In [ ]:
In [1]:
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
In [2]:
!pip install ultralytics
Collecting ultralytics Downloading ultralytics-8.3.179-py3-none-any.whl.metadata (37 kB) Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.0.2) Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (3.10.0) Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.12.0.88) Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (11.3.0) Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (6.0.2) Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.32.3) Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.16.1) Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.6.0+cu124) Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (0.21.0+cu124) Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (4.67.1) Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from ultralytics) (5.9.5) Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.11/dist-packages (from ultralytics) (9.0.0) Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.2.2) Collecting ultralytics-thop>=2.0.0 (from ultralytics) Downloading ultralytics_thop-2.0.15-py3-none-any.whl.metadata (14 kB) Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.3) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1) Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.59.0) Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.9) Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (25.0) Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.3) Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.9.0.post0) Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.2) Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.1.4->ultralytics) (2025.2) Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.4.3) Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (3.10) Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2.5.0) Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests>=2.23.0->ultralytics) (2025.8.3) Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.18.0) Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (4.14.1) Requirement already satisfied: networkx in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.5) Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.1.6) Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2025.3.0) Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch>=1.8.0->ultralytics) Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch>=1.8.0->ultralytics) Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch>=1.8.0->ultralytics) Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB) Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch>=1.8.0->ultralytics) Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB) Collecting nvidia-cublas-cu12==12.4.5.8 (from torch>=1.8.0->ultralytics) Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cufft-cu12==11.2.1.3 (from torch>=1.8.0->ultralytics) Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-curand-cu12==10.3.5.147 (from torch>=1.8.0->ultralytics) Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch>=1.8.0->ultralytics) Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB) Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch>=1.8.0->ultralytics) Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB) Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (0.6.2) Collecting nvidia-nccl-cu12==2.21.5 (from torch>=1.8.0->ultralytics) Downloading nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB) Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127) Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch>=1.8.0->ultralytics) Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.2.0) Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (1.13.1) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/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/python3.11/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/python3.11/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.2) Downloading ultralytics-8.3.179-py3-none-any.whl (1.0 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.0/1.0 MB 59.9 MB/s eta 0:00:00 Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 363.4/363.4 MB 3.9 MB/s eta 0:00:00 Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.8/13.8 MB 121.7 MB/s eta 0:00:00 Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 24.6/24.6 MB 92.5 MB/s eta 0:00:00 Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 883.7/883.7 kB 63.1 MB/s eta 0:00:00 Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl (664.8 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 664.8/664.8 MB 2.7 MB/s eta 0:00:00 Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl (211.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 211.5/211.5 MB 5.7 MB/s eta 0:00:00 Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl (56.3 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 56.3/56.3 MB 21.9 MB/s eta 0:00:00 Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 127.9/127.9 MB 7.4 MB/s eta 0:00:00 Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 207.5/207.5 MB 5.9 MB/s eta 0:00:00 Downloading nvidia_nccl_cu12-2.21.5-py3-none-manylinux2014_x86_64.whl (188.7 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 188.7/188.7 MB 6.0 MB/s eta 0:00:00 Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 21.1/21.1 MB 102.7 MB/s eta 0:00:00 Downloading ultralytics_thop-2.0.15-py3-none-any.whl (28 kB) Installing collected packages: 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, ultralytics-thop, ultralytics Attempting uninstall: nvidia-nvjitlink-cu12 Found existing installation: nvidia-nvjitlink-cu12 12.5.82 Uninstalling nvidia-nvjitlink-cu12-12.5.82: Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82 Attempting uninstall: nvidia-nccl-cu12 Found existing installation: nvidia-nccl-cu12 2.23.4 Uninstalling nvidia-nccl-cu12-2.23.4: Successfully uninstalled nvidia-nccl-cu12-2.23.4 Attempting uninstall: nvidia-curand-cu12 Found existing installation: nvidia-curand-cu12 10.3.6.82 Uninstalling nvidia-curand-cu12-10.3.6.82: Successfully uninstalled nvidia-curand-cu12-10.3.6.82 Attempting uninstall: nvidia-cufft-cu12 Found existing installation: nvidia-cufft-cu12 11.2.3.61 Uninstalling nvidia-cufft-cu12-11.2.3.61: Successfully uninstalled nvidia-cufft-cu12-11.2.3.61 Attempting uninstall: nvidia-cuda-runtime-cu12 Found existing installation: nvidia-cuda-runtime-cu12 12.5.82 Uninstalling nvidia-cuda-runtime-cu12-12.5.82: Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82 Attempting uninstall: nvidia-cuda-nvrtc-cu12 Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82 Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82: Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82 Attempting uninstall: nvidia-cuda-cupti-cu12 Found existing installation: nvidia-cuda-cupti-cu12 12.5.82 Uninstalling nvidia-cuda-cupti-cu12-12.5.82: Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82 Attempting uninstall: nvidia-cublas-cu12 Found existing installation: nvidia-cublas-cu12 12.5.3.2 Uninstalling nvidia-cublas-cu12-12.5.3.2: Successfully uninstalled nvidia-cublas-cu12-12.5.3.2 Attempting uninstall: nvidia-cusparse-cu12 Found existing installation: nvidia-cusparse-cu12 12.5.1.3 Uninstalling nvidia-cusparse-cu12-12.5.1.3: Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3 Attempting uninstall: nvidia-cudnn-cu12 Found existing installation: nvidia-cudnn-cu12 9.3.0.75 Uninstalling nvidia-cudnn-cu12-9.3.0.75: Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75 Attempting uninstall: nvidia-cusolver-cu12 Found existing installation: nvidia-cusolver-cu12 11.6.3.83 Uninstalling nvidia-cusolver-cu12-11.6.3.83: Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83 Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.4.127 ultralytics-8.3.179 ultralytics-thop-2.0.15
In [7]:
# @title Ejemplo de entrenamiento
from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# model = YOLO("/content/drive/MyDrive/best_model_50.pth")
# Train the model
# results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
results = model.train(data="/content/drive/MyDrive/BIOMA_VISION_TAREAS/TRABAJO/african-wildlife.yaml", epochs=100, imgsz=640)
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, 84.2MB/s]
Ultralytics 8.3.179 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB)
engine/trainer: agnostic_nms=False, amp=True, augment=False, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/content/drive/MyDrive/BIOMA_VISION_TAREAS/TRABAJO/african-wildlife.yaml, degrees=0.0, deterministic=True, device=None, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, epochs=100, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolo11n.pt, momentum=0.937, mosaic=1.0, multi_scale=False, name=train3, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, save=True, save_conf=False, save_crop=False, save_dir=runs/detect/train3, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None
WARNING ⚠️ Dataset '/content/drive/MyDrive/BIOMA_VISION_TAREAS/TRABAJO/african-wildlife.yaml' images not found, missing path '/content/datasets/african-wildlife/images/val'
Downloading https://ultralytics.com/assets/african-wildlife.zip to '/content/datasets/african-wildlife.zip': 100%|██████████| 100M/100M [00:06<00:00, 16.0MB/s] Unzipping /content/datasets/african-wildlife.zip to /content/datasets/african-wildlife...: 100%|██████████| 3018/3018 [00:00<00:00, 3173.53file/s]
Dataset download success ✅ (8.5s), saved to /content/datasets
Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100%|██████████| 755k/755k [00:00<00:00, 51.6MB/s]
Overriding model.yaml nc=80 with nc=4
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 431452 ultralytics.nn.modules.head.Detect [4, [64, 128, 256]]
YOLO11n summary: 181 layers, 2,590,620 parameters, 2,590,604 gradients, 6.4 GFLOPs Transferred 448/499 items from pretrained weights Freezing layer 'model.23.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks... AMP: checks passed ✅ train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 1358.9±759.7 MB/s, size: 54.6 KB)
train: Scanning /content/datasets/african-wildlife/labels/train... 1052 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1052/1052 [00:00<00:00, 2518.40it/s]
train: New cache created: /content/datasets/african-wildlife/labels/train.cache
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 319.2±129.4 MB/s, size: 42.2 KB)
val: Scanning /content/datasets/african-wildlife/labels/val... 225 images, 0 backgrounds, 0 corrupt: 100%|██████████| 225/225 [00:00<00:00, 1844.23it/s]
val: New cache created: /content/datasets/african-wildlife/labels/val.cache
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=0.00125, momentum=0.9) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0) Image sizes 640 train, 640 val Using 2 dataloader workers Logging results to runs/detect/train3 Starting training for 100 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/100 2.27G 0.854 2.53 1.228 40 640: 100%|██████████| 66/66 [00:22<00:00, 2.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:03<00:00, 2.55it/s]
all 225 379 0.935 0.215 0.703 0.528
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/100 2.79G 0.9463 1.582 1.255 59 640: 100%|██████████| 66/66 [00:21<00:00, 3.06it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.50it/s]
all 225 379 0.543 0.587 0.64 0.433
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/100 2.8G 0.9847 1.451 1.276 70 640: 100%|██████████| 66/66 [00:19<00:00, 3.37it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.83it/s]
all 225 379 0.483 0.571 0.584 0.372
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/100 2.82G 1.021 1.418 1.308 43 640: 100%|██████████| 66/66 [00:20<00:00, 3.28it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.28it/s]
all 225 379 0.751 0.523 0.644 0.428
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/100 2.83G 1.015 1.342 1.292 52 640: 100%|██████████| 66/66 [00:20<00:00, 3.29it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.07it/s]
all 225 379 0.585 0.531 0.525 0.357
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/100 2.85G 0.9398 1.196 1.256 52 640: 100%|██████████| 66/66 [00:19<00:00, 3.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.38it/s]
all 225 379 0.747 0.579 0.701 0.498
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/100 2.86G 0.9536 1.148 1.266 30 640: 100%|██████████| 66/66 [00:20<00:00, 3.18it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.02it/s]
all 225 379 0.753 0.729 0.838 0.58
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/100 2.88G 0.9197 1.064 1.249 39 640: 100%|██████████| 66/66 [00:19<00:00, 3.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.94it/s]
all 225 379 0.743 0.703 0.767 0.539
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/100 2.89G 0.8915 1.061 1.233 75 640: 100%|██████████| 66/66 [00:20<00:00, 3.15it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.37it/s]
all 225 379 0.72 0.667 0.717 0.488
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/100 2.91G 0.9151 1.044 1.231 33 640: 100%|██████████| 66/66 [00:19<00:00, 3.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.73it/s]
all 225 379 0.758 0.723 0.807 0.606
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/100 2.92G 0.9099 0.9984 1.233 54 640: 100%|██████████| 66/66 [00:20<00:00, 3.24it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.66it/s]
all 225 379 0.814 0.631 0.773 0.571
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/100 2.94G 0.876 0.9357 1.214 45 640: 100%|██████████| 66/66 [00:19<00:00, 3.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.87it/s]
all 225 379 0.876 0.774 0.878 0.666
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/100 2.95G 0.8652 0.9193 1.207 45 640: 100%|██████████| 66/66 [00:19<00:00, 3.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 5.19it/s]
all 225 379 0.843 0.747 0.86 0.643
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/100 2.96G 0.8653 0.9211 1.204 59 640: 100%|██████████| 66/66 [00:19<00:00, 3.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.86it/s]
all 225 379 0.735 0.726 0.805 0.577
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/100 2.98G 0.8351 0.8646 1.182 49 640: 100%|██████████| 66/66 [00:19<00:00, 3.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.45it/s]
all 225 379 0.917 0.807 0.91 0.719
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/100 2.99G 0.8238 0.8404 1.183 55 640: 100%|██████████| 66/66 [00:21<00:00, 3.13it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.29it/s]
all 225 379 0.878 0.792 0.883 0.687
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/100 3.01G 0.832 0.7964 1.183 59 640: 100%|██████████| 66/66 [00:19<00:00, 3.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.15it/s]
all 225 379 0.881 0.817 0.899 0.697
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/100 3.02G 0.8121 0.8138 1.17 48 640: 100%|██████████| 66/66 [00:21<00:00, 3.11it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.58it/s]
all 225 379 0.876 0.759 0.881 0.687
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/100 3.04G 0.7989 0.7917 1.148 51 640: 100%|██████████| 66/66 [00:19<00:00, 3.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.86it/s]
all 225 379 0.867 0.774 0.891 0.685
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/100 3.05G 0.8058 0.7794 1.172 56 640: 100%|██████████| 66/66 [00:20<00:00, 3.18it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.90it/s]
all 225 379 0.769 0.739 0.853 0.68
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/100 3.07G 0.795 0.7877 1.162 45 640: 100%|██████████| 66/66 [00:19<00:00, 3.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.74it/s]
all 225 379 0.902 0.806 0.913 0.71
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/100 3.08G 0.7835 0.7891 1.161 52 640: 100%|██████████| 66/66 [00:19<00:00, 3.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.58it/s]
all 225 379 0.914 0.81 0.921 0.713
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/100 3.1G 0.779 0.7457 1.148 52 640: 100%|██████████| 66/66 [00:20<00:00, 3.24it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.39it/s]
all 225 379 0.884 0.804 0.908 0.713
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/100 3.11G 0.7687 0.7369 1.145 51 640: 100%|██████████| 66/66 [00:19<00:00, 3.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.61it/s]
all 225 379 0.833 0.857 0.92 0.726
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/100 3.13G 0.7661 0.7107 1.141 40 640: 100%|██████████| 66/66 [00:20<00:00, 3.17it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.35it/s]
all 225 379 0.918 0.807 0.897 0.689
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/100 3.14G 0.7704 0.7072 1.148 65 640: 100%|██████████| 66/66 [00:19<00:00, 3.37it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 5.03it/s]
all 225 379 0.909 0.816 0.894 0.713
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/100 3.16G 0.7569 0.7058 1.146 50 640: 100%|██████████| 66/66 [00:21<00:00, 3.13it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.56it/s]
all 225 379 0.893 0.84 0.922 0.724
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/100 3.17G 0.7624 0.7167 1.135 60 640: 100%|██████████| 66/66 [00:19<00:00, 3.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.38it/s]
all 225 379 0.846 0.741 0.858 0.682
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/100 3.19G 0.7527 0.695 1.142 43 640: 100%|██████████| 66/66 [00:20<00:00, 3.19it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.59it/s]
all 225 379 0.904 0.777 0.898 0.716
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/100 3.2G 0.755 0.692 1.129 43 640: 100%|██████████| 66/66 [00:19<00:00, 3.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.07it/s]
all 225 379 0.898 0.732 0.861 0.68
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/100 3.22G 0.7449 0.6654 1.127 42 640: 100%|██████████| 66/66 [00:20<00:00, 3.26it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.98it/s]
all 225 379 0.927 0.757 0.908 0.726
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/100 3.23G 0.71 0.6511 1.102 76 640: 100%|██████████| 66/66 [00:20<00:00, 3.27it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.07it/s]
all 225 379 0.921 0.867 0.932 0.746
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/100 3.25G 0.7006 0.6285 1.104 63 640: 100%|██████████| 66/66 [00:19<00:00, 3.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.64it/s]
all 225 379 0.922 0.849 0.927 0.753
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/100 3.26G 0.6942 0.6339 1.094 45 640: 100%|██████████| 66/66 [00:21<00:00, 3.13it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.52it/s]
all 225 379 0.933 0.837 0.912 0.724
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/100 3.28G 0.7121 0.6315 1.102 70 640: 100%|██████████| 66/66 [00:19<00:00, 3.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.03it/s]
all 225 379 0.902 0.85 0.928 0.741
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/100 3.29G 0.6994 0.6241 1.105 54 640: 100%|██████████| 66/66 [00:21<00:00, 3.14it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.17it/s]
all 225 379 0.945 0.845 0.926 0.736
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/100 3.3G 0.6922 0.6126 1.102 47 640: 100%|██████████| 66/66 [00:19<00:00, 3.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.78it/s]
all 225 379 0.927 0.878 0.942 0.762
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/100 3.32G 0.724 0.649 1.12 59 640: 100%|██████████| 66/66 [00:19<00:00, 3.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.32it/s]
all 225 379 0.937 0.828 0.93 0.739
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/100 3.33G 0.7001 0.6021 1.095 47 640: 100%|██████████| 66/66 [00:20<00:00, 3.17it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.65it/s]
all 225 379 0.891 0.851 0.926 0.743
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/100 3.35G 0.7012 0.6058 1.106 43 640: 100%|██████████| 66/66 [00:19<00:00, 3.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 5.05it/s]
all 225 379 0.933 0.863 0.935 0.752
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/100 3.37G 0.6734 0.5793 1.089 51 640: 100%|██████████| 66/66 [00:21<00:00, 3.09it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.55it/s]
all 225 379 0.913 0.852 0.93 0.753
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/100 3.38G 0.6773 0.5841 1.086 47 640: 100%|██████████| 66/66 [00:19<00:00, 3.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.31it/s]
all 225 379 0.9 0.872 0.929 0.763
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/100 3.39G 0.6773 0.5914 1.086 51 640: 100%|██████████| 66/66 [00:20<00:00, 3.19it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.30it/s]
all 225 379 0.898 0.859 0.938 0.749
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/100 3.41G 0.655 0.5574 1.071 53 640: 100%|██████████| 66/66 [00:19<00:00, 3.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:03<00:00, 2.60it/s]
all 225 379 0.939 0.864 0.945 0.762
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/100 3.42G 0.666 0.5656 1.085 80 640: 100%|██████████| 66/66 [00:19<00:00, 3.32it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.76it/s]
all 225 379 0.923 0.849 0.932 0.748
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/100 3.44G 0.6776 0.5723 1.081 50 640: 100%|██████████| 66/66 [00:21<00:00, 3.10it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.12it/s]
all 225 379 0.957 0.862 0.945 0.756
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/100 3.45G 0.6682 0.5656 1.074 36 640: 100%|██████████| 66/66 [00:19<00:00, 3.37it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.58it/s]
all 225 379 0.907 0.87 0.926 0.748
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/100 3.47G 0.6515 0.5534 1.064 58 640: 100%|██████████| 66/66 [00:20<00:00, 3.14it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.58it/s]
all 225 379 0.939 0.871 0.943 0.768
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/100 3.48G 0.6514 0.553 1.071 45 640: 100%|██████████| 66/66 [00:19<00:00, 3.39it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.01it/s]
all 225 379 0.884 0.893 0.941 0.781
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/100 3.5G 0.646 0.5551 1.071 46 640: 100%|██████████| 66/66 [00:20<00:00, 3.26it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.77it/s]
all 225 379 0.934 0.871 0.944 0.77
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
51/100 3.51G 0.6414 0.5427 1.066 57 640: 100%|██████████| 66/66 [00:19<00:00, 3.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.72it/s]
all 225 379 0.912 0.882 0.95 0.761
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
52/100 3.53G 0.6419 0.5403 1.06 53 640: 100%|██████████| 66/66 [00:19<00:00, 3.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.50it/s]
all 225 379 0.924 0.907 0.948 0.777
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
53/100 3.54G 0.6163 0.5171 1.053 65 640: 100%|██████████| 66/66 [00:20<00:00, 3.27it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.10it/s]
all 225 379 0.924 0.876 0.939 0.761
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
54/100 3.56G 0.6259 0.5263 1.051 64 640: 100%|██████████| 66/66 [00:19<00:00, 3.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.51it/s]
all 225 379 0.938 0.841 0.947 0.776
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
55/100 3.57G 0.6298 0.5311 1.053 44 640: 100%|██████████| 66/66 [00:21<00:00, 3.14it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.36it/s]
all 225 379 0.947 0.86 0.943 0.769
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
56/100 3.59G 0.6217 0.5171 1.051 59 640: 100%|██████████| 66/66 [00:19<00:00, 3.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.43it/s]
all 225 379 0.946 0.863 0.941 0.772
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
57/100 3.6G 0.6332 0.521 1.058 45 640: 100%|██████████| 66/66 [00:21<00:00, 3.13it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.56it/s]
all 225 379 0.97 0.878 0.955 0.772
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
58/100 3.62G 0.6069 0.5134 1.047 65 640: 100%|██████████| 66/66 [00:19<00:00, 3.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.48it/s]
all 225 379 0.963 0.881 0.954 0.784
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
59/100 3.63G 0.6138 0.5065 1.037 45 640: 100%|██████████| 66/66 [00:20<00:00, 3.24it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.75it/s]
all 225 379 0.942 0.872 0.938 0.762
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
60/100 3.64G 0.6001 0.5126 1.049 34 640: 100%|██████████| 66/66 [00:19<00:00, 3.30it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:03<00:00, 2.62it/s]
all 225 379 0.932 0.878 0.944 0.777
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
61/100 3.66G 0.6277 0.497 1.05 52 640: 100%|██████████| 66/66 [00:19<00:00, 3.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.28it/s]
all 225 379 0.96 0.913 0.96 0.777
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
62/100 3.68G 0.6118 0.4895 1.042 50 640: 100%|██████████| 66/66 [00:22<00:00, 2.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.51it/s]
all 225 379 0.923 0.892 0.941 0.771
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
63/100 3.69G 0.6074 0.4914 1.048 43 640: 100%|██████████| 66/66 [00:19<00:00, 3.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.49it/s]
all 225 379 0.925 0.905 0.951 0.788
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
64/100 3.71G 0.5919 0.485 1.029 35 640: 100%|██████████| 66/66 [00:20<00:00, 3.16it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.59it/s]
all 225 379 0.948 0.858 0.943 0.783
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
65/100 3.72G 0.5898 0.485 1.029 55 640: 100%|██████████| 66/66 [00:19<00:00, 3.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.94it/s]
all 225 379 0.935 0.911 0.953 0.796
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
66/100 3.73G 0.5897 0.4696 1.034 31 640: 100%|██████████| 66/66 [00:19<00:00, 3.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.53it/s]
all 225 379 0.926 0.909 0.945 0.788
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
67/100 3.75G 0.5819 0.4665 1.029 41 640: 100%|██████████| 66/66 [00:20<00:00, 3.26it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.97it/s]
all 225 379 0.938 0.905 0.955 0.791
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
68/100 3.76G 0.5834 0.4796 1.024 56 640: 100%|██████████| 66/66 [00:19<00:00, 3.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.57it/s]
all 225 379 0.927 0.882 0.938 0.781
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
69/100 3.78G 0.5733 0.4646 1.015 57 640: 100%|██████████| 66/66 [00:21<00:00, 3.13it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.58it/s]
all 225 379 0.955 0.854 0.937 0.789
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
70/100 3.79G 0.5767 0.4675 1.025 56 640: 100%|██████████| 66/66 [00:19<00:00, 3.39it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.98it/s]
all 225 379 0.945 0.907 0.958 0.802
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
71/100 3.81G 0.5628 0.4495 1.015 39 640: 100%|██████████| 66/66 [00:20<00:00, 3.15it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.81it/s]
all 225 379 0.905 0.902 0.946 0.79
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
72/100 3.82G 0.5835 0.4687 1.025 75 640: 100%|██████████| 66/66 [00:19<00:00, 3.39it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.27it/s]
all 225 379 0.946 0.878 0.943 0.788
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
73/100 3.84G 0.5774 0.454 1.033 28 640: 100%|██████████| 66/66 [00:21<00:00, 3.14it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.53it/s]
all 225 379 0.957 0.89 0.948 0.802
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
74/100 3.85G 0.5581 0.4359 1.02 49 640: 100%|██████████| 66/66 [00:19<00:00, 3.35it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.93it/s]
all 225 379 0.954 0.917 0.961 0.802
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
75/100 3.87G 0.5611 0.4483 1.017 53 640: 100%|██████████| 66/66 [00:19<00:00, 3.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.64it/s]
all 225 379 0.967 0.892 0.957 0.805
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
76/100 3.88G 0.5517 0.4407 1.007 54 640: 100%|██████████| 66/66 [00:20<00:00, 3.17it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.77it/s]
all 225 379 0.961 0.889 0.955 0.797
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
77/100 3.9G 0.5578 0.4389 1.013 46 640: 100%|██████████| 66/66 [00:19<00:00, 3.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.61it/s]
all 225 379 0.937 0.897 0.95 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
78/100 3.91G 0.5413 0.4235 1 45 640: 100%|██████████| 66/66 [00:21<00:00, 3.11it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.35it/s]
all 225 379 0.959 0.896 0.952 0.81
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
79/100 3.93G 0.5465 0.417 1.006 47 640: 100%|██████████| 66/66 [00:19<00:00, 3.39it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.17it/s]
all 225 379 0.938 0.917 0.956 0.808
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
80/100 3.94G 0.538 0.4076 1 46 640: 100%|██████████| 66/66 [00:20<00:00, 3.17it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.64it/s]
all 225 379 0.953 0.888 0.948 0.806
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
81/100 3.96G 0.5594 0.4253 1.012 41 640: 100%|██████████| 66/66 [00:19<00:00, 3.38it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.33it/s]
all 225 379 0.973 0.896 0.959 0.818
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
82/100 3.97G 0.5371 0.4227 0.9986 59 640: 100%|██████████| 66/66 [00:19<00:00, 3.34it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.49it/s]
all 225 379 0.963 0.878 0.942 0.809
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
83/100 3.99G 0.5357 0.4088 0.9989 46 640: 100%|██████████| 66/66 [00:19<00:00, 3.33it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.92it/s]
all 225 379 0.94 0.899 0.944 0.801
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
84/100 4G 0.5287 0.4121 0.9998 44 640: 100%|██████████| 66/66 [00:19<00:00, 3.40it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.78it/s]
all 225 379 0.954 0.889 0.951 0.8
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
85/100 4.02G 0.5446 0.4117 1.01 55 640: 100%|██████████| 66/66 [00:20<00:00, 3.15it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.73it/s]
all 225 379 0.959 0.888 0.945 0.803
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
86/100 4.03G 0.5271 0.4017 0.9977 43 640: 100%|██████████| 66/66 [00:19<00:00, 3.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.33it/s]
all 225 379 0.956 0.896 0.95 0.811
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
87/100 4.04G 0.5284 0.403 1.001 55 640: 100%|██████████| 66/66 [00:20<00:00, 3.15it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.78it/s]
all 225 379 0.972 0.894 0.95 0.807
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
88/100 4.06G 0.5322 0.4055 1.002 44 640: 100%|██████████| 66/66 [00:19<00:00, 3.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.59it/s]
all 225 379 0.951 0.915 0.955 0.813
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
89/100 4.07G 0.5075 0.3957 0.985 34 640: 100%|██████████| 66/66 [00:20<00:00, 3.18it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.42it/s]
all 225 379 0.966 0.897 0.95 0.811
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
90/100 4.09G 0.5218 0.3929 0.9985 52 640: 100%|██████████| 66/66 [00:19<00:00, 3.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.25it/s]
all 225 379 0.952 0.908 0.95 0.812
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
91/100 4.1G 0.4338 0.322 0.9121 18 640: 100%|██████████| 66/66 [00:20<00:00, 3.16it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.21it/s]
all 225 379 0.949 0.903 0.938 0.792
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
92/100 4.12G 0.4111 0.2821 0.9059 28 640: 100%|██████████| 66/66 [00:19<00:00, 3.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.92it/s]
all 225 379 0.931 0.909 0.951 0.807
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
93/100 4.13G 0.3973 0.2671 0.8908 17 640: 100%|██████████| 66/66 [00:18<00:00, 3.52it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.53it/s]
all 225 379 0.968 0.889 0.954 0.814
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
94/100 4.15G 0.4004 0.2662 0.9006 23 640: 100%|██████████| 66/66 [00:19<00:00, 3.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.69it/s]
all 225 379 0.966 0.898 0.955 0.813
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
95/100 4.16G 0.3949 0.2663 0.8983 20 640: 100%|██████████| 66/66 [00:18<00:00, 3.50it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.82it/s]
all 225 379 0.953 0.912 0.953 0.814
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
96/100 4.18G 0.3954 0.2673 0.8933 18 640: 100%|██████████| 66/66 [00:19<00:00, 3.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:03<00:00, 2.66it/s]
all 225 379 0.959 0.909 0.956 0.815
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
97/100 4.19G 0.3943 0.2611 0.8901 21 640: 100%|██████████| 66/66 [00:18<00:00, 3.53it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.83it/s]
all 225 379 0.971 0.895 0.955 0.822
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
98/100 4.21G 0.3891 0.2589 0.8864 30 640: 100%|██████████| 66/66 [00:19<00:00, 3.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.80it/s]
all 225 379 0.975 0.898 0.956 0.824
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
99/100 4.22G 0.3871 0.2605 0.8917 23 640: 100%|██████████| 66/66 [00:18<00:00, 3.54it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:01<00:00, 4.79it/s]
all 225 379 0.944 0.921 0.957 0.822
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
100/100 4.24G 0.3791 0.2483 0.882 19 640: 100%|██████████| 66/66 [00:19<00:00, 3.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 3.24it/s]
all 225 379 0.97 0.901 0.957 0.821
100 epochs completed in 0.627 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 8.3.179 🚀 Python-3.11.13 torch-2.6.0+cu124 CUDA:0 (Tesla T4, 15095MiB) YOLO11n summary (fused): 100 layers, 2,582,932 parameters, 0 gradients, 6.3 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 8/8 [00:02<00:00, 2.84it/s]
all 225 379 0.975 0.898 0.956 0.823
buffalo 62 89 0.976 0.899 0.954 0.837
elephant 53 91 0.951 0.879 0.944 0.809
rhino 55 85 0.976 0.965 0.975 0.866
zebra 59 114 0.996 0.851 0.95 0.782
Speed: 0.2ms preprocess, 2.5ms inference, 0.0ms loss, 3.1ms postprocess per image
Results saved to runs/detect/train3
In [ ]:
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
In [8]:
# @title Ejemplo de inferencia
from ultralytics import YOLO
# Load a model
# model = YOLO("path/to/best.pt") # load a brain-tumor fine-tuned model
model = YOLO("yolo11n.pt")
# Inference using the model
results = model.predict("https://ultralytics.com/assets/african-wildlife-sample.jpg")
Downloading https://ultralytics.com/assets/african-wildlife-sample.jpg to 'african-wildlife-sample.jpg': 100%|██████████| 48.5k/48.5k [00:00<00:00, 26.1MB/s]
image 1/1 /content/african-wildlife-sample.jpg: 416x640 7 persons, 1 bench, 1 elephant, 51.9ms Speed: 1.9ms preprocess, 51.9ms inference, 2.3ms postprocess per image at shape (1, 3, 416, 640)
In [9]:
print(results)
[ultralytics.engine.results.Results object with attributes:
boxes: ultralytics.engine.results.Boxes object
keypoints: None
masks: None
names: {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'}
obb: None
orig_img: array([[[129, 144, 136],
[125, 142, 133],
[120, 140, 128],
...,
[232, 231, 235],
[255, 251, 255],
[255, 240, 249]],
[[114, 129, 121],
[118, 135, 124],
[124, 146, 134],
...,
[208, 209, 213],
[255, 243, 251],
[255, 246, 255]],
[[129, 146, 135],
[135, 155, 143],
[147, 171, 159],
...,
[185, 190, 193],
[241, 234, 241],
[255, 251, 255]],
...,
[[236, 231, 232],
[234, 229, 230],
[233, 228, 229],
...,
[223, 219, 208],
[230, 225, 216],
[235, 230, 221]],
[[241, 236, 237],
[241, 236, 237],
[240, 235, 236],
...,
[219, 215, 204],
[228, 223, 214],
[236, 231, 222]],
[[247, 242, 243],
[248, 243, 244],
[248, 243, 244],
...,
[220, 216, 205],
[226, 221, 212],
[232, 227, 218]]], dtype=uint8)
orig_shape: (415, 640)
path: '/content/african-wildlife-sample.jpg'
probs: None
save_dir: 'runs/detect/predict'
speed: {'preprocess': 1.9081989998994686, 'inference': 51.88434199999392, 'postprocess': 2.308413999799086}]
In [10]:
from PIL import Image
# Visualize the results on the image
for r in results:
im_array = r.plot() # plot a BGR numpy array of predictions
im = Image.fromarray(im_array[..., ::-1]) # RGB PIL image
im.save('african-wildlife-predecido.jpg') # save image
display(im) # display image
In [ ]:
# @title Ejemplo
# from ultralytics import YOLO
# Load a model
model = YOLO("yolo11n.pt") # load a pretrained model (recommended for training)
# Train the model
results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
In [12]:
# @title otra prueba
# Load a model
# model = YOLO("path/to/best.pt") # load a brain-tumor fine-tuned model
model = YOLO("yolo11n.pt")
# Inference using the model
results = model.predict("/content/drive/MyDrive/BIOMA_VISION_TAREAS/TRABAJO/african-wildlife/data/test/images/1 (137).jpg")
from PIL import Image
# Visualize the results on the image
for r in results:
im_array = r.plot() # plot a BGR numpy array of predictions
im = Image.fromarray(im_array[..., ::-1]) # RGB PIL image
im.save('african-wildlife-predecido_137.jpg') # save image
display(im) # display image
image 1/1 /content/drive/MyDrive/BIOMA_VISION_TAREAS/TRABAJO/african-wildlife/data/test/images/1 (137).jpg: 640x448 3 persons, 51.6ms Speed: 2.5ms preprocess, 51.6ms inference, 1.7ms postprocess per image at shape (1, 3, 640, 448)
