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SametSahin10/campusassistant.ipynb

Created August 6, 2024 08:22
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campusassistant.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"mount_file_id": "1Ou7cw6VefZuctO7nXmKJKn3akGv3qDKB",
"authorship_tag": "ABX9TyNvOS9dNRoD9Ru/he4jH5i8",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/SametSahin10/dfd1b0e19cb78bbaaa2b5d2c35271561/campusassistant.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"!pip install ultralytics\n",
"!pip install tensorflow==2.13.1"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "OZVIFLOY2UlV",
"outputId": "b0e3aaf0-f108-4a1a-d324-142a58acffff"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting ultralytics\n",
" Downloading ultralytics-8.2.16-py3-none-any.whl (756 kB)\n",
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/756.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/756.9 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m747.5/756.9 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m756.9/756.9 kB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n",
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"Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.2.1+cu121)\n",
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"Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n",
"Collecting thop>=0.1.1 (from ultralytics)\n",
" Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
"Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.0.3)\n",
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"Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
"Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
"Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
"Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
"Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
"Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
"Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
"Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
"Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
"Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
"Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
"Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.2.0)\n",
"Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics)\n",
" Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
"Requirement 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",
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"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-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 thop-0.1.1.post2209072238 ultralytics-8.2.16\n",
"Collecting tensorflow==2.13.1\n",
" Downloading tensorflow-2.13.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (479.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m479.7/479.7 MB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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" Downloading keras-2.13.1-py3-none-any.whl (1.7 MB)\n",
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"Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (2.1.5)\n",
"Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (0.6.0)\n",
"Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.14,>=2.13->tensorflow==2.13.1) (3.2.2)\n",
"Installing collected packages: typing-extensions, tensorflow-estimator, numpy, keras, gast, google-auth-oauthlib, tensorboard, tensorflow\n",
" Attempting uninstall: typing-extensions\n",
" Found existing installation: typing_extensions 4.11.0\n",
" Uninstalling typing_extensions-4.11.0:\n",
" Successfully uninstalled typing_extensions-4.11.0\n",
" Attempting uninstall: tensorflow-estimator\n",
" Found existing installation: tensorflow-estimator 2.15.0\n",
" Uninstalling tensorflow-estimator-2.15.0:\n",
" Successfully uninstalled tensorflow-estimator-2.15.0\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 1.25.2\n",
" Uninstalling numpy-1.25.2:\n",
" Successfully uninstalled numpy-1.25.2\n",
" Attempting uninstall: keras\n",
" Found existing installation: keras 2.15.0\n",
" Uninstalling keras-2.15.0:\n",
" Successfully uninstalled keras-2.15.0\n",
" Attempting uninstall: gast\n",
" Found existing installation: gast 0.5.4\n",
" Uninstalling gast-0.5.4:\n",
" Successfully uninstalled gast-0.5.4\n",
" Attempting uninstall: google-auth-oauthlib\n",
" Found existing installation: google-auth-oauthlib 1.2.0\n",
" Uninstalling google-auth-oauthlib-1.2.0:\n",
" Successfully uninstalled google-auth-oauthlib-1.2.0\n",
" Attempting uninstall: tensorboard\n",
" Found existing installation: tensorboard 2.15.2\n",
" Uninstalling tensorboard-2.15.2:\n",
" Successfully uninstalled tensorboard-2.15.2\n",
" Attempting uninstall: tensorflow\n",
" Found existing installation: tensorflow 2.15.0\n",
" Uninstalling tensorflow-2.15.0:\n",
" Successfully uninstalled tensorflow-2.15.0\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",
"sqlalchemy 2.0.30 requires typing-extensions>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
"pandas-stubs 2.0.3.230814 requires numpy>=1.25.0; python_version >= \"3.9\", but you have numpy 1.24.3 which is incompatible.\n",
"pydantic 2.7.1 requires typing-extensions>=4.6.1, but you have typing-extensions 4.5.0 which is incompatible.\n",
"pydantic-core 2.18.2 requires typing-extensions!=4.7.0,>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
"tf-keras 2.15.1 requires tensorflow<2.16,>=2.15, but you have tensorflow 2.13.1 which is incompatible.\n",
"torch 2.2.1+cu121 requires typing-extensions>=4.8.0, but you have typing-extensions 4.5.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed gast-0.4.0 google-auth-oauthlib-1.0.0 keras-2.13.1 numpy-1.24.3 tensorboard-2.13.0 tensorflow-2.13.1 tensorflow-estimator-2.13.0 typing-extensions-4.5.0\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"numpy"
]
},
"id": "4ef696adbb1f488889e1acdd44d13cf1"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"from ultralytics import YOLO\n",
"\n",
"\n",
"os.chdir('/content/drive/MyDrive/campus-assistant')\n",
"\n",
"# Load a model\n",
"# Modeli ilk kez egitirken pretrained bir model almiyordum.\n",
"# Alttaki satiri, egitim bittikten sonra prediction yapmak icin ekledigimi\n",
"# hatirliyorum. Pretrained model'i load etmeden nasil model olusturulduguna\n",
"# bakmak gerek.\n",
"model = YOLO('runs/detect/train13/weights/best.pt') # load a pretrained model (recommended for training)"
],
"metadata": {
"id": "35Yk9hH9iBk5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!ls -l\n",
"!cat campus_dataset.yaml\n",
"# Train the model\n",
"results = model.train(data='campus_dataset.yaml', epochs=100, imgsz=320, device=[0])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tYk334Q6-Apv",
"outputId": "e009286c-1ce4-4e7e-9a99-d7484d575991"
},
"execution_count": null,
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"total 9\n",
"-rw------- 1 root root 283 May 14 20:49 campus_dataset.yaml\n",
"drwx------ 4 root root 4096 May 14 21:25 dataset\n",
"drwx------ 3 root root 4096 May 14 21:21 runs\n",
"path: dataset\n",
"train: images/train\n",
"val: images/val\n",
"test: images/test\n",
"\n",
"names:\n",
" 0: aKapisiKuzey\n",
" 1: aKapisiDogu\n",
" 2: aKapisiGuney\n",
" 3: aKapisiBati\n",
" 4: havuzKuzey\n",
" 5: havuzDogu\n",
" 6: havuzGuney\n",
" 7: havuzBati\n",
" 8: iletisimKuzey\n",
" 9: iletisimDogu\n",
" 10: iletisimGuney\n",
" 11: iletisimBatiUltralytics YOLOv8.2.15 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=campus_dataset.yaml, epochs=100, time=None, patience=100, batch=16, imgsz=320, save=True, save_period=-1, cache=False, device=[0], workers=8, project=None, name=train13, 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, bgr=0.0, 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/detect/train13\n",
"Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 755k/755k [00:00<00:00, 49.5MB/s]\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"Overriding model.yaml nc=80 with nc=12\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 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] \n",
" 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] \n",
" 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] \n",
" 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] \n",
" 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] \n",
" 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n",
" 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] \n",
" 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] \n",
" 22 [15, 18, 21] 1 753652 ultralytics.nn.modules.head.Detect [12, [64, 128, 256]] \n",
"Model summary: 225 layers, 3013188 parameters, 3013172 gradients, 8.2 GFLOPs\n",
"\n",
"Transferred 319/355 items from pretrained weights\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train13', view at http://localhost:6006/\n",
"Freezing layer 'model.22.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
"Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6.23M/6.23M [00:00<00:00, 108MB/s]\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/dataset/labels/train... 857 images, 2 backgrounds, 0 corrupt: 100%|██████████| 859/859 [00:00<00:00, 1938.12it/s]"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/dataset/labels/train.cache\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\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), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
" self.pid = os.fork()\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/dataset/labels/val... 214 images, 0 backgrounds, 0 corrupt: 100%|██████████| 214/214 [00:00<00:00, 1047.05it/s]"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/dataset/labels/val.cache\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"Plotting labels to runs/detect/train13/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=0.000625, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
"Image sizes 320 train, 320 val\n",
"Using 2 dataloader workers\n",
"Logging results to \u001b[1mruns/detect/train13\u001b[0m\n",
"Starting training for 100 epochs...\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
" 1/100 0.91G 1.332 3.57 1.466 26 320: 100%|██████████| 54/54 [00:11<00:00, 4.80it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:03<00:00, 1.93it/s]"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
" all 214 244 0.957 0.0689 0.104 0.058\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
" 2/100 0.856G 1.188 2.521 1.391 27 320: 100%|██████████| 54/54 [00:10<00:00, 5.33it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 6.03it/s]"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
" all 214 244 0.617 0.398 0.392 0.242\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
" 3/100 0.856G 1.192 2.017 1.425 31 320: 100%|██████████| 54/54 [00:12<00:00, 4.48it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.46it/s]"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
" all 214 244 0.543 0.529 0.44 0.242\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
" 4/100 0.856G 1.121 1.74 1.373 25 320: 100%|██████████| 54/54 [00:07<00:00, 6.79it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 5.47it/s]\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
" all 214 244 0.778 0.511 0.626 0.371\n",
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
" 5/100 0.849G 1.096 1.626 1.318 24 320: 100%|██████████| 54/54 [00:12<00:00, 4.49it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 6.16it/s]"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
" all 214 244 0.534 0.58 0.574 0.346\n"
]
},
{
"metadata": {
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"output_type": "stream",
"text": [
"\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
},
{
"metadata": {
"tags": null
},
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"output_type": "stream",
"text": [
" 6/100 0.849G 1.073 1.489 1.3 25 320: 100%|██████████| 54/54 [00:09<00:00, 5.91it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 3.60it/s]"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
" all 214 244 0.84 0.554 0.688 0.438\n"
]
},
{
"metadata": {
"tags": null
},
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"output_type": "stream",
"text": [
"\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
" 7/100 0.87G 1.072 1.411 1.287 32 320: 100%|██████████| 54/54 [00:09<00:00, 5.69it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 5.88it/s]"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
" all 214 244 0.748 0.628 0.636 0.401\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stderr",
"output_type": "stream",
"text": [
" 8/100 0.849G 1.03 1.329 1.281 28 320: 100%|██████████| 54/54 [00:12<00:00, 4.44it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 3.04it/s]"
]
},
{
"metadata": {
"tags": null
},
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" all 214 244 0.664 0.694 0.694 0.445\n"
]
},
{
"metadata": {
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"output_type": "stream",
"text": [
"\n"
]
},
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
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" 84/100 0.847G 0.6096 0.4768 1.023 34 320: 100%|██████████| 54/54 [00:11<00:00, 4.57it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 6.55it/s]"
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]
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" all 214 244 0.944 0.838 0.896 0.684\n"
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]
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" all 214 244 0.951 0.846 0.901 0.691\n"
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" all 214 244 0.936 0.876 0.895 0.679\n"
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]
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" all 214 244 0.951 0.877 0.893 0.678\n"
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"output_type": "stream",
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"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Closing dataloader mosaic\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), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
" self.pid = os.fork()\n"
]
},
{
"output_type": "stream",
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"\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n"
]
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" all 214 244 0.945 0.886 0.893 0.679\n",
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"\n"
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},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"100 epochs completed in 0.347 hours.\n",
"Optimizer stripped from runs/detect/train13/weights/last.pt, 6.2MB\n",
"Optimizer stripped from runs/detect/train13/weights/best.pt, 6.2MB\n",
"\n",
"Validating runs/detect/train13/weights/best.pt...\n",
"Ultralytics YOLOv8.2.15 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"Model summary (fused): 168 layers, 3007988 parameters, 0 gradients, 8.1 GFLOPs\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
" Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:02<00:00, 2.63it/s]\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" all 214 244 0.956 0.88 0.914 0.698\n",
" aKapisiKuzey 214 11 1 0.956 0.995 0.734\n",
" aKapisiDogu 214 3 0.894 1 0.995 0.709\n",
" aKapisiGuney 214 33 0.762 0.909 0.915 0.715\n",
" aKapisiBati 214 3 1 0 0.187 0.07\n",
" havuzKuzey 214 3 1 0.934 0.995 0.676\n",
" havuzDogu 214 5 1 0.98 0.995 0.729\n",
" havuzGuney 214 21 0.883 0.857 0.913 0.624\n",
" havuzBati 214 15 1 0.925 0.991 0.645\n",
" iletisimKuzey 214 31 0.966 1 0.994 0.865\n",
" iletisimDogu 214 32 0.989 1 0.995 0.823\n",
" iletisimGuney 214 26 0.982 1 0.995 0.889\n",
" iletisimBati 214 61 0.991 1 0.995 0.893\n",
"Speed: 0.1ms preprocess, 1.1ms inference, 0.0ms loss, 1.8ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/train13\u001b[0m\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"results = model('dataset/images/test/IMG_2713.JPG')\n",
"prediction = results[0]\n",
"print(f\"Prediction: {prediction}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uCzOgUeNGs5f",
"outputId": "d00c531a-4c1e-4ab9-c950-85d25d8dd40c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_runtime.py:184: UserWarning: Pydantic is installed but cannot be imported. Please check your installation. `huggingface_hub` will default to not using Pydantic. Error message: '{e}'\n",
" warnings.warn(\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"image 1/1 /content/drive/MyDrive/campus-assistant/dataset/images/test/IMG_2713.JPG: 320x320 1 iletisimBati, 7.1ms\n",
"Speed: 4.4ms preprocess, 7.1ms inference, 3121.3ms postprocess per image at shape (1, 3, 320, 320)\n",
"Prediction: ultralytics.engine.results.Results object with attributes:\n",
"\n",
"boxes: ultralytics.engine.results.Boxes object\n",
"keypoints: None\n",
"masks: None\n",
"names: {0: 'aKapisiKuzey', 1: 'aKapisiDogu', 2: 'aKapisiGuney', 3: 'aKapisiBati', 4: 'havuzKuzey', 5: 'havuzDogu', 6: 'havuzGuney', 7: 'havuzBati', 8: 'iletisimKuzey', 9: 'iletisimDogu', 10: 'iletisimGuney', 11: 'iletisimBati'}\n",
"obb: None\n",
"orig_img: array([[[109, 65, 41],\n",
" [110, 67, 40],\n",
" [114, 69, 42],\n",
" ...,\n",
" [101, 57, 34],\n",
" [100, 55, 34],\n",
" [ 99, 54, 33]],\n",
"\n",
" [[110, 66, 42],\n",
" [111, 68, 41],\n",
" [115, 70, 43],\n",
" ...,\n",
" [102, 58, 35],\n",
" [101, 56, 35],\n",
" [100, 55, 34]],\n",
"\n",
" [[111, 67, 43],\n",
" [112, 69, 42],\n",
" [116, 71, 44],\n",
" ...,\n",
" [103, 59, 36],\n",
" [102, 58, 35],\n",
" [101, 57, 34]],\n",
"\n",
" ...,\n",
"\n",
" [[ 63, 66, 94],\n",
" [ 58, 62, 87],\n",
" [ 58, 61, 82],\n",
" ...,\n",
" [ 74, 69, 70],\n",
" [ 70, 68, 68],\n",
" [ 63, 61, 61]],\n",
"\n",
" [[ 57, 62, 87],\n",
" [ 63, 69, 92],\n",
" [ 64, 69, 90],\n",
" ...,\n",
" [ 79, 74, 75],\n",
" [ 76, 72, 71],\n",
" [ 66, 62, 61]],\n",
"\n",
" [[ 61, 67, 90],\n",
" [ 64, 71, 91],\n",
" [ 59, 64, 85],\n",
" ...,\n",
" [ 84, 79, 80],\n",
" [ 84, 80, 79],\n",
" [ 70, 66, 65]]], dtype=uint8)\n",
"orig_shape: (320, 320)\n",
"path: '/content/drive/MyDrive/campus-assistant/dataset/images/test/IMG_2713.JPG'\n",
"probs: None\n",
"save_dir: 'runs/detect/predict'\n",
"speed: {'preprocess': 4.35948371887207, 'inference': 7.105588912963867, 'postprocess': 3121.2780475616455}\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"model.export(format='tflite')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 392
},
"id": "cE3-p6H5uAtc",
"outputId": "4fd0d5a6-316d-406b-d548-044d1f890740"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Ultralytics YOLOv8.2.16 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.00GHz)\n",
"Model summary (fused): 168 layers, 3007988 parameters, 0 gradients, 8.1 GFLOPs\n",
"\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'runs/detect/train13/weights/best.pt' with input shape (1, 3, 320, 320) BCHW and output shape(s) (1, 16, 2100) (5.9 MB)\n",
"\n",
"\u001b[34m\u001b[1mTensorFlow SavedModel:\u001b[0m starting export with tensorflow 2.13.1...\n",
"\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m starting export with onnx 1.16.0 opset 17...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m simplifying with onnxsim 0.4.36...\n",
"\u001b[34m\u001b[1mONNX:\u001b[0m export success ✅ 2.5s, saved as 'runs/detect/train13/weights/best.onnx' (11.6 MB)\n",
"\u001b[34m\u001b[1mTensorFlow SavedModel:\u001b[0m starting TFLite export with onnx2tf 1.17.5...\n",
"\u001b[34m\u001b[1mTensorFlow SavedModel:\u001b[0m export success ✅ 14.0s, saved as 'runs/detect/train13/weights/best_saved_model' (29.1 MB)\n",
"\n",
"\u001b[34m\u001b[1mTensorFlow Lite:\u001b[0m starting export with tensorflow 2.13.1...\n",
"\u001b[34m\u001b[1mTensorFlow Lite:\u001b[0m export success ✅ 0.0s, saved as 'runs/detect/train13/weights/best_saved_model/best_float32.tflite' (11.6 MB)\n",
"\n",
"Export complete (15.8s)\n",
"Results saved to \u001b[1m/content/drive/MyDrive/campus-assistant/runs/detect/train13/weights\u001b[0m\n",
"Predict: yolo predict task=detect model=runs/detect/train13/weights/best_saved_model/best_float32.tflite imgsz=320 \n",
"Validate: yolo val task=detect model=runs/detect/train13/weights/best_saved_model/best_float32.tflite imgsz=320 data=campus_dataset.yaml \n",
"Visualize: https://netron.app\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'runs/detect/train13/weights/best_saved_model/best_float32.tflite'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 3
}
]
}
]
}
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