1.GitHub 下載yolov5
2.環境：win10，visual studio 2015 ，anaconda2.7，python=3.7，cuda=10.2 ,pytorch=1.5(GPU)
3.更改鏡像-清華
4.安裝pytorch
5.安裝requirements.txt:報錯。解決方案：
python
git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI
用這個替換掉原有語句
git
6.下載預訓練模型，YOLOv5l.pt。
7.直接運行detect文件，報錯。解決方案：
github
1.python detect.py --source ./inference/images/bus.jpg --weights ./weights/yolov5s.pt --conf 0.4 --device cuda:0
2.這裏報錯：找不到cuda
3.進入到torch_utils.py,
api
def select_device(device='', apex=False, batch_size=None): # device = 'cpu' or '0' or '0,1,2,3' cpu_request = device.lower() == 'cpu' if device and not cpu_request: # if device requested other than 'cpu' os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device # check availablity . . .
加入一個語句bash
def select_device(device='', apex=False, batch_size=None): # device = 'cpu' or '0' or '0,1,2,3' print(torch.cuda.is_available()) cpu_request = device.lower() == 'cpu' if device and not cpu_request: # if device requested other than 'cpu' os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device # check availablity
這樣就能夠找到cudaapp
因而獲得結果：
測試
接下來，是製做本身的小型數據集，訓練和測試ui
0.下載coco2017，手動選40張圖片和對應標籤。spa
1.目錄結構：3d
##數據集和代碼平行目錄
yolo的格式是 xxx.jpg和對應的 xxx.txt,共2個文件
txt中存放的是標註好的人、狗、摩托車的座標；還有所屬類別的序號
2.自制yaml文件
train: ../lee/train/images val: ../lee/valid/images test: ../lee/test/images # number of classes nc: 80 # class names names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
運行指令：
python train.py --img 640 --batch 16 --epochs 10 --data ./data/lee.yaml --cfg ./models/yolov5s.yaml --device cuda:0
結果以下
(yolov5_py37_torch1.5) D:\yolo\yolov5-master>python train.py --img 640 --batch 16 --epochs 10 --data ./data/lee.yaml --cfg ./models/yolov5s.yaml --device cuda:0 Namespace(batch_size=16, bucket='', cache_images=False, cfg='./models/yolov5s.yaml', data='./data/lee.yaml', device='cuda:0', epochs=10, evolve=False, hyp='', img_size=[640], multi_scale=F alse, name='', noautoanchor=False, nosave=False, notest=False, rect=False, resume=False, single_cls=False, weights='') True Using CUDA Apex device0 _CudaDeviceProperties(name='GeForce RTX 2060 SUPER', total_memory=8192MB) Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/ Hyperparameters {'optimizer': 'SGD', 'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.58, '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': 0.014, 'hsv_s': 0.68, 'hsv_v': 0.36, 'degrees': 0.0, 'translate': 0.0, 'scale': 0.5, 'shear': 0.0} 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 19904 models.common.BottleneckCSP [64, 64, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3] 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] 6 -1 1 641792 models.common.BottleneckCSP [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 1248768 models.common.BottleneckCSP [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 378624 models.common.BottleneckCSP [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 95104 models.common.BottleneckCSP [256, 128, 1, False] 18 -1 1 32895 torch.nn.modules.conv.Conv2d [128, 255, 1, 1] 19 -2 1 147712 models.common.Conv [128, 128, 3, 2] 20 [-1, 14] 1 0 models.common.Concat [1] 21 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False] 22 -1 1 65535 torch.nn.modules.conv.Conv2d [256, 255, 1, 1] 23 -2 1 590336 models.common.Conv [256, 256, 3, 2] 24 [-1, 10] 1 0 models.common.Concat [1] 25 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] 26 -1 1 130815 torch.nn.modules.conv.Conv2d [512, 255, 1, 1] 27 [-1, 22, 18] 1 0 models.yolo.Detect [80, [[116, 90, 156, 198, 373, 326], [30, 61, 62, 45, 59, 119], [10, 13, 16, 30, 33, 23]]] Model Summary: 191 layers, 7.46816e+06 parameters, 7.46816e+06 gradients Optimizer groups: 62 .bias, 70 conv.weight, 59 other Scanning labels ..\lee\train\labels.cache (39 found, 0 missing, 0 empty, 0 duplicate, for 39 images): 100%|████████████████████████████████████████ █████| 39/39 [00:00<00:00, 19317.18it/s] Scanning labels ..\lee\valid\labels.cache (29 found, 0 missing, 0 empty, 0 duplicate, for 29 images): 100%|████████████████████████████████████████ █████| 29/29 [00:00<00:00, 29281.37it/s] Analyzing anchors... Best Possible Recall (BPR) = 0.9935 Image sizes 640 train, 640 test Using 8 dataloader workers Starting training for 10 epochs... Epoch gpu_mem GIoU obj cls total targets img_size 0/9 2.93G 0.1186 0.08896 0.1206 0.3281 242 640: 33%|███████████████████████▎ | 1/3 [ 0/9 2.95G 0.1167 0.08447 0.1214 0.3225 161 640: 33%|███████████████████████▎ | 1/3 [ 0/9 2.95G 0.1167 0.08447 0.1214 0.3225 161 640: 67%|██████████████████████████████████████████████▋ 0/9 2.95G 0.1169 0.08754 0.121 0.3255 105 640: 67%|██████████████████████████████████████████████▋ 0/9 2.95G 0.1169 0.08754 0.121 0.3255 105 640: 100%|██████████████████████████████████████████████████ 0/9 2.95G 0.1169 0.08754 0.121 0.3255 105 640: 100%|██████████████████████████████████████████████████ ████████████████████| 3/3 [00:04<00:00, 1.61s/it]
注意：下載的數據集中，有幾張圖片的txt文件有問題，訓練時會報錯
好比：KeyError: ‘…\lee\train\images\000000000009.jpg’
能夠直接刪除相應的圖片和txt文件
而後，會在這個weights文件夾中，生成新的xxx.pt權重文件
win10這步會有問題，因爲代碼是針對Linux寫的。
打開train.py文件
'runs/exp' //這個是Linux的目錄結構，須要改爲win10的
# Train if not opt.evolve: tb_writer = SummaryWriter(log_dir=increment_dir('runs/exp', opt.name)) print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/') train(hyp)
改爲：
# Train if not opt.evolve: tb_writer = SummaryWriter(log_dir=increment_dir('..\\yolo\\yolov5-master\\runs\\exp', opt.name)) print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/') train(hyp)
最後是訓練完成後，用本身的權重測試圖片
命令：
python detect.py --source ../lee/test/images --weights ./weights/myweights.pt
(yolov5_py37_torch1.5) D:\yolo\yolov5-master>python detect.py --source ../lee/test/images --device cuda:0 --weights ./weights/yolov5s.pt Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='cuda:0', img_size=640, iou_thres=0.5, output='inference/output', save_txt=False, source='../lee/test/images', update=False, vi ew_img=False, weights=['./weights/myweights.pt']) True Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 2060 SUPER', total_memory=8192MB) Fusing layers... Model Summary: 140 layers, 7.45958e+06 parameters, 7.45958e+06 gradients image 1/29 D:\yolo\lee\test\images\000000000389.jpg: 512x640 12 persons, 2 ties, Done. (0.034s) image 2/29 D:\yolo\lee\test\images\000000000394.jpg: 640x640 1 frisbees, Done. (0.017s) image 3/29 D:\yolo\lee\test\images\000000000395.jpg: 640x640 7 persons, 1 cell phones, Done. (0.015s) image 4/29 D:\yolo\lee\test\images\000000000397.jpg: 512x640 1 persons, 1 pizzas, 1 chairs, Done. (0.017s) image 5/29 D:\yolo\lee\test\images\000000000400.jpg: 640x640 1 dogs, Done. (0.015s) image 6/29 D:\yolo\lee\test\images\000000000404.jpg: 640x448 1 boats, Done. (0.017s) image 7/29 D:\yolo\lee\test\images\000000000415.jpg: 640x384 1 persons, 1 tennis rackets, Done. (0.016s) image 8/29 D:\yolo\lee\test\images\000000000419.jpg: 512x640 2 persons, 2 cars, 1 tennis rackets, Done. (0.017s) image 9/29 D:\yolo\lee\test\images\000000000428.jpg: 384x640 1 persons, 1 cakes, Done. (0.017s) image 10/29 D:\yolo\lee\test\images\000000000431.jpg: 448x640 1 persons, 1 sports balls, 1 tennis rackets, Done. (0.016s) image 11/29 D:\yolo\lee\test\images\000000000436.jpg: 640x448 1 persons, 1 bottles, 1 donuts, Done. (0.016s) image 12/29 D:\yolo\lee\test\images\000000000438.jpg: 512x640 17 donuts, Done. (0.016s) image 13/29 D:\yolo\lee\test\images\000000000443.jpg: 512x640 1 remotes, Done. (0.016s) image 14/29 D:\yolo\lee\test\images\000000000446.jpg: 640x512 1 persons, 1 couchs, 1 potted plants, Done. (0.016s) image 15/29 D:\yolo\lee\test\images\000000000450.jpg: 512x640 1 wine glasss, 3 cups, 1 pizzas, Done. (0.017s) image 16/29 D:\yolo\lee\test\images\000000000459.jpg: 640x576 1 persons, 1 ties, Done. (0.018s) image 17/29 D:\yolo\lee\test\images\000000000471.jpg: 448x640 1 buss, Done. (0.016s) image 18/29 D:\yolo\lee\test\images\000000000472.jpg: 256x640 1 airplanes, Done. (0.016s) image 19/29 D:\yolo\lee\test\images\000000000474.jpg: 640x448 1 persons, 1 baseball gloves, Done. (0.016s) image 20/29 D:\yolo\lee\test\images\000000000486.jpg: 448x640 1 bowls, 1 refrigerators, Done. (0.016s) image 21/29 D:\yolo\lee\test\images\000000000488.jpg: 448x640 4 persons, 1 sports balls, 3 baseball gloves, Done. (0.015s) image 22/29 D:\yolo\lee\test\images\000000000490.jpg: 640x640 1 dogs, 1 skateboards, 1 chairs, Done. (0.015s) image 23/29 D:\yolo\lee\test\images\000000000491.jpg: 448x640 1 birds, 1 teddy bears, Done. (0.016s) image 24/29 D:\yolo\lee\test\images\000000000502.jpg: 448x640 1 bears, Done. (0.015s) image 25/29 D:\yolo\lee\test\images\000000000510.jpg: 512x640 1 persons, 1 benchs, Done. (0.016s) image 26/29 D:\yolo\lee\test\images\000000000514.jpg: 640x384 1 beds, Done. (0.016s) image 27/29 D:\yolo\lee\test\images\000000000520.jpg: 512x640 2 persons, 7 birds, Done. (0.017s) image 28/29 D:\yolo\lee\test\images\000000000529.jpg: 640x448 1 persons, 1 motorcycles, Done. (0.015s) image 29/29 D:\yolo\lee\test\images\000000000531.jpg: 512x640 9 persons, 1 tennis rackets, Done. (0.016s) Results saved to D:\yolo\yolov5-master\inference/output Done. (1.946s)
能夠在…\yolov5-master\inference\output中看到