Detection_Of_Dyslexia_From_Handwriting.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ts4iaMCX0Old",
        "outputId": "7e40d619-d795-4dff-e8a8-6a7a82beddeb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: nvidia-smi: command not found\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### 1. Mount Google Drive ###\n",
        "\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJK8rLch0yED",
        "outputId": "24ff9aa3-a01a-4d54-9c76-db7e539657de"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### 2. Prepare data ###\n",
        "\n",
        "!scp '/content/gdrive/My Drive/AIProject/data.zip' '/content/data.zip'\n",
        "\n",
        "!unzip '/content/data.zip' -d '/content/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZpYF_TeS1Ayp",
        "outputId": "bfe29517-622a-4c71-a7e5-b043dd383b59"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/data.zip\n",
            "replace /content/test/dyslexic/1.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "### 3. Install Ultralytics ###\n",
        "\n",
        "!pip install ultralytics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rIvNV33W1jq3",
        "outputId": "8c6bec10-95dc-498d-f5ac-67212a03437e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.1.29-py3-none-any.whl (721 kB)\n",
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            "Collecting thop>=0.1.1 (from ultralytics)\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
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            "  Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
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            "\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.8.0->ultralytics)\n",
            "  Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.8.0->ultralytics)\n",
            "  Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
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            "  Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
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            "\u001b[?25hCollecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.8.0->ultralytics)\n",
            "  Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
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            "\u001b[?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.8.0->ultralytics)\n",
            "  Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
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            "\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8.0->ultralytics)\n",
            "  Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
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            "\u001b[?25hCollecting nvidia-nccl-cu12==2.19.3 (from torch>=1.8.0->ultralytics)\n",
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            "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
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            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics)\n",
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            "\u001b[?25hRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, thop, ultralytics\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 thop-0.1.1.post2209072238 ultralytics-8.1.29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### 4. Train model ###\n",
        "\n",
        "import os\n",
        "\n",
        "from ultralytics import YOLO\n",
        "\n",
        "\n",
        "# Load a model\n",
        "model = YOLO(\"yolov8n-cls.pt\")  # load a pretained model\n",
        "\n",
        "# Use the model\n",
        "results = model.train(data='/content/data/', epochs=10)  # train the model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZLqA-Kb1wD2",
        "outputId": "348218b3-8626-4a07-a78e-8428446f4c67"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.1.29 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=classify, mode=train, model=yolov8n-cls.pt, data=/content/data/, epochs=10, time=None, patience=100, batch=16, imgsz=224, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train3, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/classify/train3\n",
            "\u001b[34m\u001b[1mtrain:\u001b[0m /content/data/train... found 100 images in 2 classes ✅ \n",
            "\u001b[34m\u001b[1mval:\u001b[0m None...\n",
            "\u001b[34m\u001b[1mtest:\u001b[0m /content/data/test... found 100 images in 2 classes ✅ \n",
            "Overriding model.yaml nc=1000 with nc=2\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
            "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
            "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
            "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
            "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
            "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
            "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
            "  9                  -1  1    332802  ultralytics.nn.modules.head.Classify         [256, 2]                      \n",
            "YOLOv8n-cls summary: 99 layers, 1440850 parameters, 1440850 gradients, 3.4 GFLOPs\n",
            "Transferred 156/158 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/classify/train3', view at http://localhost:6006/\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/data/train... 100 images, 0 corrupt: 100%|██████████| 100/100 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/data/test... 100 images, 0 corrupt: 100%|██████████| 100/100 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000714, momentum=0.9) with parameter groups 26 weight(decay=0.0), 27 weight(decay=0.0005), 27 bias(decay=0.0)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 224 train, 224 val\n",
            "Using 0 dataloader workers\n",
            "Logging results to \u001b[1mruns/classify/train3\u001b[0m\n",
            "Starting training for 10 epochs...\n",
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       1/10         0G     0.7359          4        224: 100%|██████████| 7/7 [00:07<00:00,  1.07s/it]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:02<00:00,  1.72it/s]"
          ]
        },
        {
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          "text": [
            "                   all       0.49          1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
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          "text": [
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "       2/10         0G     0.6479          4        224: 100%|██████████| 7/7 [00:05<00:00,  1.20it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:03<00:00,  1.26it/s]"
          ]
        },
        {
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            "                   all       0.78          1\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       3/10         0G     0.5411          4        224: 100%|██████████| 7/7 [00:06<00:00,  1.11it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:02<00:00,  1.72it/s]"
          ]
        },
        {
          "output_type": "stream",
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          "text": [
            "                   all       0.85          1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       4/10         0G     0.4805          4        224: 100%|██████████| 7/7 [00:10<00:00,  1.50s/it]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:03<00:00,  1.13it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       0.93          1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       5/10         0G      0.422          4        224: 100%|██████████| 7/7 [00:06<00:00,  1.09it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:03<00:00,  1.21it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       0.96          1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       6/10         0G     0.3489          4        224: 100%|██████████| 7/7 [00:05<00:00,  1.21it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:02<00:00,  1.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       0.99          1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       7/10         0G     0.2929          4        224: 100%|██████████| 7/7 [00:06<00:00,  1.01it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:02<00:00,  1.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       0.99          1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       8/10         0G     0.2346          4        224: 100%|██████████| 7/7 [00:06<00:00,  1.07it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:02<00:00,  1.46it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       0.99          1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       9/10         0G     0.2302          4        224: 100%|██████████| 7/7 [00:05<00:00,  1.20it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:02<00:00,  1.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       0.99          1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem       loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      10/10         0G     0.2736          4        224: 100%|██████████| 7/7 [00:06<00:00,  1.03it/s]\n",
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:02<00:00,  1.71it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       0.99          1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "10 epochs completed in 0.028 hours.\n",
            "Optimizer stripped from runs/classify/train3/weights/last.pt, 3.0MB\n",
            "Optimizer stripped from runs/classify/train3/weights/best.pt, 3.0MB\n",
            "\n",
            "Validating runs/classify/train3/weights/best.pt...\n",
            "Ultralytics YOLOv8.1.29 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.20GHz)\n",
            "YOLOv8n-cls summary (fused): 73 layers, 1437442 parameters, 0 gradients, 3.3 GFLOPs\n",
            "WARNING ⚠️ Dataset 'split=val' not found, using 'split=test' instead.\n",
            "\u001b[34m\u001b[1mtrain:\u001b[0m /content/data/train... found 100 images in 2 classes ✅ \n",
            "\u001b[34m\u001b[1mval:\u001b[0m None...\n",
            "\u001b[34m\u001b[1mtest:\u001b[0m /content/data/test... found 100 images in 2 classes ✅ \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "               classes   top1_acc   top5_acc: 100%|██████████| 4/4 [00:03<00:00,  1.25it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all       0.99          1\n",
            "Speed: 0.0ms preprocess, 15.7ms inference, 0.0ms loss, 0.0ms postprocess per image\n",
            "Results saved to \u001b[1mruns/classify/train3\u001b[0m\n",
            "Results saved to \u001b[1mruns/classify/train3\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Access classification metrics\n",
        "top1_accuracy = results.top1\n",
        "top5_accuracy = results.top5\n",
        "print(\"Top-1 Accuracy:\", top1_accuracy)\n",
        "print(\"Top-5 Accuracy:\", top5_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5opfCqFU2boy",
        "outputId": "7cc3dc85-6cc6-4023-9c0a-891c0f8452f4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top-1 Accuracy: 0.9900000095367432\n",
            "Top-5 Accuracy: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### 5. Copy results ###\n",
        "\n",
        "!scp -r /content/runs '/content/gdrive/My Drive/AIProject'"
      ],
      "metadata": {
        "id": "wh_dQbVD4I_e"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1sfUQs_n4Xnp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}