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Object Detection on Raspberry Pi 4/3

Tested On

  • RPi4 with USB camera
  • RPi4 with Raspberry Pi Camera Module.
  • RPi3 with USB camera
  • RPi3 with Raspberry Pi Camera Module.

The purpose is to get the object detection and proof of concept working in the minimum time.


  • We use pre-compiled binaries where possible from the Raspberry Pi repository.
  • The python code contains the minimal needed to be functional.


  • Image size of 640x480
  • ssdlite_mobilenet_v2_coco_2018_05_09


  • RPi4 You can expect 2.3 FP/S using the defaults.
  • RPi3 You can expect 1.2 FP/S using the defaults.

This uses pretrained models and can has the ability to change the model easy using the configuration file.


To install run the following.

  • To install Tensorflow 1
  • To install Tensorflow 2 beta

To start the object detetection run the following


To uninstall and wipe all traces run the following.



All models are taken from Tensorflow detection model zoo

The default is ssdlite_mobilenet_v2_coco_2018_05_09. You can change the models but uncommenting the line in the and the obj-config.ini

COCO-trained models from COCO dataset

90 Classes

Size Model Status rpi4 FPS rpi4
73MB ssd_mobilenet_v1_coco_2018_01_28
44MB ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03
81MB ssd_mobilenet_v1_quantized_300x300_coco14_sync_2018_07_18
49MB ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync_2018_07_18
29MB ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03
129MB ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03
349MB ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03
179MB ssd_mobilenet_v2_coco_2018_03_29
138MB ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03
48MB ssdlite_mobilenet_v2_coco_2018_05_09 WORKS 2.8
265MB ssd_inception_v2_coco_2018_01_28
142MB faster_rcnn_inception_v2_coco_2018_01_28
363MB faster_rcnn_resnet50_coco_2018_01_28
363MB faster_rcnn_resnet50_lowproposals_coco_2018_01_28
622MB rfcn_resnet101_coco_2018_01_28
565MB faster_rcnn_resnet101_coco_2018_01_28
565MB faster_rcnn_resnet101_lowproposals_coco_2018_01_28
641MB faster_rcnn_inception_resnet_v2_atrous_coco_2018_01_28
641MB faster_rcnn_inception_resnet_v2_atrous_lowproposals_coco_2018_01_28
1.09GB faster_rcnn_nas_coco_2018_01_28
1.09GB faster_rcnn_nas_lowproposals_coco_2018_01_28
693MB mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28
169MB mask_rcnn_inception_v2_coco_2018_01_28
631MB mask_rcnn_resnet101_atrous_coco_2018_01_28
428MB mask_rcnn_resnet50_atrous_coco_2018_01_28

Mobile models

Size Model Status rpi4 FPS rpi4
? ssd_mobilenet_v3_large_coco
? ssd_mobilenet_v3_small_coco

Pixel4 Edge TPU models

Size Model Status rpi4 FPS rpi4
? ssd_mobilenet_edgetpu_coco

Kitti-trained models from Kitti dataset

2 Classes

Size Model Status rpi4 FPS rpi4
555MB faster_rcnn_resnet101_kitti_2018_01_28 FAILED (bad alloc)

Open Images-trained models from Open Images dataset

601 Classes

Size Model Status rpi4 FPS 4
680MB faster_rcnn_inception_resnet_v2_atrous_oid_2018_01_28
680MB faster_rcnn_inception_resnet_v2_atrous_lowproposals_oid_2018_01_28
124MB facessd_mobilenet_v2_quantized_320x320_open_image_v4
682MB faster_rcnn_inception_resnet_v2_atrous_oid_v4_2018_12_12
151MB ssd_mobilenet_v2_oid_v4_2018_12_12 Works 1.5
608MB ssd_resnet101_v1_fpn_shared_box_predictor_oid_512x512_sync_2019_01_20

iNaturalist Species-trained models from iNaturalist Species Detection Dataset

2854 Classs

Size Model Status rpi4 FPS rpi4
868MB faster_rcnn_resnet101_fgvc_2018_07_19
666MB faster_rcnn_resnet50_fgvc_2018_07_19 FAILED (bad alloc)

AVA v2.1 trained models from AVA v2.1 dataset

AVA is a project that provides audiovisual annotations of video for improving our understanding of human activity.

90 Classes

Size Model Status rpi4 FPS rpi4
565MB faster_rcnn_resnet101_ava_v2.1_2018_04_30 FAILED (bad alloc)

TypeError: int() argument must be a string, a bytes-like object or a number, not 'NoneType'

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