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Image(filename=f'/content/runs/detect/train/confusion_matrix.png', width=900)
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!nvidia-smi
Sat Dec 23 23:54:13 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |
| N/A 43C P8 9W / 70W | 0MiB / 15360MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
%pip install ultralytics
import ultralytics
ultralytics.checks()
Ultralytics YOLOv8.0.229 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.3/78.2 GB disk)
from ultralytics import YOLO
import os
from IPython.display import display, Image
from IPython import display
display.clear_output()
!yolo mode=checks
Traceback (most recent call last):
File "/usr/local/bin/yolo", line 8, in <module>
sys.exit(entrypoint())
File "/usr/local/lib/python3.10/dist-packages/ultralytics/cfg/__init__.py", line 395, in entrypoint
raise ValueError(f"Invalid 'mode={mode}'. Valid modes are {MODES}.\n{CLI_HELP_MSG}")
ValueError: Invalid 'mode=<module 'ultralytics.utils.checks' from '/usr/local/lib/python3.10/dist-packages/ultralytics/utils/checks.py'>'. Valid modes are ('train', 'val', 'predict', 'export', 'track', 'benchmark').
Arguments received: ['yolo', 'mode=checks']. Ultralytics 'yolo' commands use the following syntax:
yolo TASK MODE ARGS
Where TASK (optional) is one of ('detect', 'segment', 'classify', 'pose')
MODE (required) is one of ('train', 'val', 'predict', 'export', 'track', 'benchmark')
ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
yolo train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
2. Predict a YouTube video using a pretrained segmentation model at image size 320:
yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
3. Val a pretrained detection model at batch-size 1 and image size 640:
yolo val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
4. Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
5. Run special commands:
yolo help
yolo checks
yolo version
yolo settings
yolo copy-cfg
yolo cfg
Docs: https://docs.ultralytics.com
Community: https://community.ultralytics.com
GitHub: https://github.com/ultralytics/ultralytics
!pip install roboflow
from roboflow import Roboflow
rf = Roboflow(api_key="8f60PiLN5qzO0Jq6glkd")
project = rf.workspace("nicolai-hoirup-nielsen").project("cup-detection-v2")
dataset = project.version(3).download("yolov8")
Collecting roboflow
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Collecting opencv-python-headless==4.8.0.74 (from roboflow)
Downloading opencv_python_headless-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.1 MB)
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Downloading supervision-0.17.1-py3-none-any.whl (77 kB)
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Installing collected packages: python-magic, python-dotenv, pyparsing, opencv-python-headless, idna, cycler, chardet, certifi, supervision, requests-toolbelt, roboflow
Attempting uninstall: pyparsing
Found existing installation: pyparsing 3.1.1
Uninstalling pyparsing-3.1.1:
Successfully uninstalled pyparsing-3.1.1
Attempting uninstall: opencv-python-headless
Found existing installation: opencv-python-headless 4.8.1.78
Uninstalling opencv-python-headless-4.8.1.78:
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Attempting uninstall: idna
Found existing installation: idna 3.6
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Successfully uninstalled idna-3.6
Attempting uninstall: cycler
Found existing installation: cycler 0.12.1
Uninstalling cycler-0.12.1:
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Attempting uninstall: chardet
Found existing installation: chardet 5.2.0
Uninstalling chardet-5.2.0:
Successfully uninstalled chardet-5.2.0
Attempting uninstall: certifi
Found existing installation: certifi 2023.11.17
Uninstalling certifi-2023.11.17:
Successfully uninstalled certifi-2023.11.17
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
lida 0.0.10 requires fastapi, which is not installed.
lida 0.0.10 requires kaleido, which is not installed.
lida 0.0.10 requires python-multipart, which is not installed.
lida 0.0.10 requires uvicorn, which is not installed.
Successfully installed certifi-2023.7.22 chardet-4.0.0 cycler-0.10.0 idna-2.10 opencv-python-headless-4.8.0.74 pyparsing-2.4.7 python-dotenv-1.0.0 python-magic-0.4.27 requests-toolbelt-1.0.0 roboflow-1.1.14 supervision-0.17.1
loading Roboflow workspace... loading Roboflow project... Dependency ultralytics==8.0.196 is required but found version=8.0.229, to fix: `pip install ultralytics==8.0.196`
Downloading Dataset Version Zip in Cup-Detection-v2-3 to yolov8:: 100%|██████████| 10788/10788 [00:00<00:00, 66390.07it/s]
Extracting Dataset Version Zip to Cup-Detection-v2-3 in yolov8:: 100%|██████████| 730/730 [00:00<00:00, 8473.69it/s]
!yolo task=detect mode=train model=yolov8m.pt data={dataset.location}/data.yaml epochs=25 imgsz=640
Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt to 'yolov8m.pt'... 100% 49.7M/49.7M [00:00<00:00, 146MB/s] Ultralytics YOLOv8.0.229 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) engine/trainer: task=detect, mode=train, model=yolov8m.pt, data=/content/Cup-Detection-v2-3/data.yaml, epochs=25, time=None, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, 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, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, 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, 26.3MB/s] 2023-12-24 02:16:26.866507: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered 2023-12-24 02:16:26.866565: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered 2023-12-24 02:16:26.867892: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered Overriding model.yaml nc=80 with nc=6 from n params module arguments 0 -1 1 1392 ultralytics.nn.modules.conv.Conv [3, 48, 3, 2] 1 -1 1 41664 ultralytics.nn.modules.conv.Conv [48, 96, 3, 2] 2 -1 2 111360 ultralytics.nn.modules.block.C2f [96, 96, 2, True] 3 -1 1 166272 ultralytics.nn.modules.conv.Conv [96, 192, 3, 2] 4 -1 4 813312 ultralytics.nn.modules.block.C2f [192, 192, 4, True] 5 -1 1 664320 ultralytics.nn.modules.conv.Conv [192, 384, 3, 2] 6 -1 4 3248640 ultralytics.nn.modules.block.C2f [384, 384, 4, True] 7 -1 1 1991808 ultralytics.nn.modules.conv.Conv [384, 576, 3, 2] 8 -1 2 3985920 ultralytics.nn.modules.block.C2f [576, 576, 2, True] 9 -1 1 831168 ultralytics.nn.modules.block.SPPF [576, 576, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 2 1993728 ultralytics.nn.modules.block.C2f [960, 384, 2] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 2 517632 ultralytics.nn.modules.block.C2f [576, 192, 2] 16 -1 1 332160 ultralytics.nn.modules.conv.Conv [192, 192, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 2 1846272 ultralytics.nn.modules.block.C2f [576, 384, 2] 19 -1 1 1327872 ultralytics.nn.modules.conv.Conv [384, 384, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 2 4207104 ultralytics.nn.modules.block.C2f [960, 576, 2] 22 [15, 18, 21] 1 3779170 ultralytics.nn.modules.head.Detect [6, [192, 384, 576]] Model summary: 295 layers, 25859794 parameters, 25859778 gradients, 79.1 GFLOPs Transferred 469/475 items from pretrained weights TensorBoard: Start with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/ Freezing layer 'model.22.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n... Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'... 100% 6.23M/6.23M [00:00<00:00, 106MB/s] WARNING ⚠️ NMS time limit 0.550s exceeded AMP: checks passed ✅ train: Scanning /content/Cup-Detection-v2-3/train/labels... 330 images, 0 backgrounds, 0 corrupt: 100% 330/330 [00:00<00:00, 2153.17it/s] train: New cache created: /content/Cup-Detection-v2-3/train/labels.cache albumentations: Blur(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)) val: Scanning /content/Cup-Detection-v2-3/valid/labels... 19 images, 0 backgrounds, 0 corrupt: 100% 19/19 [00:00<00:00, 1504.10it/s] val: New cache created: /content/Cup-Detection-v2-3/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=0.001, momentum=0.9) with parameter groups 77 weight(decay=0.0), 84 weight(decay=0.0005), 83 bias(decay=0.0) 25 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 1/25 6.86G 1.695 2.859 1.902 69 640: 100% 21/21 [00:14<00:00, 1.42it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:01<00:00, 1.48s/it] all 19 71 0.712 0.59 0.535 0.259 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 2/25 7.02G 1.33 1.497 1.542 83 640: 100% 21/21 [00:10<00:00, 2.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.00it/s] all 19 71 0.602 0.71 0.614 0.34 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 3/25 7.05G 1.33 1.262 1.593 52 640: 100% 21/21 [00:10<00:00, 1.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.29it/s] all 19 71 0.678 0.316 0.403 0.159 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 4/25 7.03G 1.316 1.14 1.569 93 640: 100% 21/21 [00:10<00:00, 1.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.26it/s] all 19 71 0.322 0.668 0.405 0.221 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 5/25 7.05G 1.349 1.066 1.59 67 640: 100% 21/21 [00:10<00:00, 2.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.84it/s] all 19 71 0.289 0.484 0.308 0.158 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 6/25 7.09G 1.283 1.017 1.58 57 640: 100% 21/21 [00:10<00:00, 2.07it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.69it/s] all 19 71 0.537 0.516 0.518 0.268 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 7/25 7.08G 1.233 0.9163 1.506 54 640: 100% 21/21 [00:10<00:00, 2.05it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.16it/s] all 19 71 0.616 0.652 0.712 0.395 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 8/25 7.08G 1.228 0.9206 1.513 67 640: 100% 21/21 [00:10<00:00, 1.95it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.38it/s] all 19 71 0.795 0.553 0.688 0.397 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 9/25 7.06G 1.194 0.8926 1.495 73 640: 100% 21/21 [00:11<00:00, 1.84it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.75it/s] all 19 71 0.876 0.651 0.711 0.369 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 10/25 7.07G 1.147 0.822 1.452 67 640: 100% 21/21 [00:10<00:00, 1.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.09it/s] all 19 71 0.911 0.794 0.799 0.523 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 11/25 7.04G 1.121 0.8286 1.442 51 640: 100% 21/21 [00:11<00:00, 1.90it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.86it/s] all 19 71 0.938 0.78 0.804 0.408 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 12/25 7.09G 1.112 0.7667 1.44 81 640: 100% 21/21 [00:10<00:00, 1.93it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.97it/s] all 19 71 0.929 0.732 0.802 0.531 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 13/25 7.05G 1.091 0.7576 1.435 74 640: 100% 21/21 [00:10<00:00, 1.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.19it/s] all 19 71 0.91 0.705 0.781 0.406 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 14/25 7.07G 1.093 0.7322 1.428 53 640: 100% 21/21 [00:10<00:00, 2.01it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.97it/s] all 19 71 0.92 0.754 0.806 0.479 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 15/25 7.07G 1.04 0.7014 1.345 76 640: 100% 21/21 [00:10<00:00, 1.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.32it/s] all 19 71 0.971 0.811 0.823 0.482 Closing dataloader mosaic albumentations: Blur(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)) Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 16/25 7.03G 0.9586 0.5895 1.373 35 640: 100% 21/21 [00:13<00:00, 1.58it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.89it/s] all 19 71 0.91 0.784 0.816 0.55 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 17/25 7G 0.8901 0.5413 1.361 26 640: 100% 21/21 [00:10<00:00, 1.99it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.68it/s] all 19 71 0.961 0.781 0.819 0.474 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 18/25 7.04G 0.9469 0.5527 1.409 36 640: 100% 21/21 [00:10<00:00, 1.97it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.28it/s] all 19 71 0.939 0.798 0.807 0.402 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 19/25 7.04G 0.902 0.5102 1.36 36 640: 100% 21/21 [00:10<00:00, 1.94it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.44it/s] all 19 71 0.954 0.823 0.815 0.533 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 20/25 7.05G 0.8322 0.4967 1.305 29 640: 100% 21/21 [00:10<00:00, 1.95it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.34it/s] all 19 71 0.968 0.829 0.832 0.508 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 21/25 7.01G 0.7856 0.4515 1.266 36 640: 100% 21/21 [00:10<00:00, 1.96it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.41it/s] all 19 71 0.979 0.816 0.821 0.551 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 22/25 7.02G 0.7648 0.4342 1.264 32 640: 100% 21/21 [00:10<00:00, 1.95it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.19it/s] all 19 71 0.98 0.797 0.818 0.536 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 23/25 7.03G 0.7326 0.4127 1.231 28 640: 100% 21/21 [00:11<00:00, 1.82it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.20it/s] all 19 71 0.98 0.818 0.822 0.505 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 24/25 7.05G 0.718 0.3998 1.217 31 640: 100% 21/21 [00:10<00:00, 1.94it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.45it/s] all 19 71 0.967 0.8 0.809 0.527 Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size 25/25 7G 0.7035 0.3788 1.217 29 640: 100% 21/21 [00:10<00:00, 1.92it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.07it/s] all 19 71 0.952 0.803 0.807 0.538 25 epochs completed in 0.095 hours. Optimizer stripped from runs/detect/train/weights/last.pt, 52.0MB Optimizer stripped from runs/detect/train/weights/best.pt, 52.0MB Validating runs/detect/train/weights/best.pt... Ultralytics YOLOv8.0.229 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) Model summary (fused): 218 layers, 25843234 parameters, 0 gradients, 78.7 GFLOPs Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.14it/s] all 19 71 0.979 0.816 0.821 0.551 Cocio Cup 19 16 0.982 1 0.995 0.742 Cup 19 11 1 0.895 0.976 0.537 Halloween cup 19 14 0.917 1 0.967 0.581 Hand painted cup 19 15 0.993 1 0.995 0.738 White Cup 19 14 0.985 1 0.995 0.706 cup 19 1 1 0 0 0 Speed: 0.3ms preprocess, 10.7ms inference, 0.0ms loss, 1.0ms postprocess per image Results saved to runs/detect/train 💡 Learn more at https://docs.ultralytics.com/modes/train
Image(filename=f'/content/runs/detect/train/confusion_matrix.png', width=900)
Image(filename=f'/content/runs/detect/train/results.png', width=800)
!yolo task=detect mode=val model=/content/runs/detect/train/weights/best.pt data={dataset.location}/data.yaml
Ultralytics YOLOv8.0.229 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB) Model summary (fused): 218 layers, 25843234 parameters, 0 gradients, 78.7 GFLOPs val: Scanning /content/Cup-Detection-v2-3/valid/labels.cache... 19 images, 0 backgrounds, 0 corrupt: 100% 19/19 [00:00<?, ?it/s] Class Images Instances Box(P R mAP50 mAP50-95): 100% 2/2 [00:01<00:00, 1.16it/s] all 19 71 0.979 0.816 0.821 0.552 Cocio Cup 19 16 0.981 1 0.995 0.743 Cup 19 11 1 0.896 0.976 0.553 Halloween cup 19 14 0.916 1 0.967 0.576 Hand painted cup 19 15 0.993 1 0.995 0.738 White Cup 19 14 0.984 1 0.995 0.703 cup 19 1 1 0 0 0 Speed: 0.2ms preprocess, 40.3ms inference, 0.0ms loss, 30.1ms postprocess per image Results saved to runs/detect/val3 💡 Learn more at https://docs.ultralytics.com/modes/val
!yolo task=detect mode=predict model=/content/runs/detect/train/weights/best.pt conf=0.25 source={dataset.location}/test/images
Ultralytics YOLOv8.0.229 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)
Model summary (fused): 218 layers, 25843234 parameters, 0 gradients, 78.7 GFLOPs
image 1/10 /content/Cup-Detection-v2-3/test/images/img10_png.rf.1fd0fcc2f6a23a301d61081cc2d4073a.jpg: 640x640 1 Cocio Cup, 1 Cup, 1 Halloween cup, 1 Hand painted cup, 1 White Cup, 37.2ms
image 2/10 /content/Cup-Detection-v2-3/test/images/img1_png.rf.5daff3e408e1fd5975496a089b6d8229.jpg: 640x640 1 Cocio Cup, 1 Cup, 1 Halloween cup, 1 Hand painted cup, 1 White Cup, 37.1ms
image 3/10 /content/Cup-Detection-v2-3/test/images/img34_png.rf.bda5b0247fd2b04401cd4bf31084c623.jpg: 640x640 1 Cup, 1 Halloween cup, 1 Hand painted cup, 1 White Cup, 37.1ms
image 4/10 /content/Cup-Detection-v2-3/test/images/img43_png.rf.7760ddd4d75dc1bdda41cefd09cd3ffb.jpg: 640x640 1 Cocio Cup, 1 Cup, 1 Halloween cup, 1 Hand painted cup, 1 White Cup, 37.1ms
image 5/10 /content/Cup-Detection-v2-3/test/images/img59_png.rf.5fd0f9c99e67da7dc735600e9f07b33b.jpg: 640x640 1 Cocio Cup, 1 Halloween cup, 1 Hand painted cup, 1 White Cup, 30.8ms
image 6/10 /content/Cup-Detection-v2-3/test/images/img63_png.rf.67176af0b660c93f923f2d6c79fdf77c.jpg: 640x640 2 Halloween cups, 1 Hand painted cup, 1 White Cup, 1 cup, 24.8ms
image 7/10 /content/Cup-Detection-v2-3/test/images/img87_png.rf.b5c2f3e331144f024d7a64e26d5ca6b6.jpg: 640x640 1 White Cup, 24.8ms
image 8/10 /content/Cup-Detection-v2-3/test/images/img8_png.rf.4da077377f8f68f1f9a92ffd81d0aaf5.jpg: 640x640 1 Cocio Cup, 1 Cup, 1 Halloween cup, 1 Hand painted cup, 1 White Cup, 24.8ms
image 9/10 /content/Cup-Detection-v2-3/test/images/img90_png.rf.c3b09d58c4a52dd05f540e06f2a15bb1.jpg: 640x640 1 Cocio Cup, 1 cup, 24.8ms
image 10/10 /content/Cup-Detection-v2-3/test/images/img92_png.rf.6da9612f891b02a886793f9b6c7a1a94.jpg: 640x640 1 Cocio Cup, 19.8ms
Speed: 1.7ms preprocess, 29.8ms inference, 53.5ms postprocess per image at shape (1, 3, 640, 640)
Results saved to runs/detect/predict4
💡 Learn more at https://docs.ultralytics.com/modes/predict
import glob
from IPython.display import Image, display
for image_path in glob.glob(f'/content/runs/detect/predict4/*.jpg')[:3]:
display(Image(filename=image_path,height=600))
print("\n")
