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