Why when updating yolov4 with darknet I got Couldn't open file: /home/hp/computerVision/backup//yolov4_final.weights?

I am trying to update yolo v4 by using darknet and a code as follow:

./darknet detector train cfg/coco.data cfg/yolov4.cfg yolov4.weights    

whereas my coco. data is as follow :

classes= 80  train  = /home/hp/computerVision/train.txt  valid  = /home/hp/computerVision/test.txt  #valid = data/coco_val_5k.list  names = /home/hp/computerVision/coco.names  backup = /home/hp/computerVision/backup/  

and my yolov4.cfg are as follow in case it is needed

[net]  batch=64  subdivisions=8  # Training  #width=512  #height=512  width=608  height=608  channels=3  momentum=0.949  decay=0.0005  angle=0  saturation = 1.5  exposure = 1.5  hue=.1    learning_rate=0.0013  burn_in=1000  max_batches = 500500  policy=steps  steps=400000,450000  scales=.1,.1    #cutmix=1  mosaic=1    #:104x104 54:52x52 85:26x26 104:13x13 for 416    [convolutional]  batch_normalize=1  filters=32  size=3  stride=1  pad=1  activation=mish    # Downsample    [convolutional]  batch_normalize=1  filters=64  size=3  stride=2  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=64  size=1  stride=1  pad=1  activation=mish    [route]  layers = -2    [convolutional]  batch_normalize=1  filters=64  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=32  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=64  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=64  size=1  stride=1  pad=1  activation=mish    [route]  layers = -1,-7    [convolutional]  batch_normalize=1  filters=64  size=1  stride=1  pad=1  activation=mish    # Downsample    [convolutional]  batch_normalize=1  filters=128  size=3  stride=2  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=64  size=1  stride=1  pad=1  activation=mish    [route]  layers = -2    [convolutional]  batch_normalize=1  filters=64  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=64  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=64  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=64  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=64  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=64  size=1  stride=1  pad=1  activation=mish    [route]  layers = -1,-10    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    # Downsample    [convolutional]  batch_normalize=1  filters=256  size=3  stride=2  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [route]  layers = -2    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear      [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=128  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=mish    [route]  layers = -1,-28    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    # Downsample    [convolutional]  batch_normalize=1  filters=512  size=3  stride=2  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [route]  layers = -2    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear      [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear      [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear      [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear      [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear      [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear      [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=256  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=mish    [route]  layers = -1,-28    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=mish    # Downsample    [convolutional]  batch_normalize=1  filters=1024  size=3  stride=2  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=mish    [route]  layers = -2    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=512  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=512  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=512  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=mish    [convolutional]  batch_normalize=1  filters=512  size=3  stride=1  pad=1  activation=mish    [shortcut]  from=-3  activation=linear    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=mish    [route]  layers = -1,-16    [convolutional]  batch_normalize=1  filters=1024  size=1  stride=1  pad=1  activation=mish    ##########################    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=1024  activation=leaky    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=leaky    ### SPP ###  [maxpool]  stride=1  size=5    [route]  layers=-2    [maxpool]  stride=1  size=9    [route]  layers=-4    [maxpool]  stride=1  size=13    [route]  layers=-1,-3,-5,-6  ### End SPP ###    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=1024  activation=leaky    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=leaky    [upsample]  stride=2    [route]  layers = 85    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=leaky    [route]  layers = -1, -3    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=512  activation=leaky    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=512  activation=leaky    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=leaky    [upsample]  stride=2    [route]  layers = 54    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=leaky    [route]  layers = -1, -3    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=256  activation=leaky    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=256  activation=leaky    [convolutional]  batch_normalize=1  filters=128  size=1  stride=1  pad=1  activation=leaky    ##########################    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=256  activation=leaky    [convolutional]  size=1  stride=1  pad=1  filters=255  activation=linear      [yolo]  mask = 0,1,2  anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401  classes=80  num=9  jitter=.3  ignore_thresh = .7  truth_thresh = 1  scale_x_y = 1.2  iou_thresh=0.213  cls_normalizer=1.0  iou_normalizer=0.07  iou_loss=ciou  nms_kind=greedynms  beta_nms=0.6  max_delta=5      [route]  layers = -4    [convolutional]  batch_normalize=1  size=3  stride=2  pad=1  filters=256  activation=leaky    [route]  layers = -1, -16    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=512  activation=leaky    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=512  activation=leaky    [convolutional]  batch_normalize=1  filters=256  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=512  activation=leaky    [convolutional]  size=1  stride=1  pad=1  filters=255  activation=linear      [yolo]  mask = 3,4,5  anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401  classes=80  num=9  jitter=.3  ignore_thresh = .7  truth_thresh = 1  scale_x_y = 1.1  iou_thresh=0.213  cls_normalizer=1.0  iou_normalizer=0.07  iou_loss=ciou  nms_kind=greedynms  beta_nms=0.6  max_delta=5      [route]  layers = -4    [convolutional]  batch_normalize=1  size=3  stride=2  pad=1  filters=512  activation=leaky    [route]  layers = -1, -37    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=1024  activation=leaky    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=1024  activation=leaky    [convolutional]  batch_normalize=1  filters=512  size=1  stride=1  pad=1  activation=leaky    [convolutional]  batch_normalize=1  size=3  stride=1  pad=1  filters=1024  activation=leaky    [convolutional]  size=1  stride=1  pad=1  filters=255  activation=linear      [yolo]  mask = 6,7,8  anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401  classes=80  num=9  jitter=.3  ignore_thresh = .7  truth_thresh = 1  random=1  scale_x_y = 1.05  iou_thresh=0.213  cls_normalizer=1.0  iou_normalizer=0.07  iou_loss=ciou  nms_kind=greedynms  beta_nms=0.6  max_delta=5  

and i get an error like this :

Saving weights to /home/hp/computerVision/backup//yolov4_final.weights  Couldn't open file: /home/hp/computerVision/backup//yolov4_final.weights  

I am including the line where it save yolov4_final.weights is because that when I am trying to troubleshoot the problem I check the folder and there is no yolov4_final.weights so I check whole darknet directory and I couldn't found yolov4_final.weights. So what is actually happening here? How do I solve this problem? Thank you.yoyo

https://stackoverflow.com/questions/66717830/why-when-updating-yolov4-with-darknet-i-got-couldnt-open-file-home-hp-compute March 20, 2021 at 10:59AM