diff --git a/docs/ML/projects/OCR.ipynb b/docs/ML/projects/OCR.ipynb
new file mode 100644
index 00000000..b95cb24f
--- /dev/null
+++ b/docs/ML/projects/OCR.ipynb
@@ -0,0 +1,483 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_I13dfVEjVZ5"
+      },
+      "source": [
+        "\n",
+        "\n",
+        "```\n",
+        "# This is formatted as code\n",
+        "```\n",
+        "\n",
+        "# Optical Character Recognition using Tesseract"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ydvTlknjrAHU"
+      },
+      "source": [
+        "# By Aditya Singh\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wpWkMPfNj66y"
+      },
+      "source": [
+        "Optical Character Recognition(OCR) has been a popular task in Computer Vision. Tesseract is the most open-source software available for OCR. It was initially developed by HP as a tool in C++.\n",
+        "Since 2006 it is developed by Google. The original software is available as a command-line tool for windows. We are living in a python world. Because of its popularity,the tool is also available in python--developed and maintained as an opensource project."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GDZYVGrJkorH"
+      },
+      "source": [
+        "### Step1. Install Pytesseract and tesseract-OCR in Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uWwpI-24_Nob",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "553cd595-0a23-4026-afb4-bb1139d66dec"
+      },
+      "source": [
+        "!sudo apt install tesseract-ocr"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Reading package lists... Done\n",
+            "Building dependency tree... Done\n",
+            "Reading state information... Done\n",
+            "The following additional packages will be installed:\n",
+            "  tesseract-ocr-eng tesseract-ocr-osd\n",
+            "The following NEW packages will be installed:\n",
+            "  tesseract-ocr tesseract-ocr-eng tesseract-ocr-osd\n",
+            "0 upgraded, 3 newly installed, 0 to remove and 49 not upgraded.\n",
+            "Need to get 4,816 kB of archives.\n",
+            "After this operation, 15.6 MB of additional disk space will be used.\n",
+            "Get:1 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-eng all 1:4.00~git30-7274cfa-1.1 [1,591 kB]\n",
+            "Get:2 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr-osd all 1:4.00~git30-7274cfa-1.1 [2,990 kB]\n",
+            "Get:3 http://archive.ubuntu.com/ubuntu jammy/universe amd64 tesseract-ocr amd64 4.1.1-2.1build1 [236 kB]\n",
+            "Fetched 4,816 kB in 2s (2,904 kB/s)\n",
+            "debconf: unable to initialize frontend: Dialog\n",
+            "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 78, <> line 3.)\n",
+            "debconf: falling back to frontend: Readline\n",
+            "debconf: unable to initialize frontend: Readline\n",
+            "debconf: (This frontend requires a controlling tty.)\n",
+            "debconf: falling back to frontend: Teletype\n",
+            "dpkg-preconfigure: unable to re-open stdin: \n",
+            "Selecting previously unselected package tesseract-ocr-eng.\n",
+            "(Reading database ... 123614 files and directories currently installed.)\n",
+            "Preparing to unpack .../tesseract-ocr-eng_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
+            "Unpacking tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n",
+            "Selecting previously unselected package tesseract-ocr-osd.\n",
+            "Preparing to unpack .../tesseract-ocr-osd_1%3a4.00~git30-7274cfa-1.1_all.deb ...\n",
+            "Unpacking tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n",
+            "Selecting previously unselected package tesseract-ocr.\n",
+            "Preparing to unpack .../tesseract-ocr_4.1.1-2.1build1_amd64.deb ...\n",
+            "Unpacking tesseract-ocr (4.1.1-2.1build1) ...\n",
+            "Setting up tesseract-ocr-eng (1:4.00~git30-7274cfa-1.1) ...\n",
+            "Setting up tesseract-ocr-osd (1:4.00~git30-7274cfa-1.1) ...\n",
+            "Setting up tesseract-ocr (4.1.1-2.1build1) ...\n",
+            "Processing triggers for man-db (2.10.2-1) ...\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ujL16dZ2_O-3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "781a1fc8-5b33-4e9f-fcca-6718dea17723"
+      },
+      "source": [
+        "!pip install pytesseract"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting pytesseract\n",
+            "  Downloading pytesseract-0.3.13-py3-none-any.whl.metadata (11 kB)\n",
+            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from pytesseract) (24.1)\n",
+            "Requirement already satisfied: Pillow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from pytesseract) (10.4.0)\n",
+            "Downloading pytesseract-0.3.13-py3-none-any.whl (14 kB)\n",
+            "Installing collected packages: pytesseract\n",
+            "Successfully installed pytesseract-0.3.13\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zlsiJdwnkyx7"
+      },
+      "source": [
+        "### Step2. import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QKJh7JjTAqzO"
+      },
+      "source": [
+        "import pytesseract\n",
+        "import shutil\n",
+        "import os\n",
+        "import random\n",
+        "try:\n",
+        "    from PIL import Image\n",
+        "except ImportError:\n",
+        "    import Image"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yjaf9bJ3k4GC"
+      },
+      "source": [
+        "### Step3. Upload Image to the Colab"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8zmc-K_nAyg1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "outputId": "b60c904c-7496-4e8f-e964-80dff99843ef"
+      },
+      "source": [
+        "from google.colab import files\n",
+        "\n",
+        "uploaded = files.upload()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-55a35b2e-2091-4060-b94e-3f81fdc98a06\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-55a35b2e-2091-4060-b94e-3f81fdc98a06\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving quotes-about-life-love-yourself.jpg to quotes-about-life-love-yourself.jpg\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MDdbltgIlF3Q"
+      },
+      "source": [
+        "## Step4. Text Extraction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HaM3cMUDA_Ma"
+      },
+      "source": [
+        "import pytesseract\n",
+        "import shutil\n",
+        "import os\n",
+        "import random\n",
+        "try:\n",
+        "    from PIL import Image\n",
+        "except ImportError:\n",
+        "    import Image\n",
+        "import requests\n",
+        "from io import BytesIO\n",
+        "\n",
+        "# Fetch the image from the URL\n",
+        "response = requests.get('https://becomingunbusy.com/wp-content/uploads/2019/05/quotes-about-life-love-yourself.jpg', stream=True)\n",
+        "response.raw.decode_content = True  # Ensure correct decoding\n",
+        "\n",
+        "# Open the image using BytesIO to handle the image data\n",
+        "image = Image.open(BytesIO(response.content))\n",
+        "\n",
+        "# Now you can use pytesseract to extract text\n",
+        "extractedInformation = pytesseract.image_to_string(image)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D552cuoflHvx"
+      },
+      "source": [
+        "### Step5. Printing the extracted information"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Key-3vILBNUd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f87373b9-2b31-4f2c-9e7f-efbe451e37e9"
+      },
+      "source": [
+        "print(extractedInformation)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "KAMEN ARIE NA I\n",
+            "love yourself enough to no longer\n",
+            "\n",
+            "just dream of a better life, let it be the day\n",
+            "\n",
+            "TONE Hae\n",
+            "\n",
+            " \n",
+            "\f\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/docs/ML/projects/quotes-about-life-love-yourself.jpg b/docs/ML/projects/quotes-about-life-love-yourself.jpg
new file mode 100644
index 00000000..e89cdcef
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diff --git a/product detection/Product_Detection.ipynb b/product detection/Product_Detection.ipynb
new file mode 100644
index 00000000..64b654df
--- /dev/null
+++ b/product detection/Product_Detection.ipynb	
@@ -0,0 +1,389 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "# Install the required dependencies\n",
+        "!pip install opencv-python opencv-python-headless\n",
+        "\n",
+        "# Download the YOLOv3 pre-trained model weights\n",
+        "!wget https://pjreddie.com/media/files/yolov3.weights\n",
+        "!wget https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg\n",
+        "!wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jKona4B6ng4V",
+        "outputId": "b043c4c6-147e-4dda-e7d1-5b30c3833ba2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.11/dist-packages (4.10.0.84)\n",
+            "Requirement already satisfied: opencv-python-headless in /usr/local/lib/python3.11/dist-packages (4.10.0.84)\n",
+            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.11/dist-packages (from opencv-python) (1.26.4)\n",
+            "--2025-01-16 06:20:16--  https://pjreddie.com/media/files/yolov3.weights\n",
+            "Resolving pjreddie.com (pjreddie.com)... 162.0.215.52\n",
+            "Connecting to pjreddie.com (pjreddie.com)|162.0.215.52|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 248007048 (237M) [application/octet-stream]\n",
+            "Saving to: ‘yolov3.weights’\n",
+            "\n",
+            "yolov3.weights      100%[===================>] 236.52M  12.3MB/s    in 18s     \n",
+            "\n",
+            "2025-01-16 06:20:35 (12.9 MB/s) - ‘yolov3.weights’ saved [248007048/248007048]\n",
+            "\n",
+            "--2025-01-16 06:20:35--  https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 8342 (8.1K) [text/plain]\n",
+            "Saving to: ‘yolov3.cfg’\n",
+            "\n",
+            "yolov3.cfg          100%[===================>]   8.15K  --.-KB/s    in 0s      \n",
+            "\n",
+            "2025-01-16 06:20:35 (58.2 MB/s) - ‘yolov3.cfg’ saved [8342/8342]\n",
+            "\n",
+            "--2025-01-16 06:20:35--  https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 625 [text/plain]\n",
+            "Saving to: ‘coco.names’\n",
+            "\n",
+            "coco.names          100%[===================>]     625  --.-KB/s    in 0s      \n",
+            "\n",
+            "2025-01-16 06:20:35 (28.8 MB/s) - ‘coco.names’ saved [625/625]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import cv2\n",
+        "import numpy as np\n",
+        "\n",
+        "# Load YOLO model\n",
+        "net = cv2.dnn.readNet(\"yolov3.weights\", \"yolov3.cfg\")\n",
+        "layer_names = net.getLayerNames()\n",
+        "output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]\n",
+        "\n",
+        "# Load class labels (from coco.names)\n",
+        "with open(\"coco.names\", \"r\") as f:\n",
+        "    classes = [line.strip() for line in f.readlines()]\n",
+        "\n",
+        "# Function to perform object detection\n",
+        "def detect_objects(image_path):\n",
+        "    # Load image\n",
+        "    img = cv2.imread(image_path)\n",
+        "    height, width, channels = img.shape\n",
+        "\n",
+        "    # Prepare the image for YOLO\n",
+        "    blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n",
+        "    net.setInput(blob)\n",
+        "    outputs = net.forward(output_layers)\n",
+        "\n",
+        "    # Initialize lists for detected objects\n",
+        "    class_ids = []\n",
+        "    confidences = []\n",
+        "    boxes = []\n",
+        "\n",
+        "    # Iterate over all detections\n",
+        "    for out in outputs:\n",
+        "        for detection in out:\n",
+        "            scores = detection[5:]\n",
+        "            class_id = np.argmax(scores)\n",
+        "            confidence = scores[class_id]\n",
+        "\n",
+        "            if confidence > 0.5:  # Confidence threshold\n",
+        "                center_x = int(detection[0] * width)\n",
+        "                center_y = int(detection[1] * height)\n",
+        "                w = int(detection[2] * width)\n",
+        "                h = int(detection[3] * height)\n",
+        "\n",
+        "                # Coordinates for the bounding box\n",
+        "                x = int(center_x - w / 2)\n",
+        "                y = int(center_y - h / 2)\n",
+        "\n",
+        "                # Save the detection details\n",
+        "                boxes.append([x, y, w, h])\n",
+        "                confidences.append(float(confidence))\n",
+        "                class_ids.append(class_id)\n",
+        "\n",
+        "    # Apply non-maxima suppression to remove overlapping boxes\n",
+        "    indexes = cv2.dnn.NMSBoxes(boxes, confidences, score_threshold=0.5, nms_threshold=0.4)\n",
+        "\n",
+        "    # Draw the bounding boxes and labels\n",
+        "    detected_objects = []\n",
+        "    if len(indexes) > 0:\n",
+        "        for i in indexes.flatten():\n",
+        "            x, y, w, h = boxes[i]\n",
+        "            label = str(classes[class_ids[i]])\n",
+        "            confidence = confidences[i]\n",
+        "            detected_objects.append((label, confidence))\n",
+        "            cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)\n",
+        "            cv2.putText(img, f\"{label} {confidence:.2f}\", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n",
+        "\n",
+        "    return img, detected_objects\n",
+        "\n",
+        "# Display results\n",
+        "from google.colab import files\n",
+        "from IPython.display import Image\n",
+        "\n",
+        "# Upload an image\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "# Get image path\n",
+        "image_path = list(uploaded.keys())[0]\n",
+        "\n",
+        "# Run detection\n",
+        "result_img, detected_objects = detect_objects(image_path)\n",
+        "\n",
+        "# Save and display the result\n",
+        "output_path = \"result.jpg\"\n",
+        "cv2.imwrite(output_path, result_img)\n",
+        "Image(output_path)\n",
+        "\n",
+        "# Print detected objects and confidence\n",
+        "print(\"Detected Objects and Confidence Scores:\")\n",
+        "for label, confidence in detected_objects:\n",
+        "    print(f\"{label}: {confidence:.2f}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 108
+        },
+        "id": "P1krlvdqnv19",
+        "outputId": "7a7b00d2-e6c8-49cc-bbc3-13a003e9850c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-f11bbaba-b073-401e-b121-860c667e4e75\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-f11bbaba-b073-401e-b121-860c667e4e75\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving Screenshot 2024-12-24 112646.png to Screenshot 2024-12-24 112646.png\n",
+            "Detected Objects and Confidence Scores:\n",
+            "bird: 0.98\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/product detection/Readme.md b/product detection/Readme.md
new file mode 100644
index 00000000..a5db9e20
--- /dev/null
+++ b/product detection/Readme.md	
@@ -0,0 +1,27 @@
+# Product Detection
+
+## Overview
+This repository provides a solution for product detection using machine learning and computer vision techniques. The project leverages deep learning models to detect and classify products from images or video streams. It is suitable for applications in inventory management, retail analytics, and e-commerce.
+
+## Features
+- **Product Detection**: Identifies and locates products within images and videos.
+- **Product Classification**: Classifies detected products based on pre-trained models.
+- **Real-Time Processing**: Supports real-time video processing for dynamic environments.
+- **Customizable**: Allows fine-tuning to specific product datasets and use cases.
+
+## Requirements
+- Python 3.x
+- OpenCV
+- TensorFlow or PyTorch (depending on model used)
+- Other dependencies listed in `requirements.txt`
+
+## Installation
+
+Follow these steps to get started with the project.
+
+1. Clone the repository to your local machine:
+   ```bash
+   git clone https://github.com/username/product-detection.git
+   cd product-detection
+
+2. pip install -r requirements.txt