{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyPQfiHFvygmi4YhaTex1pVc"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y_KEsdtExrBP","executionInfo":{"status":"ok","timestamp":1724878653047,"user_tz":-60,"elapsed":5157,"user":{"displayName":"Mail Cloud","userId":"01708480096028966588"}},"outputId":"26f9e300-5823-4e83-b950-c9601d8aaacd"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ultralytics\n","  Downloading ultralytics-8.2.82-py3-none-any.whl.metadata (41 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/41.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.3/41.3 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sympy->torch>=1.8.0->ultralytics) (1.3.0)\n","Downloading ultralytics-8.2.82-py3-none-any.whl (871 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m871.1/871.1 kB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading ultralytics_thop-2.0.5-py3-none-any.whl (25 kB)\n","Installing collected packages: ultralytics-thop, ultralytics\n","Successfully installed ultralytics-8.2.82 ultralytics-thop-2.0.5\n"]}],"source":["!pip install ultralytics"]},{"cell_type":"code","source":["# !pip install Ipython\n","from ultralytics import YOLO\n","import os\n","\n","# !yolo mode=checks"],"metadata":{"id":"79mnoqsux26z"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!pip install ultralytics==8.0.196"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Eb6mw9gvx8qs","executionInfo":{"status":"ok","timestamp":1725083767003,"user_tz":-60,"elapsed":4632,"user":{"displayName":"Mail Cloud","userId":"01708480096028966588"}},"outputId":"d4e81eef-782c-437b-9fa9-80649bc53cbb"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting ultralytics==8.0.196\n","  Downloading ultralytics-8.0.196-py3-none-any.whl.metadata (31 kB)\n","Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics==8.0.196) (3.7.1)\n","Requirement already satisfied: numpy>=1.22.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics==8.0.196) (1.26.4)\n","Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics==8.0.196) (4.10.0.84)\n","Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics==8.0.196) (9.4.0)\n","Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics==8.0.196) (6.0.2)\n","Requirement already satisfied: requests>=2.23.0 in 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(5.9.5)\n","Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics==8.0.196) (9.0.0)\n","Collecting thop>=0.1.1 (from ultralytics==8.0.196)\n","  Downloading thop-0.1.1.post2209072238-py3-none-any.whl.metadata (2.7 kB)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics==8.0.196) (1.2.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics==8.0.196) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics==8.0.196) (4.53.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics==8.0.196) (1.4.5)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from 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ultralytics-8.0.196\n"]}]},{"cell_type":"code","source":["!pip install roboflow\n","\n","from roboflow import Roboflow\n","rf = Roboflow(api_key=\"fkktVhcIZvphzBoKst4e\")\n","project = rf.workspace(\"human-v2\").project(\"human-dataset-v2\")\n","version = project.version(6)\n","dataset = version.download(\"yolov8\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EJxMxozoyB1R","executionInfo":{"status":"ok","timestamp":1725083830686,"user_tz":-60,"elapsed":28002,"user":{"displayName":"Mail Cloud","userId":"01708480096028966588"}},"outputId":"eda88b57-eb0d-4266-8485-9e539eae0c9d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting roboflow\n","  Downloading roboflow-1.1.44-py3-none-any.whl.metadata (9.7 kB)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from roboflow) (2024.7.4)\n","Collecting idna==3.7 (from roboflow)\n","  Downloading idna-3.7-py3-none-any.whl.metadata (9.9 kB)\n","Requirement already satisfied: cycler in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.12.1)\n","Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.5)\n","Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.7.1)\n","Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.26.4)\n","Requirement already satisfied: opencv-python-headless==4.10.0.84 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.10.0.84)\n","Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (9.4.0)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n","Collecting python-dotenv (from roboflow)\n","  Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.32.3)\n","Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.16.0)\n","Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.0.7)\n","Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.66.5)\n","Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.2)\n","Collecting requests-toolbelt (from roboflow)\n","  Downloading requests_toolbelt-1.0.0-py2.py3-none-any.whl.metadata (14 kB)\n","Collecting filetype (from roboflow)\n","  Downloading filetype-1.2.0-py2.py3-none-any.whl.metadata (6.5 kB)\n","Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.2.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from 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python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n","Downloading requests_toolbelt-1.0.0-py2.py3-none-any.whl (54 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: filetype, python-dotenv, idna, requests-toolbelt, roboflow\n","  Attempting uninstall: idna\n","    Found existing installation: idna 3.8\n","    Uninstalling idna-3.8:\n","      Successfully uninstalled idna-3.8\n","Successfully installed filetype-1.2.0 idna-3.7 python-dotenv-1.0.1 requests-toolbelt-1.0.0 roboflow-1.1.44\n","loading Roboflow workspace...\n","loading Roboflow project...\n"]},{"output_type":"stream","name":"stderr","text":["Downloading Dataset Version Zip in Human-Dataset-v2-6 to yolov8:: 100%|██████████| 404291/404291 [00:16<00:00, 24432.74it/s]"]},{"output_type":"stream","name":"stdout","text":["\n"]},{"output_type":"stream","name":"stderr","text":["\n","Extracting Dataset Version Zip to Human-Dataset-v2-6 in yolov8:: 100%|██████████| 27316/27316 [00:03<00:00, 7581.42it/s]\n"]}]},{"cell_type":"code","source":["!yolo train model=yolov8s.pt data={dataset.location}/data.yaml epochs=20 imgsz=640"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5UtAZ1FLyT02","outputId":"50c030b1-a5d1-4689-9b84-99a7ac9c74c8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt to 'yolov8s.pt'...\n","100% 21.5M/21.5M [00:00<00:00, 332MB/s]\n","/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py:567: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n","  return torch.load(file, map_location='cpu'), file  # load\n","New https://pypi.org/project/ultralytics/8.2.84 available 😃 Update with 'pip install -U ultralytics'\n","Ultralytics YOLOv8.0.196 🚀 Python-3.10.12 torch-2.4.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n","\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8s.pt, data=/content/Human-Dataset-v2-6/data.yaml, epochs=20, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, 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, 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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, stream_buffer=False, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, 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, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n","Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n","100% 755k/755k [00:00<00:00, 38.8MB/s]\n","2024-08-31 05:57:49.267451: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n","2024-08-31 05:57:49.285197: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n","2024-08-31 05:57:49.291097: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","Overriding model.yaml nc=80 with nc=2\n","\n","                   from  n    params  module                                       arguments                     \n","  0                  -1  1       928  ultralytics.nn.modules.conv.Conv             [3, 32, 3, 2]                 \n","  1                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n","  2                  -1  1     29056  ultralytics.nn.modules.block.C2f             [64, 64, 1, True]             \n","  3                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n","  4                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n","  5                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n","  6                  -1  2    788480  ultralytics.nn.modules.block.C2f             [256, 256, 2, True]           \n","  7                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n","  8                  -1  1   1838080  ultralytics.nn.modules.block.C2f             [512, 512, 1, True]           \n","  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 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    591360  ultralytics.nn.modules.block.C2f             [768, 256, 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    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n"," 16                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n"," 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 18                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n"," 19                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n"," 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n"," 21                  -1  1   1969152  ultralytics.nn.modules.block.C2f             [768, 512, 1]                 \n"," 22        [15, 18, 21]  1   2116822  ultralytics.nn.modules.head.Detect           [2, [128, 256, 512]]          \n","Model summary: 225 layers, 11136374 parameters, 11136358 gradients, 28.6 GFLOPs\n","\n","Transferred 349/355 items from pretrained weights\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', 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/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n","100% 6.23M/6.23M [00:00<00:00, 174MB/s]\n","/usr/local/lib/python3.10/dist-packages/ultralytics/nn/tasks.py:567: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n","  return torch.load(file, map_location='cpu'), file  # load\n","WARNING ⚠️ NMS time limit 0.550s exceeded\n","/usr/local/lib/python3.10/dist-packages/ultralytics/utils/checks.py:558: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n","  with torch.cuda.amp.autocast(True):\n","\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n","/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py:238: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.\n","  self.scaler = amp.GradScaler(enabled=self.amp)\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/Human-Dataset-v2-6/train/labels... 10933 images, 20 backgrounds, 0 corrupt: 100% 10933/10933 [00:05<00:00, 1900.92it/s]\n","\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/Human-Dataset-v2-6/train/labels.cache\n","WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 18, len(boxes) = 35514. 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/core/composition.py:161: UserWarning: Got processor for bboxes, but no transform to process it.\n","  self._set_keys()\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","/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/Human-Dataset-v2-6/valid/labels... 1570 images, 5 backgrounds, 0 corrupt: 100% 1570/1570 [00:02<00:00, 733.34it/s] \n","\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/Human-Dataset-v2-6/valid/labels.cache\n","WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 4, len(boxes) = 5440. 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/train/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=0.001667, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n","Image sizes 640 train, 640 val\n","Using 2 dataloader workers\n","Logging results to \u001b[1mruns/detect/train\u001b[0m\n","Starting training for 20 epochs...\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       1/20      4.13G      1.602      1.556      1.517         21        640: 100% 684/684 [04:25<00:00,  2.57it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:21<00:00,  2.32it/s]\n","                   all       1570       5440      0.334      0.273      0.234        0.1\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       2/20      4.53G      1.699      1.628      1.606         26        640: 100% 684/684 [04:15<00:00,  2.67it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:20<00:00,  2.39it/s]\n","                   all       1570       5440      0.784      0.217      0.227      0.105\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       3/20       4.1G       1.67      1.616       1.59         34        640: 100% 684/684 [04:13<00:00,  2.70it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.58it/s]\n","                   all       1570       5440      0.807      0.233      0.262      0.134\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       4/20      4.25G      1.627      1.537      1.562         29        640: 100% 684/684 [04:13<00:00,  2.69it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.62it/s]\n","                   all       1570       5440      0.816      0.236      0.277      0.142\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       5/20      4.43G      1.582      1.468       1.53         40        640: 100% 684/684 [04:16<00:00,  2.67it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.58it/s]\n","                   all       1570       5440      0.814      0.255      0.277      0.146\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       6/20      4.79G      1.546      1.407      1.499         17        640: 100% 684/684 [04:09<00:00,  2.74it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:21<00:00,  2.33it/s]\n","                   all       1570       5440      0.836      0.254      0.293       0.16\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       7/20      4.39G      1.505      1.352      1.478         29        640: 100% 684/684 [04:08<00:00,  2.75it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.60it/s]\n","                   all       1570       5440      0.828      0.278      0.331       0.18\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       8/20       4.5G      1.484       1.32      1.464         35        640: 100% 684/684 [04:10<00:00,  2.74it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.52it/s]\n","                   all       1570       5440      0.847      0.278      0.313       0.17\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","       9/20      4.36G      1.467      1.278      1.447         27        640: 100% 684/684 [04:17<00:00,  2.66it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.63it/s]\n","                   all       1570       5440      0.561      0.333      0.343      0.191\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      10/20       4.6G      1.444      1.232      1.426         29        640: 100% 684/684 [04:13<00:00,  2.70it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.62it/s]\n","                   all       1570       5440      0.847      0.289      0.352        0.2\n","Closing dataloader mosaic\n","/usr/local/lib/python3.10/dist-packages/albumentations/core/composition.py:161: UserWarning: Got processor for bboxes, but no transform to process it.\n","  self._set_keys()\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","/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","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      11/20      4.45G      1.427      1.146      1.438         17        640: 100% 684/684 [04:02<00:00,  2.82it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.58it/s]\n","                   all       1570       5440      0.323      0.375       0.34      0.195\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      12/20      4.17G      1.411      1.099      1.425         19        640: 100% 684/684 [03:54<00:00,  2.92it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.60it/s]\n","                   all       1570       5440      0.848      0.298      0.343      0.199\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      13/20       4.6G      1.379      1.061      1.406         23        640: 100% 684/684 [03:59<00:00,  2.86it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.55it/s]\n","                   all       1570       5440      0.852      0.296      0.345      0.199\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      14/20      4.35G      1.359      1.043       1.39         19        640: 100% 684/684 [03:57<00:00,  2.88it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:18<00:00,  2.66it/s]\n","                   all       1570       5440      0.794      0.325       0.37      0.218\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      15/20       4.2G      1.332     0.9913      1.366         12        640: 100% 684/684 [03:54<00:00,  2.91it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.63it/s]\n","                   all       1570       5440      0.454      0.372      0.416      0.247\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      16/20      4.34G      1.304     0.9675      1.353         13        640: 100% 684/684 [04:01<00:00,  2.83it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:19<00:00,  2.51it/s]\n","                   all       1570       5440      0.754      0.382      0.435      0.269\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      17/20       4.6G      1.285     0.9289      1.337          8        640: 100% 684/684 [04:12<00:00,  2.71it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 50/50 [00:20<00:00,  2.40it/s]\n","                   all       1570       5440      0.774      0.343      0.403      0.247\n","\n","      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n","      18/20      4.55G      1.258     0.8995      1.317          8        640: 100% 684/684 [04:10<00:00,  2.74it/s]\n","                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):  22% 11/50 [00:06<00:22,  1.77it/s]"]}]},{"cell_type":"code","source":[],"metadata":{"id":"DuUmudlTzRXq"},"execution_count":null,"outputs":[]}]}