https://github.com/qengineering/tensorflow_lite_ssd_rpi_32-bits
TensorFlow Lite SSD on a bare Raspberry Pi 4 at 17 FPS
https://github.com/qengineering/tensorflow_lite_ssd_rpi_32-bits
armv7 armv8 bare-raspberry-pi cpp deep-learning frame-rate high-fps jamesbond lite raspberry-pi-4 ssd-mobilenet tensorflow-examples tensorflow-lite testtensorflow-lite
Last synced: 3 months ago
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TensorFlow Lite SSD on a bare Raspberry Pi 4 at 17 FPS
- Host: GitHub
- URL: https://github.com/qengineering/tensorflow_lite_ssd_rpi_32-bits
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2020-03-08T11:39:50.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-12-27T10:01:26.000Z (almost 4 years ago)
- Last Synced: 2025-03-27T09:23:13.314Z (6 months ago)
- Topics: armv7, armv8, bare-raspberry-pi, cpp, deep-learning, frame-rate, high-fps, jamesbond, lite, raspberry-pi-4, ssd-mobilenet, tensorflow-examples, tensorflow-lite, testtensorflow-lite
- Language: C++
- Homepage: https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html
- Size: 21.1 MB
- Stars: 16
- Watchers: 3
- Forks: 5
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TensorFlow_Lite_SSD_RPi_32-bits
## TensorFlow Lite SSD running at 17 FPS on bare Raspberry Pi 4
A fast C++ implementation of TensorFlow Lite on a bare Raspberry Pi 4.
Once overclocked to 2000 MHz, the app runs an amazing 17 FPS!
Without any hardware accelerator, just you and your Pi.
https://arxiv.org/abs/1611.10012
Training set: COCO
Size: 300x300
Frame rate V1 Lite : 17 FPS (RPi 4 @ 2000 MHz - 32 bits OS)
Frame rate V1 Lite : 24 FPS (RPi 4 @ 1925 MHz - 64 bits OS) see https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_64-bits
Special made for a bare Raspberry Pi see: https://qengineering.eu/install-tensorflow-2-lite-on-raspberry-pi-4.html
To extract and run the network in Code::Blocks
$ mkdir *MyDir*
$ cd *MyDir*
$ wget https://github.com/Qengineering/TensorFlow_Lite_SSD_RPi_32-bits/archive/refs/heads/master.zip
$ unzip -j master.zip
Remove master.zip and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md
Your *MyDir* folder must now look like this:
James.mp4
COCO_labels.txt
detect.tflite
TestTensorFlow_Lite.cpb
MobileNetV1.cpp
Run TestTensorFlow_Lite.cpb with Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).
See the movie at: https://youtu.be/uspw6KztkeQ
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