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AI_딥 러닝_시각지능

AI_파이썬_시각지능_CNN_YOLO_Roboflow_createdDataset

tweety1625 2025. 1. 2. 21:25

Roboflow에서 가져온 데이터셋 사용해보기

1.데이터셋

  • 데이터셋 라이브러리 설치하기
!pip install roboflow
Collecting roboflow
  Downloading roboflow-1.1.50-py3-none-any.whl.metadata (9.7 kB)
Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from roboflow) (2024.12.14)
Collecting idna==3.7 (from roboflow)
  Downloading idna-3.7-py3-none-any.whl.metadata (9.9 kB)
Requirement already satisfied: cycler in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.12.1)
Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.7)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.8.0)
Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.26.4)
Requirement already satisfied: opencv-python-headless==4.10.0.84 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.10.0.84)
Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (11.0.0)
Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)
Collecting python-dotenv (from roboflow)
  Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)
Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.32.3)
Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.17.0)
Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.2.3)
Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.67.1)
Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.2)
Requirement already satisfied: requests-toolbelt in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)
Collecting filetype (from roboflow)
  Downloading filetype-1.2.0-py2.py3-none-any.whl.metadata (6.5 kB)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.3.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.55.3)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (24.2)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (3.2.0)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.4.0)
Downloading roboflow-1.1.50-py3-none-any.whl (81 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 81.5/81.5 kB 6.1 MB/s eta 0:00:00
Downloading idna-3.7-py3-none-any.whl (66 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 66.8/66.8 kB 5.8 MB/s eta 0:00:00
Downloading filetype-1.2.0-py2.py3-none-any.whl (19 kB)
Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)
Installing collected packages: filetype, python-dotenv, idna, roboflow
  Attempting uninstall: idna
    Found existing installation: idna 3.10
    Uninstalling idna-3.10:
      Successfully uninstalled idna-3.10
Successfully installed filetype-1.2.0 idna-3.7 python-dotenv-1.0.1 roboflow-1.1.50
  • 데이터셋 라이브러리 불러오기 
from roboflow import Roboflow
  • 데이터셋 설치하기
#############################################
## API key는 유저마다 다른 것이 정상!!
rf = Roboflow(api_key="")
#############################################
project = rf.workspace("roboflow-58fyf").project("rock-paper-scissors-sxsw")
version = project.version(14)
dataset = version.download("yolov11")
loading Roboflow workspace...
loading Roboflow project...
Downloading Dataset Version Zip in rock-paper-scissors-14 to yolov11:: 100%|██████████| 232517/232517 [00:06<00:00, 36700.46it/s]
Extracting Dataset Version Zip to rock-paper-scissors-14 in yolov11:: 100%|██████████| 14682/14682 [00:02<00:00, 5177.37it/s]

2.모델링

  • 모델링 라이브러리 설치하기
!pip install ultralytics
Collecting ultralytics
  Downloading ultralytics-8.3.56-py3-none-any.whl.metadata (35 kB)
Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.26.4)
Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.8.0)
Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.10.0.84)
Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (11.0.0)
Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.2)
Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.32.3)
Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.13.1)
Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.5.1+cu121)
Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.20.1+cu121)
Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.67.1)
Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)
Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)
Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.2.2)
Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.2)
Collecting ultralytics-thop>=2.0.0 (from ultralytics)
  Downloading ultralytics_thop-2.0.13-py3-none-any.whl.metadata (9.4 kB)
Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.1)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)
Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.55.3)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.7)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.2)
Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.0)
Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.2)
Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.2)
Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.4.0)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.7)
Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.2.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2024.12.14)
Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.16.1)
Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.12.2)
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Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.13.1)
Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.8.0->ultralytics) (1.3.0)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)
Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.2)
Downloading ultralytics-8.3.56-py3-none-any.whl (904 kB)
   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 904.4/904.4 kB 28.8 MB/s eta 0:00:00
Downloading ultralytics_thop-2.0.13-py3-none-any.whl (26 kB)
Installing collected packages: ultralytics-thop, ultralytics
Successfully installed ultralytics-8.3.56 ultralytics-thop-2.0.13
  • 모델링 라이브러리 불러오기
from ultralytics import YOLO, settings
Creating new Ultralytics Settings v0.0.6 file ✅ 
View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'
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.
  • 데이터셋 경로 수정
settings
{'settings_version': '0.0.6',
 'datasets_dir': '/content/datasets',
 'weights_dir': 'weights',
 'runs_dir': 'runs',
 'uuid': '569f3ba64b326db489132663f79cd37279811de477381b83ac131e6cdd129cbb',
 'sync': True,
 'api_key': '',
 'openai_api_key': '',
 'clearml': True,
 'comet': True,
 'dvc': True,
 'hub': True,
 'mlflow': True,
 'neptune': True,
 'raytune': True,
 'tensorboard': True,
 'wandb': False,
 'vscode_msg': True}
settings['datasets_dir'] = '/content/'
settings.update()
settings
{'settings_version': '0.0.6',
 'datasets_dir': '/content/',
 'weights_dir': 'weights',
 'runs_dir': 'runs',
 'uuid': '569f3ba64b326db489132663f79cd37279811de477381b83ac131e6cdd129cbb',
 'sync': True,
 'api_key': '',
 'openai_api_key': '',
 'clearml': True,
 'comet': True,
 'dvc': True,
 'hub': True,
 'mlflow': True,
 'neptune': True,
 'raytune': True,
 'tensorboard': True,
 'wandb': False,
 'vscode_msg': True}

2) 모델 구조만 빌려와서 사용해보기 (권장하지 않음)

  • 모델 구조 선택하기
model_scratch = YOLO(model='yolo11n.pt', task='detect')
Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...
100%|██████████| 5.35M/5.35M [00:00<00:00, 76.5MB/s]
  • 모델 학습하기
results_train = model_scratch.train(model='/content/yolo11n.pt',
                                    data='/content/rock-paper-scissors-14/data.yaml',
                                    epochs=1,
                                    patience=5,
                                    seed=2024,
                                    pretrained=False,
                                    )
Ultralytics 8.3.56 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
engine/trainer: task=detect, mode=train, model=/content/yolo11n.pt, data=/content/rock-paper-scissors-14/data.yaml, epochs=1, time=None, patience=5, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=False, optimizer=auto, verbose=True, seed=2024, 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=None, 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, 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/train
Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...
100%|██████████| 755k/755k [00:00<00:00, 21.0MB/s]
Overriding model.yaml nc=80 with nc=3

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      
  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     
  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]                 
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]          
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]           
 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]          
 20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]           
 23        [16, 19, 22]  1    431257  ultralytics.nn.modules.head.Detect           [3, [64, 128, 256]]           
YOLO11n summary: 319 layers, 2,590,425 parameters, 2,590,409 gradients, 6.4 GFLOPs

Transferred 448/499 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Scanning /content/rock-paper-scissors-14/train/labels... 6455 images, 2516 backgrounds, 0 corrupt: 100%|██████████| 6455/6455 [00:02<00:00, 2247.28it/s]
train: New cache created: /content/rock-paper-scissors-14/train/labels.cache
albumentations: Blur(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=(1.0, 4.0), tile_grid_size=(8, 8))
A new version of Albumentations is available: 1.4.24 (you have 1.4.20). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.
val: Scanning /content/rock-paper-scissors-14/valid/labels... 576 images, 238 backgrounds, 0 corrupt: 100%|██████████| 576/576 [00:00<00:00, 1123.84it/s]
val: New cache created: /content/rock-paper-scissors-14/valid/labels.cache
Plotting labels to runs/detect/train/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.001429, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)
TensorBoard: model graph visualization added ✅
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/detect/train
Starting training for 1 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        1/1      2.47G        1.3      3.104      1.453          7        640: 100%|██████████| 404/404 [02:18<00:00,  2.93it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:08<00:00,  2.11it/s]
                   all        576        400      0.402      0.319      0.311      0.177

1 epochs completed in 0.043 hours.
Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB
Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB

Validating runs/detect/train/weights/best.pt...
Ultralytics 8.3.56 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
YOLO11n summary (fused): 238 layers, 2,582,737 parameters, 0 gradients, 6.3 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.12it/s]
                   all        576        400      0.404      0.319      0.311      0.177
                 Paper        132        139      0.197     0.0576     0.0796     0.0338
                  Rock        121        141      0.466      0.574       0.48       0.28
              Scissors        116        120      0.551      0.325      0.374      0.218
Speed: 0.2ms preprocess, 2.5ms inference, 0.0ms loss, 2.6ms postprocess per image
Results saved to runs/detect/train
  • 예측해보기
image_path = 'https://cdn.crowdpic.net/detail-thumb/thumb_d_7CBEBAEF3FFE8DD37E4912332908E40F.png'
results_pred = model_scratch.predict(source=image_path, stream=False, save=True)
Downloading https://cdn.crowdpic.net/detail-thumb/thumb_d_7CBEBAEF3FFE8DD37E4912332908E40F.png to 'thumb_d_7CBEBAEF3FFE8DD37E4912332908E40F.png'...
100%|██████████| 240k/240k [00:00<00:00, 695kB/s]
image 1/1 /content/thumb_d_7CBEBAEF3FFE8DD37E4912332908E40F.png: 448x640 2 Papers, 2 Scissorss, 49.1ms
Speed: 2.2ms preprocess, 49.1ms inference, 1.5ms postprocess per image at shape (1, 3, 448, 640)
Results saved to runs/detect/train2
results_pred
[ultralytics.engine.results.Results object with attributes:

 boxes: ultralytics.engine.results.Boxes object
 keypoints: None
 masks: None
 names: {0: 'Paper', 1: 'Rock', 2: 'Scissors'}
 obb: None
 orig_img: array([[[255, 255, 255],
         [255, 255, 255],
         [255, 255, 255],
         ...,
         [255, 255, 255],
         [255, 255, 255],
         [255, 255, 255]],

        [[255, 255, 255],
         [255, 255, 255],
         [255, 255, 255],
         ...,
         [255, 255, 255],
         [255, 255, 255],
         [255, 255, 255]],

        [[255, 255, 255],
         [255, 255, 255],
         [255, 255, 255],
         ...,
         [255, 255, 255],
         [255, 255, 255],
         [255, 255, 255]],

        ...,

        [[153, 179, 210],
         [152, 178, 209],
         [156, 180, 211],
         ...,
         [190, 209, 233],
         [184, 206, 231],
         [184, 205, 232]],

        [[151, 177, 208],
         [158, 182, 212],
         [165, 187, 214],
         ...,
         [191, 209, 232],
         [193, 212, 235],
         [188, 209, 233]],

        [[163, 186, 213],
         [164, 187, 213],
         [162, 185, 213],
         ...,
         [188, 206, 230],
         [192, 209, 232],
         [193, 211, 233]]], dtype=uint8)
 orig_shape: (650, 975)
 path: '/content/thumb_d_7CBEBAEF3FFE8DD37E4912332908E40F.png'
 probs: None
 save_dir: 'runs/detect/train2'
 speed: {'preprocess': 2.193927764892578, 'inference': 49.08275604248047, 'postprocess': 1.5325546264648438}]

3) 모델 구조에 사전 학습 가중치도 가져와서 사용해보기

  • 모델 구조 및 사전 학습 가중치 선택하기
model_transfer = YOLO('yolo11n.pt')
  • 모델 학습하기
results_train = model_transfer.train(model='/content/yolov11n.pt',
                                     data='/content/rock-paper-scissors-14/data.yaml',
                                     epochs=1,
                                     seed=2024,
                                     pretrained=True,
                                     )
Ultralytics 8.3.56 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
engine/trainer: task=detect, mode=train, model=/content/yolov11n.pt, data=/content/rock-paper-scissors-14/data.yaml, epochs=1, time=None, patience=100, batch=16, imgsz=640, 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=2024, 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=None, 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, 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/train3
Overriding model.yaml nc=80 with nc=3

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      
  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     
  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]                 
 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]          
 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]           
 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]          
 20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]           
 23        [16, 19, 22]  1    431257  ultralytics.nn.modules.head.Detect           [3, [64, 128, 256]]           
YOLO11n summary: 319 layers, 2,590,425 parameters, 2,590,409 gradients, 6.4 GFLOPs

Transferred 448/499 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train3', view at http://localhost:6006/
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Scanning /content/rock-paper-scissors-14/train/labels.cache... 6455 images, 2516 backgrounds, 0 corrupt: 100%|██████████| 6455/6455 [00:00<?, ?it/s]
albumentations: Blur(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=(1.0, 4.0), tile_grid_size=(8, 8))
val: Scanning /content/rock-paper-scissors-14/valid/labels.cache... 576 images, 238 backgrounds, 0 corrupt: 100%|██████████| 576/576 [00:00<?, ?it/s]
Plotting labels to runs/detect/train3/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.001429, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0)
TensorBoard: model graph visualization added ✅
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/detect/train3
Starting training for 1 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        1/1       2.6G        1.3      3.104      1.453          7        640: 100%|██████████| 404/404 [02:24<00:00,  2.79it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:07<00:00,  2.28it/s]
                   all        576        400      0.402      0.319      0.311      0.177

1 epochs completed in 0.045 hours.
Optimizer stripped from runs/detect/train3/weights/last.pt, 5.5MB
Optimizer stripped from runs/detect/train3/weights/best.pt, 5.5MB

Validating runs/detect/train3/weights/best.pt...
Ultralytics 8.3.56 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
YOLO11n summary (fused): 238 layers, 2,582,737 parameters, 0 gradients, 6.3 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 18/18 [00:05<00:00,  3.14it/s]
                   all        576        400      0.404      0.319      0.311      0.177
                 Paper        132        139      0.197     0.0576     0.0796     0.0338
                  Rock        121        141      0.466      0.574       0.48       0.28
              Scissors        116        120      0.551      0.325      0.374      0.218
Speed: 0.3ms preprocess, 2.5ms inference, 0.0ms loss, 2.7ms postprocess per image
Results saved to runs/detect/train3
  • 예측해보기
image_path = 'https://cdn.crowdpic.net/detail-thumb/thumb_d_7CBEBAEF3FFE8DD37E4912332908E40F.png'
results_pred = model_transfer.predict(source=image_path,
                                      save=True,
                                      )
Found https://cdn.crowdpic.net/detail-thumb/thumb_d_7CBEBAEF3FFE8DD37E4912332908E40F.png locally at thumb_d_7CBEBAEF3FFE8DD37E4912332908E40F.png
image 1/1 /content/thumb_d_7CBEBAEF3FFE8DD37E4912332908E40F.png: 448x640 2 Papers, 2 Scissorss, 18.6ms
Speed: 5.1ms preprocess, 18.6ms inference, 2.1ms postprocess per image at shape (1, 3, 448, 640)
Results saved to runs/detect/train32

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