记录一下yolo v5从零训练COCO数据集的情况_yolov5训练coco数据集要多久

关于coco2017数据集

coco2017 80个类别  

训练集118287 验证集 5000 测试集40670 一共163957

训练集中有117266被标注(每张图片有多个不同种类的目标) 验证集中有4952张被标注 

关于混合精度训练 

yolov5默认开启混合精度训练：

            # Forward
            with torch.cuda.amp.autocast(amp):
                pred = model(imgs)  # forward
                loss, loss_items = compute_loss(pred, targets.to(device))  # loss scaled by batch_size
                if RANK != -1:
                    loss *= WORLD_SIZE  # gradient averaged between devices in DDP mode
                if opt.quad:
                    loss *= 4.

 官方说的是在单卡V100显卡上大概训练s模型需要2天

 我这里是单卡3090，如果是基于预训练模型训练，用时不到4天半，55个小时，但是从零训练应该不能基于预训练模型，如果weights不传参，训练就很慢了，大概测算需要多半个月。

基于预训练模型训练完后的日志，忘了保存，只有一部分：

                fork       5000        215      0.646      0.349      0.431      0.248
               knife       5000        325      0.516      0.206      0.223      0.111
               spoon       5000        253       0.54       0.19      0.229      0.117
                bowl       5000        623      0.616      0.475      0.516      0.356
              banana       5000        370      0.486      0.332      0.343      0.175
               apple       5000        236      0.433      0.275      0.219      0.138
            sandwich       5000        177       0.61      0.458      0.487      0.314
              orange       5000        285      0.492      0.373      0.349      0.255
            broccoli       5000        312      0.485      0.394      0.377      0.184
              carrot       5000        365      0.381      0.359      0.309      0.184
             hot dog       5000        125       0.62      0.472      0.477       0.31
               pizza       5000        284      0.712      0.634      0.668      0.455
               donut       5000        328      0.547      0.491        0.5      0.372
                cake       5000        310      0.618      0.439      0.508       0.31
               chair       5000       1771      0.596      0.404      0.451      0.257
               couch       5000        261      0.707      0.489      0.596      0.398
        potted plant       5000        342      0.564      0.415      0.435      0.235
                 bed       5000        163      0.709      0.509      0.591      0.368
        dining table       5000        695      0.592      0.357      0.394      0.241
              toilet       5000        179      0.805       0.76      0.829      0.607
                  tv       5000        288      0.758      0.664      0.735      0.521
              laptop       5000        231      0.765      0.649      0.706      0.538
               mouse       5000        106      0.804      0.708      0.762      0.543
              remote       5000        283      0.547      0.406      0.433      0.215
            keyboard       5000        153      0.652      0.575      0.658      0.423
          cell phone       5000        262      0.595      0.454      0.488      0.296
           microwave       5000         55      0.631      0.636      0.712      0.494
                oven       5000        143      0.626      0.448      0.535      0.304
             toaster       5000          9      0.793      0.333      0.515      0.355
                sink       5000        225      0.656      0.498      0.556      0.338
        refrigerator       5000        126      0.759      0.603       0.69      0.489
                book       5000       1129      0.455      0.181      0.219     0.0885
               clock       5000        267      0.777      0.704      0.735      0.471
                vase       5000        274      0.566      0.496      0.482      0.311
            scissors       5000         36      0.566      0.222      0.246       0.18
          teddy bear       5000        190      0.721      0.526      0.611      0.373
          hair drier       5000         11          1          0     0.0206    0.00303
          toothbrush       5000         57      0.529      0.298      0.344       0.19
 
Evaluating pycocotools mAP... saving runs/train/exp/_predictions.json...
loading annotations into memory...
Done (t=0.53s)
creating index...
index created!
Loading and preparing results...
DONE (t=3.21s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type *bbox*
DONE (t=41.97s).
Accumulating evaluation results...
DONE (t=6.99s).
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.372
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.566
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.402
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.209
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.422
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.477
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.309
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.514
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.570
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.374
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.631
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716
Results saved to runs/train/exp

训练好的模型和结果在 从零训练yolov5在COCO数据集上的模型和结果-深度学习文档类资源-CSDN下载