!yolo task=detect mode=train model=yolov5x.pt data={dataset.location}/data.yaml epochs=100 imgsz=640
This is the input code
The output is as given below :
Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov5xu.pt to 'yolov5xu.pt'...
100% 186M/186M [00:00<00:00, 365MB/s]
Ultralytics YOLOv8.2.42 ???? Python-3.10.12 torch-2.3.0+cu121 CPU (Intel Xeon 2.20GHz)
engine/trainer: task=detect, mode=train, model=yolov5x.pt, data=/content/football-players-detection-1/data.yaml, epochs=100, time=None, patience=100, 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, multi_scale=False, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, 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, 95.1MB/s]
Overriding model.yaml nc=80 with nc=4
from n params module arguments
0 -1 1 8800 ultralytics.nn.modules.conv.Conv [3, 80, 6, 2, 2]
1 -1 1 115520 ultralytics.nn.modules.conv.Conv [80, 160, 3, 2]
2 -1 4 309120 ultralytics.nn.modules.block.C3 [160, 160, 4]
3 -1 1 461440 ultralytics.nn.modules.conv.Conv [160, 320, 3, 2]
4 -1 8 2259200 ultralytics.nn.modules.block.C3 [320, 320, 8]
5 -1 1 1844480 ultralytics.nn.modules.conv.Conv [320, 640, 3, 2]
6 -1 12 13125120 ultralytics.nn.modules.block.C3 [640, 640, 12]
7 -1 1 7375360 ultralytics.nn.modules.conv.Conv [640, 1280, 3, 2]
8 -1 4 19676160 ultralytics.nn.modules.block.C3 [1280, 1280, 4]
9 -1 1 4099840 ultralytics.nn.modules.block.SPPF [1280, 1280, 5]
10 -1 1 820480 ultralytics.nn.modules.conv.Conv [1280, 640, 1, 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 4 5332480 ultralytics.nn.modules.block.C3 [1280, 640, 4, False]
14 -1 1 205440 ultralytics.nn.modules.conv.Conv [640, 320, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
17 -1 4 1335040 ultralytics.nn.modules.block.C3 [640, 320, 4, False]
18 -1 1 922240 ultralytics.nn.modules.conv.Conv [320, 320, 3, 2]
19 [-1, 14] 1 0 ultralytics.nn.modules.conv.Concat [1]
20 -1 4 4922880 ultralytics.nn.modules.block.C3 [640, 640, 4, False]
21 -1 1 3687680 ultralytics.nn.modules.conv.Conv [640, 640, 3, 2]
22 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
23 -1 4 19676160 ultralytics.nn.modules.block.C3 [1280, 1280, 4, False]
24 [17, 20, 23] 1 11025820 ultralytics.nn.modules.head.Detect [4, [320, 640, 1280]]
YOLOv5x summary: 493 layers, 97203260 parameters, 97203244 gradients, 246.9 GFLOPs
Transferred 817/823 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/
Freezing layer 'model.24.dfl.conv.weight'
train: Scanning /content/football-players-detection-1/football-players-detection-1/train/labels... 612 images, 0 backgrounds, 0 corrupt: 100% 612/612 [00:00<00:00, 1644.29it/s]
train: New cache created: /content/football-players-detection-1/football-players-detection-1/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/football-players-detection-1/football-players-detection-1/valid/labels... 38 images, 0 backgrounds, 0 corrupt: 100% 38/38 [00:00<00:00, 1625.62it/s]
val: New cache created: /content/football-players-detection-1/football-players-detection-1/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=0.00125, momentum=0.9) with parameter groups 135 weight(decay=0.0), 142 weight(decay=0.0005), 141 bias(decay=0.0)
TensorBoard: model graph visualization added ✅
Image sizes 640 train, 640 val
Using 0 dataloader workers
Logging results to runs/detect/train
Starting training for 100 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
0% 0/39 [00:00<?, ?it/s]^C
If you see at the end, the epoch doesn’t run at all, it doesn’t list each of the epochs, but the google colab shows it ran successfully.
After running it show give me 2 files, the best weight and the last weight
enter image description here
I was expecting something like this
It should give me the best.pt and the last.pt file
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