YOLOv5白皮书-第Y6周：模型改进

本周任务：模型改进

注：对yolov5l.yaml文件中的backbone模块和head模块进行改进。

任务结构图： 

 

 

 YOLOv5s网络结构图:

原始模型代码：

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

 改进代码：

# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C2, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 3, C3, [512]],
   #[-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   #[-1, 3, C3, [1024]],
   [-1, 1, SPPF, [512, 5]],  # 9
  ]

# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 3, 2]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 12], 1, Concat, [1]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 8], 1, Concat, [1]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[15, 18, 21], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

 运行模型：

python train.py --img 640 --batch 8 --epoch 1 --data data/ab.yaml  --cfg models/yolov5s.yaml

---

(venv) D:\Out\yolov5-master>python train.py --img 640 --batch 8 --epoch 1 --data data/ab.yaml  --cfg models/yolov5s.yaml
train: weights=yolov5s.pt, cfg=models/yolov5s.yaml, data=data/ab.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=1, batch_size=8, imgsz=640, 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=exp, 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: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5
YOLOv5  2023-6-27 Python-3.10.3 torch-2.0.1+cpu 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
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=4

                 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     14592  models.common.C2                        [64, 64, 1]
  4                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]
  5                -1  2    115712  models.common.C3                        [128, 128, 2]
  6                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]
  7                -1  3    625152  models.common.C3                        [256, 256, 3]
  8                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]
  9                -1  1   1182720  models.common.C3                        [512, 512, 1]
 10                -1  1    656896  models.common.SPPF                      [512, 512, 5]
 11                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]
 12                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
 13           [-1, 6]  1         0  models.common.Concat                    [1]
 14                -1  1    361984  models.common.C3                        [512, 256, 1, False]
 15                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]
 16                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']
 17           [-1, 4]  1         0  models.common.Concat                    [1]
 18                -1  1     90880  models.common.C3                        [256, 128, 1, False]
 19                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]
 20          [-1, 14]  1         0  models.common.Concat                    [1]
 21                -1  1    329216  models.common.C3                        [384, 256, 1, False]
 22                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]
 23          [-1, 10]  1         0  models.common.Concat                    [1]
 24                -1  1   1313792  models.common.C3                        [768, 512, 1, False]
 25      [17, 20, 23]  1     38097  models.yolo.Detect                      [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [256, 384, 768]]
YOLOv5s summary: 229 layers, 7222673 parameters, 7222673 gradients, 17.0 GFLOPs

Transferred 49/373 items from yolov5s.pt
optimizer: SGD(lr=0.01) with parameter groups 61 weight(decay=0.0), 64 weight(decay=0.0005), 64 bias
train: Scanning D:\Out\yolov5-master\paper_data\train.cache... 160 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1
val: Scanning D:\Out\yolov5-master\paper_data\val.cache... 20 images, 0 backgrounds, 0 corrupt: 100%|██████████| 20/20

AutoAnchor: 5.35 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset
Plotting labels to runs\train\exp3\labels.jpg...
Image sizes 640 train, 640 val
Using 4 dataloader workers
Logging results to runs\train\exp3
Starting training for 1 epochs...

      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size
        0/0         0G     0.1101    0.04563     0.0454         49        640: 100%|██████████| 20/20 [02:44<00:00,  8.
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:05<0
                   all         20         60   0.000542       0.25   0.000682   0.000268

1 epochs completed in 0.048 hours.
Optimizer stripped from runs\train\exp3\weights\last.pt, 14.8MB
Optimizer stripped from runs\train\exp3\weights\best.pt, 14.8MB

Validating runs\train\exp3\weights\best.pt...
Fusing layers...
YOLOv5s summary: 168 layers, 7213041 parameters, 0 gradients, 16.8 GFLOPs
                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100%|██████████| 2/2 [00:05<0
                   all         20         60   0.000542       0.25   0.000685   0.000268
                banana         20         12          0          0          0          0
           snake fruit         20         20          0          0          0          0
          dragon fruit         20         13    0.00217          1    0.00274    0.00107
             pineapple         20         15          0          0          0          0
Results saved to runs\train\exp3

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