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!python train.py --img 460 --batch 40 --epochs 10 --data /content/drive/MyDrive/Rose/Dataset/Dataset/data.yaml --cfg ./models/yolov5s.yaml --weights yolov5s.pt --name result

train: weights=yolov5s.pt, cfg=./models/yolov5s.yaml, data=/content/drive/MyDrive/Rose/Dataset/Dataset/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=10, batch_size=40, imgsz=460, 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=result, 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 ✅ requirements: /content/drive/MyDrive/Rose/requirements.txt not found, check failed. YOLOv5 🚀 v7.0-162-gc3e4e94 Python-3.10.11 torch-2.0.0+cu118 CPU

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/ Overriding model.yaml nc=80 with nc=8

             from  n    params  module                                  arguments

0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
10 -1 1 131584 models.common.Conv [512, 256, 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 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 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 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 35061 models.yolo.Detect [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]] YOLOv5s summary: 214 layers, 7041205 parameters, 7041205 gradients, 16.0 GFLOPs

Transferred 342/349 items from yolov5s.pt WARNING ⚠️ --img-size 460 must be multiple of max stride 32, updating to 480 optimizer: SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.000625), 60 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/drive/MyDrive/Rose/Dataset/Dataset/train/labels/─▄├╩... 567 images, 81 backgrounds, 0 corrupt: 100% 648/648 [00:07<00:00, 87.67it/s] train: New cache created: /content/drive/MyDrive/Rose/Dataset/Dataset/train/labels/─▄├╩.cache val: Scanning /content/drive/MyDrive/Rose/Dataset/Dataset/valid/labels/─▄├╩... 121 images, 11 backgrounds, 0 corrupt: 100% 132/132 [00:01<00:00, 71.61it/s] val: New cache created: /content/drive/MyDrive/Rose/Dataset/Dataset/valid/labels/─▄├╩.cache

AutoAnchor: 3.31 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Plotting labels to runs/train/result/labels.jpg... Image sizes 480 train, 480 val Using 2 dataloader workers Logging results to runs/train/result Starting training for 10 epochs...

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    0/9         0G     0.1116    0.01821    0.06235         69        480:   0% 0/17 [02:29<?, ?it/s]WARNING ⚠️ TensorBoard graph visualization failure Sizes of tensors must match except in dimension 1. Expected size 30 but got size 29 for tensor number 1 in the list.
    0/9         0G     0.1071    0.02038     0.0638         22        480: 100% 17/17 [14:27<00:00, 51.04s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:13<00:00, 36.99s/it]
               all        132        121    0.00301      0.766     0.0168    0.00394

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    1/9         0G    0.07277    0.02185    0.05991         17        480: 100% 17/17 [12:11<00:00, 43.03s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:11<00:00, 35.87s/it]
               all        132        121    0.00332      0.968     0.0412     0.0128

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    2/9         0G     0.0589    0.02051    0.05456         19        480: 100% 17/17 [12:12<00:00, 43.07s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:09<00:00, 34.67s/it]
               all        132        121      0.109       0.35      0.146     0.0575

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    3/9         0G    0.05018    0.01752    0.05136         15        480: 100% 17/17 [12:12<00:00, 43.11s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:10<00:00, 35.19s/it]
               all        132        121     0.0926      0.435       0.13     0.0684

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    4/9         0G    0.04824    0.01559    0.04843         13        480: 100% 17/17 [12:16<00:00, 43.33s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:06<00:00, 33.31s/it]
               all        132        121      0.198       0.17      0.136     0.0557

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    5/9         0G    0.04233    0.01407    0.04515          8        480: 100% 17/17 [12:17<00:00, 43.41s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:07<00:00, 33.74s/it]
               all        132        121      0.191      0.394      0.245       0.12

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    6/9         0G    0.04433    0.01333    0.04395         11        480: 100% 17/17 [12:10<00:00, 42.99s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:09<00:00, 34.68s/it]
               all        132        121      0.157      0.496      0.245      0.135

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    7/9         0G    0.03818    0.01367    0.04417         22        480: 100% 17/17 [12:10<00:00, 42.96s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:06<00:00, 33.48s/it]
               all        132        121      0.172      0.604       0.33      0.216

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    8/9         0G    0.03349    0.01245     0.0418         14        480: 100% 17/17 [12:18<00:00, 43.46s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:07<00:00, 33.72s/it]
               all        132        121     0.0302       0.61      0.121     0.0532

  Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
    9/9         0G    0.03194     0.0127    0.04184         16        480: 100% 17/17 [12:21<00:00, 43.63s/it]
             Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 2/2 [01:07<00:00, 33.51s/it]
               all        132        121     0.0317      0.929      0.204      0.107

10 epochs completed in 2.272 hours. Optimizer stripped from runs/train/result/weights/last.pt, 14.4MB Optimizer stripped from runs/train/result/weights/best.pt, 14.4MB

Validating runs/train/result/weights/best.pt... Fusing layers... YOLOv5s summary: 157 layers, 7031701 parameters, 0 gradients, 15.8 GFLOPs Class Images Instances P R mAP50 mAP50-95: 100% 2/2 [00:54<00:00, 27.40s/it] all 132 121 0.173 0.616 0.331 0.217 꼬깔콘고소한맛 132 22 0.107 0.273 0.16 0.104 농심매운새우깡 132 22 0.122 0.409 0.124 0.0774 콘초 132 22 0.152 0.545 0.312 0.242 농심바나나킥 132 22 0.343 0.909 0.438 0.282 포카칩오리지널 132 11 0.172 0.727 0.349 0.233 도도한나쵸미니미크림어니언맛 132 11 0.0866 0.448 0.108 0.0483 허니버터칩 132 11 0.225 1 0.824 0.533 Results saved to runs/train/result