以下の手順による。
DIR="/content/tmp"
!mkdir $DIR
%cd $DIR
!git clone https://github.com/ultralytics/yolov5
%cd yolov5
!pip install -qr requirements.txt
!wget https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
!unzip coco128.zip
!python train.py --img 640 --batch 16 --epochs 300 --data coco128.yaml --weights yolov5x.pt
train: weights=yolov5x.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=300, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False,
noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False,
quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-66-g9650f16 Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)
hyperparameters: 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=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0,
fl_gamma=0.0, 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
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir runs/train', view at http://localhost:6006/
Dataset not found ⚠️, missing paths ['/content/tmp/datasets/coco128/images/train2017']
Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...
100% 6.66M/6.66M [00:00<00:00, 203MB/s]
Dataset download success ✅ (0.5s), saved to /content/tmp/datasets
Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...
100% 755k/755k [00:00<00:00, 46.2MB/s]
Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt to yolov5x.pt...
100% 166M/166M [00:15<00:00, 10.9MB/s]
from n params module arguments
0 -1 1 8800 models.common.Conv [3, 80, 6, 2, 2]
1 -1 1 115520 models.common.Conv [80, 160, 3, 2]
2 -1 4 309120 models.common.C3 [160, 160, 4]
3 -1 1 461440 models.common.Conv [160, 320, 3, 2]
4 -1 8 2259200 models.common.C3 [320, 320, 8]
5 -1 1 1844480 models.common.Conv [320, 640, 3, 2]
6 -1 12 13125120 models.common.C3 [640, 640, 12]
7 -1 1 7375360 models.common.Conv [640, 1280, 3, 2]
8 -1 4 19676160 models.common.C3 [1280, 1280, 4]
9 -1 1 4099840 models.common.SPPF [1280, 1280, 5]
10 -1 1 820480 models.common.Conv [1280, 640, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 4 5332480 models.common.C3 [1280, 640, 4, False]
14 -1 1 205440 models.common.Conv [640, 320, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 4 1335040 models.common.C3 [640, 320, 4, False]
18 -1 1 922240 models.common.Conv [320, 320, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 4 4922880 models.common.C3 [640, 640, 4, False]
21 -1 1 3687680 models.common.Conv [640, 640, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 4 19676160 models.common.C3 [1280, 1280, 4, False]
24 [17, 20, 23] 1 571965 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [320, 640, 1280]]
Model summary: 445 layers, 86749405 parameters, 86749405 gradients, 206.3 GFLOPs
Transferred 745/745 items from yolov5x.pt
AMP: checks passed ✅
optimizer: SGD(lr=0.01) with parameter groups 123 weight(decay=0.0), 126 weight(decay=0.0005), 126 bias
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))
train: Scanning /content/tmp/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1758.95it/s]
train: New cache created: /content/tmp/datasets/coco128/labels/train2017.cache
val: Scanning /content/tmp/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]
AutoAnchor: 4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅
Plotting labels to runs/train/exp/labels.jpg...
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to runs/train/exp
Starting training for 300 epochs...
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
0/299 13.8G 0.03571 0.06081 0.01071 232 640: 100% 8/8 [00:13<00:00, 1.71s/it]
Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:04<00:00, 1.15s/it]
all 128 929 0.808 0.714 0.812 0.615
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
1/299 13.8G 0.03577 0.05759 0.01104 201 640: 100% 8/8 [00:08<00:00, 1.10s/it]
Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:03<00:00, 1.19it/s]
all 128 929 0.815 0.755 0.842 0.651
Epoch GPU_mem box_loss obj_loss cls_loss Instances Size
2/299 14.1G 0.03302 0.05204 0.009551 227 640: 100% 8/8 [00:08<00:00, 1.09s/it]
Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:03<00:00, 1.27it/s]
all 128 929 0.839 0.775 0.857 0.66
...
