- 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.
Ethos:
- 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 Tensorflow 1
curl https://raw.githubusercontent.com/RattyDAVE/pi-object-detection/master/install.sh|/bin/sh
- To install Tensorflow 2 beta
curl https://raw.githubusercontent.com/RattyDAVE/pi-object-detection/master/install2.sh|/bin/sh
curl https://raw.githubusercontent.com/RattyDAVE/pi-object-detection/master/run.sh|/bin/sh
curl https://raw.githubusercontent.com/RattyDAVE/pi-object-detection/master/uninstall.sh|/bin/sh
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 install.sh 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 |
Size | Model | Status rpi4 | FPS rpi4 |
---|---|---|---|
? | ssd_mobilenet_v3_large_coco | ||
? | ssd_mobilenet_v3_small_coco |
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'