> & D:/Anaconda/envs/yolo_v5/python.exe e:/yolov5-6.0/train.py
train: weights=E:\yolov5-6.0\weights\yolov5s.pt, cfg=, data=E:\yolov5-6.0\data\scripts\zjc.yaml, hyp=data\hyps\hyp.scratch.yaml, epochs=300, batch_size=2, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=0, save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5 2025-7-7 torch 1.8.1+cu111 CUDA:0 (NVIDIA GeForce RTX 4060 Laptop GPU, 8187.5MB)
hyperparameters: lr0=0.01, lrf=0.1, 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
Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 runs (RECOMMENDED)
TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/
from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
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 3 156928 models.common.C3 [128, 128, 3]
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 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1182720 models.common.C3 [512, 512, 1, False]
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 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.2 GFLOPs
Transferred 361/361 items from E:\yolov5-6.0\weights\yolov5s.pt
Scaled weight_decay = 0.0005
optimizer: SGD with parameter groups 59 weight, 62 weight (no decay), 62 bias
train: Scanning '..\datasets\coco128\labels\train2017.cache' images and labels... 126 found, 2 missing, 0 empty, 0 corrupted: 100%|███████████| 128/128 [00:00<?, ?it/s]
val: Scanning '..\datasets\coco128\labels\train2017.cache' images and labels... 126 found, 2 missing, 0 empty, 0 corrupted: 100%|█████████████| 128/128 [00:00<?, ?it/s]
Plotting labels...
autoanchor: Analyzing anchors... anchors/target = 4.27, Best Possible Recall (BPR) = 0.9935
Traceback (most recent call last):
File "e:/yolov5-6.0/train.py", line 620, in <module>
main(opt)
File "e:/yolov5-6.0/train.py", line 517, in main
train(opt.hyp, opt, device, callbacks)
File "e:/yolov5-6.0/train.py", line 251, in train
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
File "e:\yolov5-6.0\utils\general.py", line 470, in labels_to_class_weights
classes = labels[:, 0].astype(np.int) # labels = [class xywh]
File "D:\Anaconda\envs\yolo_v5\lib\site-packages\numpy\__init__.py", line 305, in __getattr__
raise AttributeError(__former_attrs__[attr])
AttributeError: module 'numpy' has no attribute 'int'.
`np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
PS E:\yolov5-6.0>