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MODEL_ZOO.md

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TPU Object Detection and Segmentation Model Zoo

Introduction

Model zoo provides a large collection of baselines and checkpoints for object detection, instance segmentation, and image classification.

Object Detection and Instance Segmentation

Common Settings and Notes

  • We provide models based on two detection frameworks, RetinaNet or Mask R-CNN, and three backbones, ResNet-FPN, ResNet-NAS-FPN, or SpineNet.
  • Models are all trained on COCO train2017 and evaluated on COCO val2017.
  • Training details:
    • Models finetuned from ImageNet pretrained checkpoints adopt the 36 epochs (~3x) schedule, where 1x is around 12 COCO epochs.
    • Most models trained from scratch adopt the 72 or 350 epochs schedule.
    • The default training data augmentation implements horizontal flipping and scale jittering with a random scale between [0.5, 2.0].
    • Unless noted, all models are trained with l2 weight regularization and ReLU activation.
    • We use batch size 256 and stepwise learning rate that decays at the last 30 and 10 epoch.
    • We use square image as input by resizing the long side of an image to the target size then padding the short side with zeros.
  • Inference latency:
    • Latency is measured on a V100/P100 GPU from inputs to raw outputs (without image pre-processing or post-processing, e.g. NMS).
    • TensorRT optimization is not implemented in all tests.

COCO Object Detection Baselines

RetinaNet (ImageNet pretrained)

Coming soon.

RetinaNet (Trained from scratch)

model resolution epochs FLOPs (B) params (M) V100 / P100
lat (ms/im)		 box AP download
R50-FPN 640x640 350 97.0 34.0 23 / 37 40.4 ckpt | config
R101-FPN 1024x1024 350 326.3 53.1 55 / 95 43.9 ckpt | config
R152-FPN 1280x1280 350 630.5 68.7 100 / 167 45.2 ckpt | config
R50-NAS-FPN 640x640 72 140.6 60.3 29 / 48 37.3 N/A
R50-NAS-FPN 640x640 350 140.6 60.3 29 / 48 42.4 ckpt | config
SpineNet-49 640x640 72 85.4 28.5 24 / 38 37.7 N/A
SpineNet-49 640x640 350 85.4 28.5 24 /38 42.8 ckpt | config
SpineNet-49S 640x640 350 33.8 11.9 19 / 26 39.5 ckpt | config
SpineNet-96 1024x1024 350 265.4 43.0 53 / 87 46.7 ckpt | config
SpineNet-143 1280x1280 350 524.0 67.0 97 / 159 48.0 ckpt | config

SpineNet models trained with stochastic depth and swish activation for a longer shedule:

model resolution epochs FLOPs (B) params (M) box AP download
SpineNet-49S 640x640 500 33.8 11.9 41.5 ckpt | config
SpineNet-49 640x640 500 85.4 28.5 44.3 ckpt | config
SpineNet-96 1024x1024 500 265.4 43.0 48.5 ckpt | config
SpineNet-143 1280x1280 500 524.0 67.0 50.6 ckpt | config
SpineNet-190 1280x1280 400 1885.0 163.6 52.0 ckpt | config

Mobile RetinaNet (Trained from scratch)

model resolution epochs FLOPs (B) params (M) box AP download
SpineNetMB-49 384x384 600 1.0 2.34 28.6 ckpt | config

Instance Segmentation Baselines

Mask R-CNN (ImageNet pretrained)

Coming soon.

Mask R-CNN (Trained from scratch)

model resolution epochs FLOPs (B) params (M) box AP mask AP download
SpineNet-49 640x640 350 215.7 40.8 42.8 37.8 ckpt | config
SpineNet-96 1024x1024 350 314.6 55.2 46.8 41.2 ckpt | config
SpineNet-143 1280x1280 350 498.4 79.2 48.7 42.6 ckpt | config

SpineNet-190 trained with stochastic depth and swish activation for a longer shedule:

model resolution epochs FLOPs (B) params (M) box AP mask AP download
SpineNet-190 1536x1536 400 1685.7 168.2 52.0 45.9 ckpt | config

Image Classification

Common Settings and Notes

  • We provide ImageNet and iNaturalist-2017 pretrained checkpoints for ResNet and SpineNet models at various scales.
  • Training details:
    • All models are trained from scratch for 200 epochs with cosine learning rate decay and batch size 4096.
    • Unless noted, all models are trained with l2 weight regularization and ReLU activation.

ImageNet Baselines

model resolution epochs FLOPs (B) params (M) Top-1 Top-5 download
ResNet-34 224x224 200 3.7 21.8 74.4 92.0 ckpt | config
ResNet-50 224x224 200 4.1 25.6 77.1 93.6 ckpt | config
ResNet-101 224x224 200 7.8 44.6 78.2 94.2 ckpt | config
ResNet-152 224x224 200 11.5 60.2 78.4 94.2 ckpt | config
SpineNet-49 224x224 200 3.5 22.1 77.0 93.3 ckpt | config
SpineNet-96 224x224 200 5.7 36.5 78.2 94.0 ckpt | config
SpineNet-143 224x224 200 9.1 60.5 79.0 94.4 ckpt| config

SpineNet models trained with stochastic depth, swish activation, and label smoothing:

model resolution epochs FLOPs (B) params (M) Top-1 Top-5 download
SpineNet-49 224x224 200 3.5 22.1 78.1 94.0 ckpt | config
SpineNet-96 224x224 200 5.7 36.5 79.4 94.6 ckpt| config
SpineNet-143 224x224 200 9.1 60.5 80.1 95.0 ckpt | config
SpineNet-190 224x224 200 19.1 127.1 80.8 95.3 ckpt | config

iNaturalist-2017 Baselines

model resolution epochs FLOPs (B) params (M) Top-1 Top-5
ResNet-34 224x224 200 3.7 23.9 54.1 76.7
ResNet-50 224x224 200 4.1 33.9 54.6 77.2
ResNet-101 224x224 200 7.8 52.9 57.0 79.3
ResNet-152 224x224 200 11.5 68.6 58.4 80.2
SpineNet-49 224x224 200 3.5 23.1 59.3 81.9
SpineNet-96 224x224 200 5.7 37.6 61.7 83.4
SpineNet-143 224x224 200 9.1 61.6 63.6 84.8

SpineNet models trained with stochastic depth, swish activation, and label smoothing:

model resolution epochs FLOPs (B) params (M) Top-1 Top-5
SpineNet-49 224x224 200 3.5 23.1 63.3 85.1
SpineNet-96 224x224 200 5.7 37.6 64.7 85.9
SpineNet-143 224x224 200 9.1 61.6 66.7 87.1
SpineNet-190 224x224 200 19.1 129.2 67.6 87.4