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Kneron AI Training Platform Model_Zoo

Introduction

We provide a collection of classification and detection models pre-trained on the ImageNet dataset and the COCO dataset. In the table below, we summarized each such pre-trained model including:

  • a model name.
  • model input size.
  • model speed: we report frame per second (fps) evaluated on our 520 and 720 hardwares.
  • model size.
  • model performance on the ImageNet validation set and COCO validation set.

Pre-trained Classification Models

Model Input Size FPS on 520 FPS on 720 Model Size Rank 1 Accuracy Rank 5 Accuracy
mobilenetv2 224x224 58.9418 620.677 14M 69.82% 89.29%
resnet18 224x224 20.4376 141.371 46.9M 66.46% 87.09%
resnet50 224x224 6.32576 49.0828 102.9M 72.80% 90.91%
FP_classifier 56x32 323.471 3370.47 5.1M 94.13% -

mobilenetv2, resnet18 and resnet50 are models pre-trained on ImageNet classification dataset. FP_classifier is a model pre-trained on our own dataset for classifying person and background images.

Resnet50 is currently under training for Kneron preprocessing.

Pre-trained Detection Models

Backbone Input Size FPS on 520 FPS on 720 Model Size mAP
YOLOv5s (no upsample) 640x640 4.91429 - 13.1M 40.4%
YOLOv5s (with upsample) 640x640 - 24.4114 14.6M 50.9%
FCOS (darknet53s backbone) 416x416 7.27369 48.8437 33.9M 44.8%

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