Prayer Prediction.ipynb

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    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "8_nFjlvh8L5h",
        "outputId": "0418bd61-161e-46ed-9536-14f76de2d719"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          "text": [
            "Downloading Dataset Version Zip in Prayer-postures-4 to yolov11:: 100%|██████████| 222411/222411 [00:04<00:00, 50155.85it/s]"
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            "\n",
            "Extracting Dataset Version Zip to Prayer-postures-4 in yolov11:: 100%|██████████| 9410/9410 [00:02<00:00, 4445.30it/s]\n"
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      ],
      "source": [
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"wAFpXIOS5YhwXaos60c3\")\n",
        "project = rf.workspace(\"istaqim\").project(\"prayer-postures-mz0kh\")\n",
        "version = project.version(4)\n",
        "dataset = version.download(\"yolov11\")\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ultralytics\n",
        "from ultralytics import YOLO\n",
        "model = YOLO(\"yolo11n.pt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "GR33g_Ke8jrH",
        "outputId": "1f8693bc-877f-4b42-94dc-6cf7d2f45c45"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
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            "Installing collected packages: ultralytics-thop, ultralytics\n",
            "Successfully installed ultralytics-8.3.18 ultralytics-thop-2.0.9\n",
            "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
            "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
            "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n"
          ]
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          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vACNh6NA8vxk",
        "outputId": "62e46961-0e85-4209-c5f1-f9a7fb8bffbb"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\u001b[01;34mPrayer-postures-4\u001b[0m/  \u001b[01;34msample_data\u001b[0m/  yolo11n.pt\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.location"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "RW14bHJ380xG",
        "outputId": "89c7dac6-d672-46f6-a885-5a049f2ed04d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/Prayer-postures-4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Disable W&B\n",
        "import os\n",
        "os.environ['WANDB_MODE'] = 'disabled'\n",
        "\n",
        "# Run YOLO training\n",
        "!yolo task=detect mode=train epochs=100 batch=32 plots=True model='yolo11n.pt' data={dataset.location}/data.yaml imgsz=640\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q4KvKy0d86wl",
        "outputId": "61dc59b0-ae30-4c36-8303-3a8ea0890543"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics 8.3.18 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11n.pt, data=/content/Prayer-postures-4/data.yaml, epochs=100, time=None, patience=100, batch=32, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train4, 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=True, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train4\n",
            "Overriding model.yaml nc=80 with nc=9\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      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      \n",
            "  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
            "  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     \n",
            "  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
            "  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]           \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           \n",
            "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
            " 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]                 \n",
            " 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]          \n",
            " 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]           \n",
            " 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
            " 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]          \n",
            " 20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
            " 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]           \n",
            " 23        [16, 19, 22]  1    432427  ultralytics.nn.modules.head.Detect           [9, [64, 128, 256]]           \n",
            "YOLO11n summary: 319 layers, 2,591,595 parameters, 2,591,579 gradients, 6.4 GFLOPs\n",
            "\n",
            "Transferred 448/499 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train4', view at http://localhost:6006/\n",
            "Freezing layer 'model.23.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLO11n...\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/Prayer-postures-4/train/labels... 3294 images, 0 backgrounds, 0 corrupt: 100% 3294/3294 [00:01<00:00, 2087.99it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/Prayer-postures-4/train/labels.cache\n",
            "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 40, len(boxes) = 4430. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n",
            "/usr/local/lib/python3.10/dist-packages/albumentations/__init__.py:13: UserWarning: A new version of Albumentations is available: 1.4.18 (you have 1.4.15). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.\n",
            "  check_for_updates()\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/Prayer-postures-4/valid/labels... 1030 images, 0 backgrounds, 0 corrupt: 100% 1030/1030 [00:00<00:00, 1407.85it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/Prayer-postures-4/valid/labels.cache\n",
            "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 24, len(boxes) = 1467. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n",
            "Plotting labels to runs/detect/train4/labels.jpg... \n",
            "\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=0.000769, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train4\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      1/100      5.05G      1.155      3.191       1.43         79        640: 100% 103/103 [01:01<00:00,  1.67it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 17/17 [00:11<00:00,  1.45it/s]\n",
            "                   all       1030       1467      0.616      0.202      0.204      0.111\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      2/100      4.71G      1.167      2.293      1.394         72        640: 100% 103/103 [01:01<00:00,  1.68it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 17/17 [00:10<00:00,  1.59it/s]\n",
            "                   all       1030       1467      0.356      0.432      0.248      0.143\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      3/100      4.72G      1.148      2.067      1.368         84        640:  46% 47/103 [00:28<00:36,  1.52it/s]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "MI-DhWNh-X7N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r runs/train/your_run_folder_name /content/drive/MyDrive/your_desired_path/\n"
      ],
      "metadata": {
        "id": "SVBZO8jrDxB1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BsFV9_uvD1bX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}