Uploaded January 2025 | Updated September 2026, 2 weeks ago
Product: T5 E-Paper S3 Pro
MCU: ESP32-S3-WROOM-1
Flash / PSRAM: 16M / 8M
Lora: SX1262
Touch: GT911
Driver IC: ED047TC1 (4.7 inches, 960x540 , 16 gray)
Battery Capacity: 1500mAh
Battery Chip: BQ25896, BQ27220
RTC: PCF85063
[T5 E-Paper S3 Pro]
lilygo.cc/products/t5-e-paper-s3-pro
Product: T5 E-Paper S3 Pro
MCU: ESP32-S3-WROOM-1
Flash / PSRAM: 16M / 8M
Lora: SX1262
Touch: GT911
Driver IC: ED047TC1 (4.7 inches, 960x540 , 16 gray)
Battery Capacity: 1500mAh
Battery Chip: BQ25896, BQ27220
RTC: PCF85063
[T5 E-Paper S3 Pro]
lilygo.cc/products/t5-e-paper-s3-pro
![[AMB82-Mini IoT AI Camera] *On-Device* object detector powered by Tiny Yolo v7! #standalone](https://i.ytimg.com/vi/TGqOUVhQQY8/mqdefault.jpg "[AMB82-Mini IoT AI Camera] *On-Device* object detector powered by Tiny Yolo v7! #standalone
Tiny YOLO version 7 is a simplified version of YOLO version 7, which has a much smaller number of convolution layers than YOLO version 7, which means that Tiny YOLO version 7 does not need to have a large amount of memory and hardware performance but lose some of the detection accuracy.
Lets make one thing clear.
The object detector we are trying to build in a low-power MCU environment is a very simple one. For example, it triggers an action when it detects a person, or acts as a kind of switch to take a certain action. For better performance, it is recommended to run the latest full version of YOLO on a device such as Nano Jetson.
[AMB82-Mini Camera module]
https://amzn.to/4bYOuNk
[Project GitHub]
https://github.com/0015/AMB82-Mini-Board")
