Published September 29, 2024 © GPL3+

is-it-green? : Traffic Light Colour Detection ADAS

is-it-green is an AI-powered Traffic Light Colour Detection ADAS providing real-time traffic light status alerts.

IntermediateFull instructions providedOver 1 day424

is-it-green? : Traffic Light Colour Detection ADAS

Things used in this project

Hardware components

Seeed Studio Grove Vision AI v2

Seeed Studio XIAO ESP32S3 Sense

0.96 Inch OLED Display Module

Seeed Studio Grove Base for XIAO

Software apps and online services

Edge Impulse Studio

Arduino IDE

Solidworks

Google Colab

Github

Roboflow

Seeed Studio SenseCraft AI

Hand tools and fabrication machines

3D Printer (generic)

Multitool, Screwdriver

Jumper Wire, Bundle

Hot glue gun (generic)

Story

Overview

This project demonstrates how to build a Traffic Light Colour Detection model, the backbone of an AI-powered ADAS (Advanced Driver Assistance System) for visually impaired colour blind drivers.

The modular ADAS device will be able to attach to the dashboard of any vehicle and assist the driver in determining the status of the traffic light. It is also helpful for people with normal vision as it alerts the driver when the colour changes.

It is currently prototyped on the Grove Vision AI v2 with a Raspberry Pi camera, connected to an XIAO ESP32S3, and OLED Display.

Build2gether 2.0 — Inclusive Innovation Challenge

VISUAL IMPAIRMENTS 👨‍🦯👩‍🦯

Track 1:Adaptation for OUTDOOR Activities for People with Visual Impairments

This project is a submission to the Build2Gether 2.0 Inclusive Innovation Challenge as an adaptation for Outdoor Activities for people with visual impairments. My vision without spectacles is poor; often, like many people, I am not alerted when the traffic light changes. So, I can only imagine how hard such a simple task is for my colour-blind friends. Hence, this is my attempt at tackling this problem.

Problem Identification

The identified problem revolves around the difficulty colourblind individuals face in discerning traffic lights, particularly the green signal, due to their colour perception limitations. This challenge stems from the inherent design of traffic lights, which primarily rely on colour differentiation to convey meaning. Colourblind individuals often memorize the vertical arrangement of traffic lights (red on top, yellow in the middle, and green at the bottom) to navigate intersections. However, the distinction becomes hard when lights are positioned horizontally or encountered alongside hazy white street lights. This problem is exacerbated at night when visibility is reduced, and colour perception is compromised. Approximately 8% of men, being colourblind, encounter significant difficulty distinguishing between the green traffic light and adjacent sources of hazy white light. As a result, there's a risk of misinterpretation, potentially leading to dangerous situations on the road. Thus, the underlying cause of the problem lies in the reliance on colour-coded signals for conveying critical information, which poses a significant challenge for individuals with colour vision deficiencies, particularly in low-light conditions.People with total colour blindness or monochromacy are not licensed drivers in most countries. In India, and most countries mild to medium partial colourblind people can apply for a driving license. Red-green colour blindness is the most common type, mostly affecting males. There are four types of red-green colour blindness, including protanopia (red-blind), deuteranopia (green-blind), protanomaly (red-weak), and deuteranomaly (green-weak).

Hence they will have different levels of confidence and have to rely on external factors to confirm the colour of the traffic light. This problem is exacerbated when the colour-blind driver is in the front of the traffic, any delay can result in a reduction in traffic efficiency and anxiety-inducing horns from the vehicles behind. Similarly, even a driver with normal visual acuity can miss the change in traffic light.

Learnings

Do colourblind people see traffic lights differently? - Antonio the Optometrist

Designing a Colorblind-Friendly Traffic Light - Chromaphobe

Proposed Solution

To address the specific challenge of colourblind individuals distinguishing traffic lights, the proposed solution entails the development of a Traffic Light Colour Detection ADAS. This system would detect the status of traffic lights (red, green, or yellow). Once a traffic light is detected, the system continuously monitors its colour changes in real-time. The system provides audio and text feedback, clearly indicating the current status of the traffic light, for colourblind drivers, This feedback empowers colourblind drivers to navigate intersections confidently and safely, without relying solely on colour recognition. By leveraging object detection models and real-time monitoring, the smart dashboard camera ensures accurate and timely notifications, enhancing the driving experience for colourblind individuals and promoting road safety.

Key features:

How It Works

Working of Traffic Light COlour Detection ADAS without enclosure

Working of Traffic Light COlour Detection ADAS with enclosure

Components

The device consists of the following electronics enclosed within a simple enclosure, which can be placed on the dashboard of any vehicle to help determine the traffic signal.

Seeed Studio Grove Vision AI module v2 with RPi OV5647-62 FOV Camera Module

Grove Vision AI module v2 is an MCU-based vision AI module powered by Arm Cortex-M55 & Ethos-U55 that will run our machine learning model to detect and classify traffic lights. To get started use this documentation: https://wiki.seeedstudio.com/grove_vision_ai_v2/

Seeed Studio XIAO ESP32S3

We will be using Seeed Studio XIAO ESP32S3 as our microcontroller, which will act as a master of the Grove Vision AI v2 and control the OLED display. Any XIAO series board can be used.

Grove Base for XIAO

The Grove Base for XIAO will help us by having built-in battery management, a power on/off switch, and two I2C ports along with other ports.

0.96 Inch I2C OLED Display

An OLED display will be used to display the status of the traffic light and alert the driver. We are using a small and generic 0.96-inch OLED display with I2C pins.

WLY853837 1250 mAh 3.7V single cell Rechargeable LiPo Battery

A single-cell 3.7 V battery will power the system, I had a 1250 mAh battery so I am using it. They are soldered onto the respective battery pad on the Grove Base Board.The components then need to be electrically isolated from each other by using double-sided foam tape or hot glue. Don't use hot glue on the OLED display as it is sensitive to heat. The CAD model called "Component Placement" needs to be followed to be able to fit the final components into the enclosure.

Prototype Circuit

Traffic Light Detection ADAS components without enclosure

Build Instructions

Below are instructions on how to prototype, assemble, and test the Traffic Light Colour Detection ADAS. These instructions are based on official documentation on Deploying Models from Datasets to Grove Vision AI V2.The training dataset used is from Roboflow:Original Colab Notebook: https://colab.research.google.com/github/seeed-studio/sscma-model-zoo/blob/main/notebooks/en/COCO_Detection_Swift-YOLO_320.ipynb

Other models available at: https://wiki.seeedstudio.com/ModelAssistant_Introduce_Quick_Start/#model-training

Colab Notebook used for training Traffic light colour detection ML model: https://colab.research.google.com/drive/1i34uz9xc8WmP76DLcjtQczmPtsyuMAFz

Make a copy and start editing or use the model file int8_vela.tflite file attached in this guide. Upload the model file to the SenseCraft Model Assistant dashboard to deploy the model for prototyping.

Prototype Testing

Prototpye Testing 1 - Running inference on SenseCraft AI

Prototpye Testing 2 - Running inference on SenseCraft AI

Next, we need to get the recognition results to the XIAO board to be able to control the display. Initially, the XIAO can be directly connected to the Grove Vision AI v2. Plug in the XIAO to your computer, set the port and upload the inference example.https://wiki.seeedstudio.com/grove_vision_ai_v2_software_support/#demo-1-use-xiao-to-get-recognition-results

Use the OLED Test Code to test the display. Edit the font if needed, by replacing the font name with the available fonts given in https://github.com/olikraus/u8g2/wiki/fntlist8x8

Finally, upload the file " TLCD-ADAS" to the XIAO. Edit the definition of red, green and yellow based on the trained model.The complete project code and 3D print files are found in this project's attachments.

Future Development

A buzzer can be added for auditory feedback. It can easily be connected to the Grove Base for XIAO and be controlled by XIAO. I did not have the component at hand. Also, a better camera with a higher resolution can be connected to Vision AI via the CSI interface.

Custom parts and enclosures

Sketchfab still processing.

Schematics

Code

#include <Seeed_Arduino_SSCMA.h>
#include <Arduino.h>
#include <U8x8lib.h>
#include <Wire.h>

#define GREEN 0
#define RED 1
#define YELLOW 2

SSCMA AI;

U8X8_SSD1306_128X64_NONAME_HW_I2C u8x8(/* clock=*/ SCL, /* data=*/ SDA, /* reset=*/ U8X8_PIN_NONE);   // OLEDs without Reset of the Display

void setup(void) {
  AI.begin();
  Serial.begin(9600);
    
  u8x8.begin();
  u8x8.setFlipMode(1);   // set number from 1 to 3, the screen word will rotary 180

  u8x8.clear();
  u8x8.clear();
  u8x8.setFont(u8x8_font_7x14_1x2_r);
  u8x8.setCursor(2, 0);
  u8x8.print("Traffic Light");
  u8x8.setCursor(0, 2);
  u8x8.print("Colour Detection");
  u8x8.setCursor(5, 4);
  u8x8.print("ADAS");
  delay(3000);
  u8x8.clear();
  u8x8.setCursor(3, 3);
  u8x8.print("By jeromtom");
  delay(3000);

  u8x8.clear();
  u8x8.setFont(u8x8_font_8x13_1x2_f);
  u8x8.setCursor(3, 3);
  u8x8.print("Detecting...");
  delay(2000);
}

void loop(void) {
  u8x8.clear();
  
  if (!AI.invoke())
    {
        Serial.println("invoke success");
        Serial.print("perf: prepocess=");
        Serial.print(AI.perf().prepocess);
        Serial.print(", inference=");
        Serial.print(AI.perf().inference);
        Serial.print(", postpocess=");
        Serial.println(AI.perf().postprocess);

        for (int i = 0; i < AI.boxes().size(); i++)
        {
            Serial.print("Box[");
            Serial.print(i);
            Serial.print("] target=");
            Serial.print(AI.boxes()[i].target);
            Serial.print(", score=");
            Serial.print(AI.boxes()[i].score);
            Serial.print(", x=");
            Serial.print(AI.boxes()[i].x);
            Serial.print(", y=");
            Serial.print(AI.boxes()[i].y);
            Serial.print(", w=");
            Serial.print(AI.boxes()[i].w);
            Serial.print(", h=");
            Serial.println(AI.boxes()[i].h);

            if (AI.boxes()[i].target == GREEN && AI.boxes()[i].score >= 65){
                u8x8.clear();
                u8x8.setFont(u8x8_font_inr21_2x4_f);
                u8x8.setCursor(3, 2);
                u8x8.print("Green");
                delay(500);
            }
            else if (AI.boxes()[i].target == RED && AI.boxes()[i].score >= 65){
                u8x8.clear();
                u8x8.setFont(u8x8_font_inr21_2x4_f);
                u8x8.setCursor(5, 2);
                u8x8.print("Red");
                delay(500);
            }
            else if (AI.boxes()[i].target == YELLOW && AI.boxes()[i].score >= 65){
                u8x8.clear();
                u8x8.setFont(u8x8_font_inr21_2x4_f);
                u8x8.setCursor(2, 2);
                u8x8.print("Yellow");
                delay(500);
            }

            else {
                u8x8.clear();
                u8x8.setFont(u8x8_font_8x13_1x2_f);
                u8x8.setCursor(3, 3);
                u8x8.print("Detecting...");
                delay(300);
            }
        }
        for (int i = 0; i < AI.classes().size(); i++)
        {
            Serial.print("Class[");
            Serial.print(i);
            Serial.print("] target=");
            Serial.print(AI.classes()[i].target);
            Serial.print(", score=");
            Serial.println(AI.classes()[i].score);
            
        }
        for (int i = 0; i < AI.points().size(); i++)        
        {
            Serial.print("Point[");
            Serial.print(i);
            Serial.print("] target=");
            Serial.print(AI.points()[i].target);
            Serial.print(", score=");
            Serial.print(AI.points()[i].score);
            Serial.print(", x=");
            Serial.print(AI.points()[i].x);
            Serial.print(", y=");
            Serial.println(AI.points()[i].y);
        }
    }
    else{
        u8x8.clear();
        u8x8.setFont(u8x8_font_chroma48medium8_r);
        u8x8.setCursor(0, 0);
        u8x8.print("Invoke Fail"); 
    }
}

#include <Arduino.h>
#include <U8x8lib.h>
#include <Wire.h>

U8X8_SSD1306_128X64_NONAME_HW_I2C u8x8(/* clock=*/ SCL, /* data=*/ SDA, /* reset=*/ U8X8_PIN_NONE);   // OLEDs without Reset of the Display

void setup(void) {
  u8x8.begin();
  u8x8.setFlipMode(1);   // set number from 1 to 3, the screen word will rotary 180

  u8x8.clear();
  u8x8.setFont(u8x8_font_7x14_1x2_r);
  u8x8.setCursor(2, 0);
  u8x8.print("Traffic Light");
  u8x8.setCursor(0, 2);
  u8x8.print("Colour Detection");
  u8x8.setCursor(5, 4);
  u8x8.print("ADAS");
  delay(2000);
  u8x8.clear();
  u8x8.setCursor(3, 3);
  u8x8.print("By jeromtom");
  delay(3000);
}

void loop(void) {

  u8x8.clear();
  u8x8.setFont(u8x8_font_8x13_1x2_f);
  u8x8.setCursor(3, 3);
  u8x8.print("Detecting...");
  delay(5000);
  
  u8x8.clear();
  u8x8.setFont(u8x8_font_inr21_2x4_f);
  u8x8.setCursor(3, 2);
  u8x8.print("Green");
  delay(2000);
  
  u8x8.clear();
  u8x8.setFont(u8x8_font_inr21_2x4_f);
  u8x8.setCursor(5, 2);
  u8x8.print("Red");
  delay(2000);
  
  u8x8.clear();
  u8x8.setFont(u8x8_font_inr21_2x4_f);
  u8x8.setCursor(2, 2);
  u8x8.print("Yellow");
  delay(2000);
  
}

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UnWYFoFR1Wa"
      },
      "source": [
        "<div align=\"center\">\n",
        "  <h1>Traffic Light Colour Detection- By jeromtom</h1>\n",
        "  <a href=\"https://www.hackster.io/contests/buildtogether2\" target=\"_blank\"><img width=\"80%\" src=\"https://hackster.imgix.net/uploads/attachments/1696069/_kJYAWaTROz.blob?auto=format&w=1600&h=400&fit=min&dpr=1\"></a>\n",
        "  <p>Detects the colour of the traffic light using object detection to assist the visually impaired colour blind driver </p>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SLupbBVsR1Wd"
      },
      "source": [
        "# COCO Detection - Swift-YOLO\n",
        "\n",
        "Basic COCO detection of a person: [![Basic COCO detection of a person](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/seeed-studio/sscma-model-zoo/blob/main/notebooks/en/COCO_Detection_Swift-YOLO_320.ipynb)\n",
        "\n",
        "**Version:** 1.0.0\n",
        "\n",
        "**Category:** Object Detection\n",
        "\n",
        "**Algorithm:** [Swift-YOLO](configs/yolov5/swift_yolo_shuff_1xb16_300e_coco.py)\n",
        "\n",
        "**Pre-Training Dataset:** [COCO2017](https://public.roboflow.com/object-detection/microsoft-coco-subset)\n",
        "\n",
        "**Class:** `Traffic Red` `Traffic Green` `Traffic Yellow`\n",
        "\n",
        "\n",
        "The model is a Swift-YOLO model trained on the COCO2017 dataset.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BoCR3Yz6R1Wd"
      },
      "source": [
        "## Prerequisites\n",
        "### Setup SSCMA\n",
        "Clone the [repository](https://github.com/Seeed-Studio/ModelAssistant) and install the dependencies."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "07GtLaibR1Wd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7514832c-9904-4ff7-e647-3bac094a4ca1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'ethos-u-vela'...\n",
            "remote: Counting objects: 180, done\u001b[K\n",
            "remote: Finding sources: 100% (9234/9234)\u001b[K\n",
            "remote: Total 9234 (delta 5745), reused 8027 (delta 5745)\u001b[K\n",
            "Receiving objects: 100% (9234/9234), 5.15 MiB | 3.33 MiB/s, done.\n",
            "Resolving deltas: 100% (5745/5745), done.\n",
            "/content/ethos-u-vela\n",
            "Processing /content/ethos-u-vela\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: flatbuffers==24.3.25 in /usr/local/lib/python3.10/dist-packages (from ethos-u-vela==4.0.0rc2) (24.3.25)\n",
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            "Building wheels for collected packages: ethos-u-vela\n",
            "  Building wheel for ethos-u-vela (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ethos-u-vela: filename=ethos_u_vela-4.0.0rc2-cp310-cp310-linux_x86_64.whl size=1556692 sha256=40e548dfa3a04cada6e1765594a73b77e1ad306fa229261c782d24f7b7d06b6c\n",
            "  Stored in directory: /root/.cache/pip/wheels/59/bb/a4/8cbf7845835c564e303787c5b86a3233501a9126e0070abb2d\n",
            "Successfully built ethos-u-vela\n",
            "Installing collected packages: ethos-u-vela\n",
            "Successfully installed ethos-u-vela-4.0.0rc2\n",
            "/content\n",
            "Cloning into 'ModelAssistant'...\n",
            "remote: Enumerating objects: 14554, done.\u001b[K\n",
            "remote: Counting objects: 100% (2513/2513), done.\u001b[K\n",
            "remote: Compressing objects: 100% (948/948), done.\u001b[K\n",
            "remote: Total 14554 (delta 1754), reused 1878 (delta 1550), pack-reused 12041 (from 1)\u001b[K\n",
            "Receiving objects: 100% (14554/14554), 24.95 MiB | 9.59 MiB/s, done.\n",
            "Resolving deltas: 100% (8518/8518), done.\n",
            "/content/ModelAssistant\n",
            "Checking if CUDA available... \u001b[032mOK\u001b[m\n",
            "Collecting torch==2.0.0\n",
            "  Downloading torch-2.0.0-cp310-cp310-manylinux1_x86_64.whl.metadata (24 kB)\n",
            "Collecting torchvision==0.15.1\n",
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            "Collecting torchaudio==2.0.1\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.0.0) (3.15.4)\n",
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            "\u001b[?25hInstalling collected packages: lit, nvidia-nvtx-cu11, nvidia-nccl-cu11, nvidia-cusparse-cu11, nvidia-curand-cu11, nvidia-cufft-cu11, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cuda-cupti-cu11, nvidia-cublas-cu11, nvidia-cusolver-cu11, nvidia-cudnn-cu11, triton, torch, torchvision, torchaudio\n",
            "  Attempting uninstall: torch\n",
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            "Successfully installed lit-18.1.8 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-cupti-cu11-11.7.101 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 nvidia-cufft-cu11-10.9.0.58 nvidia-curand-cu11-10.2.10.91 nvidia-cusolver-cu11-11.4.0.1 nvidia-cusparse-cu11-11.7.4.91 nvidia-nccl-cu11-2.14.3 nvidia-nvtx-cu11-11.7.91 torch-2.0.0 torchaudio-2.0.1 torchvision-0.15.1 triton-2.0.0\n",
            "Installing base deps... Collecting TinyNeuralNetwork@ https://files.seeedstudio.com/sscma/library/TinyNeuralNetwork-0.1.1-py3-none-any.whl (from -r requirements/export.txt (line 2))\n",
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            "Building wheels for collected packages: cbor, oss2, aliyun-python-sdk-core, crcmod\n",
            "  Building wheel for cbor (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cbor: filename=cbor-1.0.0-cp310-cp310-linux_x86_64.whl size=53433 sha256=1807392ad68cbbf68a3cfdf1e95861b0834c15ecd47425c1fba831bfd8e6dea6\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/df/c9/b39e40eccaf76dbd218556639a6dc81562226f4c6a64902c85\n",
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            "  Stored in directory: /root/.cache/pip/wheels/87/04/7b/7e61b8157fdf211c5131375240d0d86ca82e2a88ead9672c88\n",
            "  Building wheel for aliyun-python-sdk-core (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for aliyun-python-sdk-core: filename=aliyun_python_sdk_core-2.15.2-py3-none-any.whl size=535313 sha256=91c5e397c7e06dae915c263dda273e524f8f04ee3efd768f77f5751fb068f6b8\n",
            "  Stored in directory: /root/.cache/pip/wheels/96/64/cd/d4ec2561e5a29e5272b8e776afb164d339b2fd0d0b616a5bcf\n",
            "  Building wheel for crcmod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for crcmod: filename=crcmod-1.7-cp310-cp310-linux_x86_64.whl size=31403 sha256=c8dcf0670e035775e091fe306520ff115bbc607016a1ff11ab399bc0dda6a1c3\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/4c/07/72215c529bd59d67e3dac29711d7aba1b692f543c808ba9e86\n",
            "Successfully built cbor oss2 aliyun-python-sdk-core crcmod\n",
            "Installing collected packages: texttable, pytz, pnnx, libusb1, distlib, crcmod, cbor, urllib3, tqdm, setuptools, ruff, ruamel.yaml.clib, pycryptodome, protobuf, portalocker, pathspec, ordered-set, nodeenv, mypy-extensions, jmespath, isort, igraph, identify, humanfriendly, filelock, colorama, cfgv, virtualenv, ruamel.yaml, rich, requests, onnx, model-index, coloredlogs, black, TinyNeuralNetwork, qudida, pre-commit, onnxsim, onnxruntime, onnxmltools, ncnn, aliyun-python-sdk-core, aliyun-python-sdk-kms, albumentations, oss2, openxlab, opendatalab, openmim, pyvww\n",
            "  Attempting uninstall: pytz\n",
            "    Found existing installation: pytz 2024.1\n",
            "    Uninstalling pytz-2024.1:\n",
            "      Successfully uninstalled pytz-2024.1\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 2.0.7\n",
            "    Uninstalling urllib3-2.0.7:\n",
            "      Successfully uninstalled urllib3-2.0.7\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.66.5\n",
            "    Uninstalling tqdm-4.66.5:\n",
            "      Successfully uninstalled tqdm-4.66.5\n",
            "  Attempting uninstall: setuptools\n",
            "    Found existing installation: setuptools 71.0.4\n",
            "    Uninstalling setuptools-71.0.4:\n",
            "      Successfully uninstalled setuptools-71.0.4\n",
            "  Attempting uninstall: protobuf\n",
            "    Found existing installation: protobuf 3.20.3\n",
            "    Uninstalling protobuf-3.20.3:\n",
            "      Successfully uninstalled protobuf-3.20.3\n",
            "  Attempting uninstall: filelock\n",
            "    Found existing installation: filelock 3.15.4\n",
            "    Uninstalling filelock-3.15.4:\n",
            "      Successfully uninstalled filelock-3.15.4\n",
            "  Attempting uninstall: rich\n",
            "    Found existing installation: rich 13.8.0\n",
            "    Uninstalling rich-13.8.0:\n",
            "      Successfully uninstalled rich-13.8.0\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.32.3\n",
            "    Uninstalling requests-2.32.3:\n",
            "      Successfully uninstalled requests-2.32.3\n",
            "  Attempting uninstall: albumentations\n",
            "    Found existing installation: albumentations 1.4.14\n",
            "    Uninstalling albumentations-1.4.14:\n",
            "      Successfully uninstalled albumentations-1.4.14\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "ipython 7.34.0 requires jedi>=0.16, which is not installed.\n",
            "google-colab 1.0.0 requires requests==2.32.3, but you have requests 2.28.2 which is incompatible.\n",
            "tensorflow-metadata 1.15.0 requires protobuf<4.21,>=3.20.3; python_version < \"3.11\", but you have protobuf 4.25.4 which is incompatible.\n",
            "yfinance 0.2.43 requires requests>=2.31, but you have requests 2.28.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed TinyNeuralNetwork-0.1.1 albumentations-1.3.1 aliyun-python-sdk-core-2.15.2 aliyun-python-sdk-kms-2.16.5 black-24.8.0 cbor-1.0.0 cfgv-3.4.0 colorama-0.4.6 coloredlogs-15.0.1 crcmod-1.7 distlib-0.3.8 filelock-3.14.0 humanfriendly-10.0 identify-2.6.0 igraph-0.11.6 isort-5.13.2 jmespath-0.10.0 libusb1-3.1.0 model-index-0.1.11 mypy-extensions-1.0.0 ncnn-1.0.20240820 nodeenv-1.9.1 onnx-1.16.2 onnxmltools-1.12.0 onnxruntime-1.19.0 onnxsim-0.4.36 opendatalab-0.0.10 openmim-0.3.9 openxlab-0.1.1 ordered-set-4.1.0 oss2-2.17.0 pathspec-0.12.1 pnnx-0.0.4 portalocker-2.10.1 pre-commit-3.8.0 protobuf-4.25.4 pycryptodome-3.20.0 pytz-2023.4 pyvww-0.1.1 qudida-0.0.4 requests-2.28.2 rich-13.4.2 ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8 ruff-0.6.3 setuptools-60.2.0 texttable-1.7.0 tqdm-4.65.2 urllib3-1.26.20 virtualenv-20.26.3\n",
            "Installing OpenMIM deps... \n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu117/torch2.0.0/index.html\n",
            "Collecting mmcls>=1.0.0.rc6 (from -r requirements/mmlab.txt (line 2))\n",
            "  Downloading mmcls-1.0.0rc6-py2.py3-none-any.whl.metadata (16 kB)\n",
            "Collecting mmcv-full<=2.1.0 (from -r requirements/mmlab.txt (line 3))\n",
            "  Downloading https://download.openmmlab.com/mmcv/dist/cu117/torch2.0.0/mmcv_full-1.7.2-cp310-cp310-manylinux1_x86_64.whl (70.3 MB)\n",
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            "\u001b[?25hCollecting mmdet<3.1.0,>=3.0.0 (from -r requirements/mmlab.txt (line 4))\n",
            "  Downloading mmdet-3.0.0-py3-none-any.whl.metadata (26 kB)\n",
            "Collecting mmengine>=0.8.2 (from -r requirements/mmlab.txt (line 5))\n",
            "  Downloading mmengine-0.10.4-py3-none-any.whl.metadata (20 kB)\n",
            "Ignoring mmcv: markers 'extra == \"mim\"' don't match your environment\n",
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            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mmcls>=1.0.0.rc6->-r requirements/mmlab.txt (line 2)) (3.7.1)\n",
            "Collecting modelindex (from mmcls>=1.0.0.rc6->-r requirements/mmlab.txt (line 2))\n",
            "  Downloading modelindex-0.0.2-py3-none-any.whl.metadata (756 bytes)\n",
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            "Collecting addict (from mmcv-full<=2.1.0->-r requirements/mmlab.txt (line 3))\n",
            "  Downloading addict-2.4.0-py3-none-any.whl.metadata (1.0 kB)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from mmcv-full<=2.1.0->-r requirements/mmlab.txt (line 3)) (9.4.0)\n",
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            "  Downloading yapf-0.40.2-py3-none-any.whl.metadata (45 kB)\n",
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            "\u001b[?25hRequirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.10/dist-packages (from mmcv-full<=2.1.0->-r requirements/mmlab.txt (line 3)) (4.10.0.84)\n",
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            "Ignoring mmengine: markers 'extra == \"mim\"' don't match your environment\n",
            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.10/dist-packages (from mmdet<3.1.0,>=3.0.0->-r requirements/mmlab.txt (line 4)) (2.0.8)\n",
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            "  Downloading terminaltables-3.1.10-py2.py3-none-any.whl.metadata (3.5 kB)\n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmcls>=1.0.0.rc6->-r requirements/mmlab.txt (line 2)) (1.4.5)\n",
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            "Downloading mmcls-1.0.0rc6-py2.py3-none-any.whl (906 kB)\n",
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            "\u001b[?25hDownloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n",
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            "Downloading terminaltables-3.1.10-py2.py3-none-any.whl (15 kB)\n",
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            "\u001b[?25hInstalling collected packages: addict, terminaltables, yapf, modelindex, mmengine, mmcv-full, mmcls, mmdet\n",
            "Successfully installed addict-2.4.0 mmcls-1.0.0rc6 mmcv-full-1.7.2 mmdet-3.0.0 mmengine-0.10.4 modelindex-0.0.2 terminaltables-3.1.10 yapf-0.40.2\n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu117/torch2.0.0/index.html\n",
            "Collecting mmcv==2.0.0\n",
            "  Downloading https://download.openmmlab.com/mmcv/dist/cu117/torch2.0.0/mmcv-2.0.0-cp310-cp310-manylinux1_x86_64.whl (74.3 MB)\n",
            "\u001b[2K     \u001b[90m\u001b[0m \u001b[32m74.3/74.3 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: addict in /usr/local/lib/python3.10/dist-packages (from mmcv==2.0.0) (2.4.0)\n",
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            "Installing collected packages: mmcv\n",
            "Successfully installed mmcv-2.0.0\n",
            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu117/torch2.0.0/index.html\n",
            "Obtaining file:///content/ModelAssistant\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Checking if build backend supports build_editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build editable ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing editable metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting TinyNeuralNetwork@ https://files.seeedstudio.com/sscma/library/TinyNeuralNetwork-0.1.1-py3-none-any.whl (from sscma==2.0.0rc3)\n",
            "  Using cached https://files.seeedstudio.com/sscma/library/TinyNeuralNetwork-0.1.1-py3-none-any.whl (416 kB)\n",
            "Requirement already satisfied: torch<=2.0.1 in /usr/local/lib/python3.10/dist-packages (from sscma==2.0.0rc3) (2.0.0)\n",
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            "Ignoring mmcv: markers 'extra == \"mim\"' don't match your environment\n",
            "Ignoring mmengine: markers 'extra == \"mim\"' don't match your environment\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from mmcls>=1.0.0.rc6->sscma==2.0.0rc3) (3.7.1)\n",
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            "Ignoring mmcv: markers 'extra == \"mim\"' don't match your environment\n",
            "Ignoring mmengine: markers 'extra == \"mim\"' don't match your environment\n",
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            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard>=2.12.3->sscma==2.0.0rc3) (3.0.4)\n",
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            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.12.0->sscma==2.0.0rc3) (3.3.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.12.0->sscma==2.0.0rc3) (1.16.0)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.12.0->sscma==2.0.0rc3) (3.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow>=2.12.0->sscma==2.0.0rc3) (0.37.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (3.14.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (3.1.4)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu11==11.7.101 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (11.7.101)\n",
            "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (8.5.0.96)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (11.10.3.66)\n",
            "Requirement already satisfied: nvidia-cufft-cu11==10.9.0.58 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (10.9.0.58)\n",
            "Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (10.2.10.91)\n",
            "Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (11.4.0.1)\n",
            "Requirement already satisfied: nvidia-cusparse-cu11==11.7.4.91 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (11.7.4.91)\n",
            "Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (2.14.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (11.7.91)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch<=2.0.1->sscma==2.0.0rc3) (2.0.0)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch<=2.0.1->sscma==2.0.0rc3) (0.44.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch<=2.0.1->sscma==2.0.0rc3) (3.30.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch<=2.0.1->sscma==2.0.0rc3) (18.1.8)\n",
            "Requirement already satisfied: lxml>=4.5.2 in /usr/local/lib/python3.10/dist-packages (from ethos-u-vela->sscma==2.0.0rc3) (4.9.4)\n",
            "Requirement already satisfied: ruamel.yaml>=0.16.12 in /usr/local/lib/python3.10/dist-packages (from TinyNeuralNetwork@ https://files.seeedstudio.com/sscma/library/TinyNeuralNetwork-0.1.1-py3-none-any.whl->sscma==2.0.0rc3) (0.18.6)\n",
            "Requirement already satisfied: igraph>=0.9 in /usr/local/lib/python3.10/dist-packages (from TinyNeuralNetwork@ https://files.seeedstudio.com/sscma/library/TinyNeuralNetwork-0.1.1-py3-none-any.whl->sscma==2.0.0rc3) (0.11.6)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0->soundfile>=0.12.1->sscma==2.0.0rc3) (2.22)\n",
            "Requirement already satisfied: texttable>=1.6.2 in /usr/local/lib/python3.10/dist-packages (from igraph>=0.9->TinyNeuralNetwork@ https://files.seeedstudio.com/sscma/library/TinyNeuralNetwork-0.1.1-py3-none-any.whl->sscma==2.0.0rc3) (1.7.0)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow>=2.12.0->sscma==2.0.0rc3) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow>=2.12.0->sscma==2.0.0rc3) (0.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->ncnn>=1.0.20230517->sscma==2.0.0rc3) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->ncnn>=1.0.20230517->sscma==2.0.0rc3) (3.8)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->ncnn>=1.0.20230517->sscma==2.0.0rc3) (1.26.20)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->ncnn>=1.0.20230517->sscma==2.0.0rc3) (2024.7.4)\n",
            "Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /usr/local/lib/python3.10/dist-packages (from ruamel.yaml>=0.16.12->TinyNeuralNetwork@ https://files.seeedstudio.com/sscma/library/TinyNeuralNetwork-0.1.1-py3-none-any.whl->sscma==2.0.0rc3) (0.2.8)\n",
            "Requirement already satisfied: distlib<1,>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from virtualenv>=20.10.0->pre-commit>=3.3.3->sscma==2.0.0rc3) (0.3.8)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard>=2.12.3->sscma==2.0.0rc3) (2.1.5)\n",
            "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.10/dist-packages (from coloredlogs->onnxruntime>=1.15.1->sscma==2.0.0rc3) (10.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmcls>=1.0.0.rc6->sscma==2.0.0rc3) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmcls>=1.0.0.rc6->sscma==2.0.0rc3) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmcls>=1.0.0.rc6->sscma==2.0.0rc3) (4.53.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmcls>=1.0.0.rc6->sscma==2.0.0rc3) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->mmcls>=1.0.0.rc6->sscma==2.0.0rc3) (3.1.4)\n",
            "Requirement already satisfied: ordered-set in /usr/local/lib/python3.10/dist-packages (from model-index->openmim>=0.3.7->sscma==2.0.0rc3) (4.1.0)\n",
            "Requirement already satisfied: pycryptodome in /usr/local/lib/python3.10/dist-packages (from opendatalab->openmim>=0.3.7->sscma==2.0.0rc3) (3.20.0)\n",
            "Requirement already satisfied: openxlab in /usr/local/lib/python3.10/dist-packages (from opendatalab->openmim>=0.3.7->sscma==2.0.0rc3) (0.1.1)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->mmcls>=1.0.0.rc6->sscma==2.0.0rc3) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->mmcls>=1.0.0.rc6->sscma==2.0.0rc3) (2.16.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime>=1.15.1->sscma==2.0.0rc3) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata>=6.6.0 in /usr/local/lib/python3.10/dist-packages (from yapf->mmcv-full<=2.1.0->sscma==2.0.0rc3) (8.4.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.10/dist-packages (from importlib-metadata>=6.6.0->yapf->mmcv-full<=2.1.0->sscma==2.0.0rc3) (3.20.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->mmcls>=1.0.0.rc6->sscma==2.0.0rc3) (0.1.2)\n",
            "Requirement already satisfied: oss2~=2.17.0 in /usr/local/lib/python3.10/dist-packages (from openxlab->opendatalab->openmim>=0.3.7->sscma==2.0.0rc3) (2.17.0)\n",
            "Requirement already satisfied: crcmod>=1.7 in /usr/local/lib/python3.10/dist-packages (from oss2~=2.17.0->openxlab->opendatalab->openmim>=0.3.7->sscma==2.0.0rc3) (1.7)\n",
            "Requirement already satisfied: aliyun-python-sdk-kms>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from oss2~=2.17.0->openxlab->opendatalab->openmim>=0.3.7->sscma==2.0.0rc3) (2.16.5)\n",
            "Requirement already satisfied: aliyun-python-sdk-core>=2.13.12 in /usr/local/lib/python3.10/dist-packages (from oss2~=2.17.0->openxlab->opendatalab->openmim>=0.3.7->sscma==2.0.0rc3) (2.15.2)\n",
            "Requirement already satisfied: jmespath<1.0.0,>=0.9.3 in /usr/local/lib/python3.10/dist-packages (from aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim>=0.3.7->sscma==2.0.0rc3) (0.10.0)\n",
            "Requirement already satisfied: cryptography>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from aliyun-python-sdk-core>=2.13.12->oss2~=2.17.0->openxlab->opendatalab->openmim>=0.3.7->sscma==2.0.0rc3) (43.0.0)\n",
            "Building wheels for collected packages: sscma\n",
            "  Building editable for sscma (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sscma: filename=sscma-2.0.0rc3-0.editable-py3-none-any.whl size=11134 sha256=33736da37fb119382dab9cec6bf84dd855bea9286a7f787786185426a820004a\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-skuj1har/wheels/90/1c/ba/0dcfb496beef1b933cf590042cc252e1a365a514ee48989a82\n",
            "Successfully built sscma\n",
            "Installing collected packages: sscma\n",
            "Successfully installed sscma-2.0.0rc3\n",
            "Finished setup... \u001b[032mOK\u001b[m\n"
          ]
        }
      ],
      "source": [
        "# Ethos-U-Vela need to be installed this way, or SSCMA does not work anymore...\n",
        "!git clone https://review.mlplatform.org/ml/ethos-u/ethos-u-vela.git\n",
        "%cd ethos-u-vela\n",
        "!pip install .\n",
        "%cd ..\n",
        "\n",
        "!git clone https://github.com/Seeed-Studio/ModelAssistant.git   #clone the repo\n",
        "%cd ModelAssistant\n",
        "!. ./scripts/setup_colab.sh"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHV2_9cPR1We"
      },
      "source": [
        "### Download the pretrain model weights file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DTndkgczR1We",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74d30cdf-5939-4ac1-c134-7ef8e3d0e3a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-09-03 22:14:21--  https://files.seeedstudio.com/sscma/model_zoo/detection/coco/swift_yolo_shuffle_coco_320_float32_sha1_a5927bd6a6c6569d27edb98da946a8e75a8d816f.pth\n",
            "Resolving files.seeedstudio.com (files.seeedstudio.com)... 108.157.254.78, 108.157.254.103, 108.157.254.102, ...\n",
            "Connecting to files.seeedstudio.com (files.seeedstudio.com)|108.157.254.78|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 77669861 (74M) [application/octet-stream]\n",
            "Saving to: Traffic_Light_Colour_Detection/pretrain.pth\n",
            "\n",
            "Traffic_Light_Colou 100%[===================>]  74.07M  22.7MB/s    in 4.1s    \n",
            "\n",
            "2024-09-03 22:14:26 (17.9 MB/s) - Traffic_Light_Colour_Detection/pretrain.pth saved [77669861/77669861]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "%mkdir -p Traffic_Light_Colour_Detection\n",
        "!wget -c https://files.seeedstudio.com/sscma/model_zoo/detection/coco/swift_yolo_shuffle_coco_320_float32_sha1_a5927bd6a6c6569d27edb98da946a8e75a8d816f.pth -O Traffic_Light_Colour_Detection/pretrain.pth"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nyGtWBBR1Wf"
      },
      "source": [
        "### Download the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tOwyG4TOR1Wf"
      },
      "outputs": [],
      "source": [
        "# %mkdir -p COCO_Detection_Swift-YOLO_320/dataset\n",
        "# !wget -c https://public.roboflow.com/ds/saZ2K9LYJf?key=FKkHrP6bjs -O COCO_Detection_Swift-YOLO_320/dataset.zip\n",
        "# !unzip -q COCO_Detection_Swift-YOLO_320/dataset.zip -d COCO_Detection_Swift-YOLO_320/dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%mkdir -p Traffic_Light_Colour_Detection/dataset\n",
        "!wget -c https://universe.roboflow.com/ds/DMu9Ql2JOA?key=2C9owVTifA -O Traffic_Light_Colour_Detection/dataset.zip\n",
        "!unzip -q Traffic_Light_Colour_Detection/dataset.zip -d Traffic_Light_Colour_Detection/dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FVztu8qSRKI",
        "outputId": "01be2f76-8a93-4b08-cc4e-3734f0b492b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-09-03 22:14:27--  https://universe.roboflow.com/ds/DMu9Ql2JOA?key=2C9owVTifA\n",
            "Resolving universe.roboflow.com (universe.roboflow.com)... 151.101.1.195, 151.101.65.195, 2620:0:890::100\n",
            "Connecting to universe.roboflow.com (universe.roboflow.com)|151.101.1.195|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://storage.googleapis.com/roboflow-platform-regional-exports/Y81e6sf4D3ROUZeEmAhpmlh6XAq2/XYTRude1Olex2L3vkvCz/1/coco.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=481589474394-compute%40developer.gserviceaccount.com%2F20240903%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240903T221427Z&X-Goog-Expires=900&X-Goog-SignedHeaders=host&X-Goog-Signature=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 [following]\n",
            "--2024-09-03 22:14:27--  https://storage.googleapis.com/roboflow-platform-regional-exports/Y81e6sf4D3ROUZeEmAhpmlh6XAq2/XYTRude1Olex2L3vkvCz/1/coco.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=481589474394-compute%40developer.gserviceaccount.com%2F20240903%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240903T221427Z&X-Goog-Expires=900&X-Goog-SignedHeaders=host&X-Goog-Signature=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\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.130.207, 74.125.68.207, 64.233.170.207, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.130.207|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 212770722 (203M) [application/zip]\n",
            "Saving to: Traffic_Light_Colour_Detection/dataset.zip\n",
            "\n",
            "Traffic_Light_Colou 100%[===================>] 202.91M  18.4MB/s    in 13s     \n",
            "\n",
            "2024-09-03 22:14:41 (15.8 MB/s) - Traffic_Light_Colour_Detection/dataset.zip saved [212770722/212770722]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing - Resize Images"
      ],
      "metadata": {
        "id": "AXtrA6CxSp4d"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLD06zN-R1Wf"
      },
      "source": [
        "## Train a model with SSCMA\n",
        "All the training parameters are in the `config.py` file, you can change the parameters to train your own model.\n",
        "\n",
        "Below are explanations of some common parameters. You can also refer to the [documentation](https://sensecraftma.seeed.cc/tutorials/config) for more details.\n",
        "- `data_root` - the datasets path.\n",
        "- `epochs`- the train epochs. **we use 10 epochs as an example**.\n",
        "- `batch_size` - the batch size.\n",
        "- `height` - the image height.\n",
        "- `width` - the image width.\n",
        "- `load_from` - the pretrained model path.\n",
        "- `num_classes` - the number of classes.\n",
        "\n",
        "You can overwrite the parameters in the `config.py` file by using the `--cfg-options` argument.\n",
        "```bash\n",
        "# Example\n",
        "sscma.train config.py --cfg-options data_root=./datasets/test_dataset epochs=10\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iXPgxPcuR1Wf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1a22eb5-5512-4ea1-c2df-f8c40728d697"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using automatically generated input shape (from config 'swift_yolo_shuff_1xb16_300e_coco.py'): [1, 3, 192, 192]\n",
            "09/03 22:14:51 - mmengine - \u001b[4m\u001b[97mINFO\u001b[0m - \n",
            "------------------------------------------------------------\n",
            "System environment:\n",
            "    sys.platform: linux\n",
            "    Python: 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0]\n",
            "    CUDA available: True\n",
            "    MUSA available: False\n",
            "    numpy_random_seed: 2096186615\n",
            "    GPU 0: Tesla T4\n",
            "    CUDA_HOME: /usr/local/cuda\n",
            "    NVCC: Cuda compilation tools, release 12.2, V12.2.140\n",
            "    GCC: x86_64-linux-gnu-gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\n",
            "    PyTorch: 2.0.0+cu117\n",
            "    PyTorch compiling details: PyTorch built with:\n",
            "  - GCC 9.3\n",
            "  - C++ Version: 201703\n",
            "  - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications\n",
            "  - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e)\n",
            "  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n",
            "  - LAPACK is enabled (usually provided by MKL)\n",
            "  - NNPACK is enabled\n",
            "  - CPU capability usage: AVX2\n",
            "  - CUDA Runtime 11.7\n",
            "  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n",
            "  - CuDNN 8.5\n",
            "  - Magma 2.6.1\n",
            "  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n",
            "\n",
            "    TorchVision: 0.15.1+cu117\n",
            "    OpenCV: 4.10.0\n",
            "    MMEngine: 0.10.4\n",
            "\n",
            "Runtime environment:\n",
...

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Jerom Palimattom Tom

2 projects • 1 follower

is-it-green? : Traffic Light Colour Detection ADAS

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Build2gether 2.0 — Inclusive Innovation Challenge

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Traffic Light Colour Detection YOLO

Exported model in int8_vela.tflite format

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Jerom Palimattom Tom

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is-it-green? : Traffic Light Colour Detection ADAS

is-it-green? : Traffic Light Colour Detection ADAS

Things used in this project

Hardware components

Software apps and online services

Hand tools and fabrication machines

Story

Overview

Build2gether 2.0 — Inclusive Innovation Challenge

Problem Identification

Learnings

Proposed Solution

How It Works

Components

Prototype Circuit

Build Instructions

Prototype Testing

Future Development

Custom parts and enclosures

Enclosure Bottom STL

Enclosure Top STL

Component Placement

Schematics

Wiring Schematic

Code

TLCD-ADAS

OLED Test

Traffic Light Colour Detection YOLO

Exported model in int8_vela.tflite format

Credits

Jerom Palimattom Tom

Comments

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