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## TinyML-CAM - Image Recognition System that Runs at 80 FPS in 1 Kb of RAM ## TinyML-CAM - Image Recognition System that Runs at 80 FPS in 1 Kb of RAM
### Image Recognition Demo - ESP32 ### Demo - HOG and Random Forest based Image Recognition on ESP32
ESP32 classifying Raspberry Pi Pico, Portenta H7, Wio Terminal from image frames ESP32 classifying Raspberry Pi Pico, Portenta H7, Wio Terminal from image frames
https://user-images.githubusercontent.com/16524846/179447640-d7f5efa9-3a44-431c-922d-348ee526c782.mp4 https://user-images.githubusercontent.com/16524846/179447640-d7f5efa9-3a44-431c-922d-348ee526c782.mp4
Following can be observed from the video: Following can be observed from the video:
- **Time** For image frames, the digital signal processing (DSP) based features extraction time is ≈ 12 ms, while classification time is ≈ < 20 𝜇𝑠 (1/1000<sup>th</sup> of DSP). - **Time.** For image frames, the digital signal processing (DSP) based features extraction time is ≈ 12 ms, while classification time is ≈ < 20 𝜇𝑠 (1/1000<sup>th</sup> of DSP).
- **FPS** It is 1000/12 ms = 83.3 FPS, which is the time taken by the TinyML-CAM image recognition system to process (DSP) plus classify using a single image frame. Since the ESP32 has a 30 FPS frame rate, just to capture frames, it takes 1000/30 = 33 ms. So the entire frame rate is 1000/(33+12) = 22 FPS. - **FPS.** It is 1000/12 ms = 83.3 FPS, which is the time taken by the TinyML-CAM image recognition system to process (DSP) plus classify using a single image frame. Since the ESP32 has a 30 FPS frame rate, just to capture frames, it takes 1000/30 = 33 ms. So the entire frame rate is 1000/(33+12) = 22 FPS.
- **Accuracy** As expected during Pairplot analysis, Portenta and Pi (features overlapped) are mislabelled quite often, which can be rectified by improving dataset quality. - **Accuracy.** As expected during Pairplot analysis, Portenta and Pi (features overlapped) are mislabelled quite often, which can be rectified by improving dataset quality.
- **Memory** Consumes only 1 kB of RAM - difference between the RAM calculated by Arduino IDE before and after adding the TinyML-CAM image recognition system. - **Memory.** Consumes only 1 kB of RAM - difference between the RAM calculated by Arduino IDE before and after adding the TinyML-CAM image recognition system.
### Code ### Code
- [[ino]-CameraWebServer.ino](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bino%5D-CameraWebServer.ino) - For image dataset collection. After upload to ESP32, it will connect to WiFi network and start an HTTP video streaming server that can be accessed from any web broswer. - [[ino]-CameraWebServer.ino](https://github.com/bharathsudharsan/TinyML-CAM/blob/main/%5Bino%5D-CameraWebServer.ino) - For image dataset collection. After upload to ESP32, it will connect to WiFi network and start an HTTP video streaming server that can be accessed from any web broswer.