP32Acc Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]INT8FP32INT8/FP32
bert-large-uncased-whole-word-masking-finetuned-squad 92.993.16-0.28%37.1311.453.24xipex
ResNeXt101_32x16d_wsl84.02%84.17%-0.18%163.4528.95.66xipex
ResNet5076.00%76.15%-0.20%707.86202.023.51xipex
SSD ResNet3419.97%19.99% 20.00%-0.15%30.848.553.61x-0.06%11.198.721.28x11.639.821.18x ipex
-### ONNX Models with ONNX Runtime 1.11.0 +### PyTorch models with Torch 1.11.0+cpu IPEX - +
- - - - + + + + + - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelAccuracyPerformance
throughput (samples/sec)
ExampleModelAccuracyPerformance
Throughput(samples/sec)
1s 10ins 4c/ins bs=64
Performance
Throughput(samples/sec)
1s 10ins 4c/ins bs=1
Example
INT8FP32Accuracy Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]INT8FP32Acc Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]INT8FP32INT8/FP32
AlexNet54.74%54.79%-0.09%1518.97676.742.24xqlinearops
AlexNet 54.74%54.79%-0.09%1411.3652.62.16xqdq
BERT base MRPC DYNAMIC85.54%86.03%-0.57%379.71156.162.43xqlinearops
BERT base MRPC STATIC85.29%86.03%-0.86%756.33316.362.39xqlinearops
BERT SQuAD80.4480.67-0.29%115.5864.711.79xqlinearops
BERT SQuAD80.4480.67-0.29%115.464.681.78xqdq
CaffeNet56.19%56.30%-0.20%2786.79802.73.47xqlinearops
CaffeNet56.19%56.30%-0.20%2726.86819.413.33xqdq
DenseNet60.20%60.96%-1.25%404.83340.631.19xqlinearops
DistilBERT base MRPC84.56%84.56%0.00%1630.41596.682.73xqlinearops
EfficientNet77.58%77.70%-0.15%1985.351097.331.81xqlinearops
Faster R-CNN33.99%34.37%-1.11%10.024.322.32xqlinearops
Faster R-CNN33.94%34.37%-1.25%10.414.282.43xqdq
FCN64.66%64.98%-0.49%44.3114.23.12xqlinearops
FCN64.66%64.98%-0.49%18.1114.191.28xqdq
GoogleNet67.61%67.79%-0.27%1165.84810.651.44xqlinearops
GoogleNet67.61%67.79%-0.27%1165.73809.981.44xqdq
Inception V167.23%67.24%-0.01%1205.89838.711.44xqlinearops
Inception V167.23%67.24%-0.01%1204.93843.161.43xqdq
Mask R-CNN33.40%33.72%-0.95%8.563.762.27xqlinearops
Mask R-CNN33.33%33.72%-1.16%8.43.812.20xqdq
Mobile bert MRPC86.03%86.27%-0.28%790.11686.351.15xqlinearops
MobileBERT SQuAD MLPerf89.8490.03-0.20%102.9295.191.08xqlinearops
MobileNet V265.47%66.89%-2.12%5133.843394.731.51xqlinearops
MobileNet V265.47%66.89%-2.12%5066.313386.31.50xqdq
MobileNet V3 MLPerf75.59%75.74%-0.20%4133.222132.921.94xqlinearops
MobileNetV2 (ONNX Model Zoo)68.30%69.48%-1.70%5349.423373.291.59xqlinearops
ResNet50 V1.5 MLPerf76.13%76.46%-0.43%1139.56549.882.07xqlinearops
ResNet50 V1.572.28%72.29%-0.01%1165.35556.022.10xqlinearops
ResNet50 V1.572.28%72.29%-0.01%1319.32543.442.43xqdq
ResNet50 V1.5 (ONNX Model Zoo)74.76%74.99%-0.31%1363.39573.12.38xqlinearops
Roberta Base MRPC90.44%89.95%0.54%811.05312.712.59xqlinearops
ShuffleNet V266.13%66.36%-0.35%4948.772847.661.74xqlinearops
SqueezeNet56.55%56.87%-0.56%6296.794340.511.45xqlinearopsResNet1869.48%69.76%-0.40%3968.051182.363.36x2995.81140.632.63xipex
ResNeXt101_32x16d_wsl84.26%84.17%0.11%195.7251.283.82x178.0159.123.01xipex
ResNet5076.07%76.15%-0.10%1731.8474.243.65x1347.64518.392.60xipex
+ +### ONNX Models with ONNX Runtime 1.11.0 + + + - - - - - - - - + + + + - - - - - - - - + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SqueezeNet56.55%56.87%-0.56%6227.764383.81.42xqdqModelAccuracyPerformance
Throughput(samples/sec)
Example
SSD MobileNet V122.20%23.10%-3.90%917.64709.481.29xqlinearopsINT8FP32Acc Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]
SSD MobileNet V122.20%23.10%-3.90%840.99655.991.28xqdq
SSD MobileNet V1 (ONNX Model Zoo)22.88%23.03%-0.65%845.17666.251.27xqlinearops
SSD MobileNet V1 (ONNX Model Zoo)22.88%23.03%-0.65%790.06624.21.27xqdq
SSD MobileNet V223.83%24.68%-3.44%703.55506.61.39xqlinearops
SSD18.68%18.98%-1.58%41.9911.123.78xqdq
Tiny YOLOv312.08%12.43%-2.82%836.21659.691.27xqlinearops
VGG1666.60%66.69%-0.13%312.48128.982.42xqlinearops
VGG16 (ONNX Model Zoo)72.28%72.40%-0.17%446.13131.043.40xqlinearops
YOLOv326.88%28.74%-6.47%157.3966.722.36xqlinearops
YOLOv433.18%33.71%-1.57%58.5538.091.54xqlinearops
ZFNet55.89%55.96%-0.13%664.37358.621.85xqlinearops
ZFNet55.89%55.96%-0.13%666.99354.381.88xqdqAlexNet 54.74%54.79%-0.09%1498.07649.992.30xqdq
BERT base MRPC DYNAMIC85.54%86.03%-0.57%381.45156.052.44xqlinearops
BERT base MRPC STATIC85.29%86.03%-0.86%766.09316.62.42xqlinearops
BERT SQuAD80.4480.67-0.29%116.8864.591.81xqlinearops
BERT SQuAD80.4480.67-0.29%116.9364.641.81xqdq
BiDAF65.92%66.08%-0.24%1468.581406.211.04xqlinearops
CaffeNet56.26%56.30%-0.07%2750.7812.733.38xqdq
DistilBERT base MRPC84.56%84.56%0.00%1654.95595.322.78xqlinearops
EfficientNet77.58%77.70%-0.15%2066.021096.861.88xqlinearops
FCN64.66%64.98%-0.49%15.137.22.10xqlinearops
GoogleNet67.67%67.79%-0.18%1174.98807.681.45xqdq
Inception V167.23%67.24%-0.01%1181.71831.011.42xqdq
Mobile bert MRPC86.03%86.27%-0.28%774.96678.661.14xqlinearops
MobileBERT SQuAD MLPerf89.8490.03-0.20%104.5194.881.10xqlinearops
MobileNet V265.47%66.89%-2.12%5172.043312.761.56xqlinearops
MobileNet V3 MLPerf75.59%75.74%-0.20%4168.82146.591.94xqlinearops
ResNet50 v1.5 MLPerf76.13%76.46%-0.43%1154.73554.692.08xqlinearops
ResNet50 V1.572.28%72.29%-0.01%1156.05555.722.08xqlinearops
ResNet50 V1.5 (ONNX Model Zoo)74.76%74.99%-0.31%1347.89588.842.29xqlinearops
ResNet50 V1.5 (ONNX Model Zoo)74.75%74.99%-0.32%840.87588.771.43xqdq
Roberta Base MRPC90.44%89.95%0.54%811.74312.932.59xqlinearops
Tiny YOLOv312.08%12.43%-2.82%801.46653.421.23xqlinearops
VGG1666.60%66.69%-0.13%312.98128.72.43xqlinearops
VGG16 (ONNX Model Zoo)72.28%72.40%-0.17%450.47130.743.45xqlinearops
YOLOv326.88%28.74%-6.47%157.5866.622.37xqlinearops
ZFNet55.89%55.96%-0.13%658.93359.421.83xqdq
### MXNet models with MXNet 1.7.0 - +
- - - + + + - - - - - - + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelAccuracyPerformance
throughput (samples/sec)
ModelAccuracyPerformance
Throughput(samples/sec)
INT8FP32Accuracy Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]INT8FP32Acc Ratio[(INT8-FP32)/FP32]INT8FP32Performance Ratio[INT8/FP32]
Inception V377.80%77.65%0.20%920.74276.733.33x
MobileNet V171.60%72.23%-0.86%6585.192529.212.60x
MobileNet V270.80%70.87%-0.10%5230.321996.472.62x
ResNet V1 15278.28%78.54%-0.33%574.85156.23.68x
ResNet50 V1.075.91%76.33%-0.55%1567.9427.993.66x
SqueezeNet56.80%56.97%-0.28%4704.511332.293.53x
SSD MobileNet V174.94%75.54%-0.79%769.26193.033.99xInception V377.80%77.65%0.20%922.38277.593.32x
MobileNet V171.60%72.23%-0.86%6614.692560.422.58x
MobileNet V3 MLPerf70.80%70.87%-0.10%5230.582024.852.58x
ResNet v1 15278.28%78.54%-0.33%578.27156.383.70x
ResNet50 V1.075.91%76.33%-0.55%1571.33429.533.66x
SqueezeNet56.80%56.97%-0.28%4712.151323.683.56x
SSD MobileNet V174.94%75.54%-0.79%768.59191.554.01x