######################1##########################

D:\Anaconda3\envs\py38torch_gpu\python.exe D:\needed\yolov7-main\train.py --weights weights/yolov7.pt --cfg cfg/training/yolov7.yaml --data data/datasets.yaml --device 0 --batch-size 8 --epoch 5 

YOLOR  2022-9-16 torch 1.9.0+cu111 CUDA:0 (NVIDIA GeForce RTX 3060 Ti, 8191.5MB)

Namespace(adam=False, artifact_alias=latest, batch_size=8, bbox_interval=-1, bucket=, cache_images=False, cfg=cfg/training/yolov7.yaml, data=data/datasets.yaml, device=0, entity=None, epochs=5, evolve=False, exist_ok=False, freeze=[0], global_rank=-1, hyp=data/hyp.scratch.p5.yaml, image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name=exp, noautoanchor=False, nosave=False, notest=False, project=runs/train, quad=False, rect=False, resume=False, save_dir=runs\\train\\exp5, save_period=-1, single_cls=False, sync_bn=False, total_batch_size=8, upload_dataset=False, v5_metric=False, weights=weights/yolov7.pt, workers=0, world_size=1)

tensorboard: Start with tensorboard --logdir runs/train, view at http://localhost:6006/

2022-10-07 21:29:10.393199: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library cudart64_101.dll; dlerror: cudart64_101.dll not found

2022-10-07 21:29:10.393278: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.

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=0.3, cls_pw=1.0, obj=0.7, 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=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1

wandb: Install Weights & Biases for YOLOR logging with pip install wandb (recommended)

fatal: not a git repository (or any of the parent directories): .git

Traceback (most recent call last):

  File "D:\needed\yolov7-main\utils\google_utils.py", line 26, in attempt_download

    assets = [x[name] for x in response[assets]]  # release assets

KeyError: assets

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

  File "D:\needed\yolov7-main\train.py", line 616, in

    train(hyp, opt, device, tb_writer)

  File "D:\needed\yolov7-main\train.py", line 86, in train

    attempt_download(weights)  # download if not found locally

  File "D:\needed\yolov7-main\utils\google_utils.py", line 31, in attempt_download

    tag = subprocess.check_output(git tag, shell=True).decode().split()[-1]

  File "D:\Anaconda3\envs\py38torch_gpu\lib\subprocess.py", line 415, in check_output

    return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,

  File "D:\Anaconda3\envs\py38torch_gpu\lib\subprocess.py", line 516, in run

    raise CalledProcessError(retcode, process.args, subprocess.CalledProcessError: Command git tag returned non-zero exit status 128.

Process finished with exit code 1

answer:下载yolov7.pt权重文件

GitHub - WongKinYiu/yolov7: Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

 修改train.py中：

parser.add_argument(--weights, type=str, default=yolov7.pt, help=initial weights path)

#######################2##########################_pickle.UnpicklingError: STACK_GLOBAL requires str

找到数据集下面的图片和标签文件 
  
  
  
  
  
  ，删掉labels.cache
  
  
  
  
  
  
 ，labels.cache.npy文件

yolov7用自己的数据集训练：

1.data文件夹下面的mydata.yaml文件修改：

train: D:\needed\air-filter\train\images # 训练集绝对路径 进入到训练集存放图片的文件夹里面
 


 


 


 


 


 


，按ctrl+L复制过来即可 val: D:\needed\air-filter\valid\images # 验证集绝对路径 进入到验证集存放图片的文件夹里面 
 
 
 
 
 
 ，按ctrl+L复制过来即可 # test: D:\needed\air-filter\train\images nc: 6 # class数 names: [aa,bb,cc,dd,ee,ff] # 模型类别名

2.修改yolov7.yaml文件

将nc修改为自己的类别数
   
   
   
   
   
   
 ，如果自己的GPU不给力
 

 

 

 

 

 

，把下面的参数改改：

depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple

3.训练

--weights weights/yolov7.pt --cfg cfg/training/yolov7.yaml --data data/mydata.yaml --device 0 --batch-size 16 --epoch 100 --device 0