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网站建设通讯稿,泉州做网站工资,可以做t恤的网站,西安那些做网站的公司在训练之前先要按照一定目录格式准备数据#xff1a; VOC标签格式转yolo格式并划分训练集和测试集_爱钓鱼的歪猴的博客-CSDN博客
目录 1、修改数据配置文件 2、修改模型配置文件 3、训练 1、修改数据配置文件 coco.yaml 拷贝data/scripts/coco.yaml文件#xff0c; pa…在训练之前先要按照一定目录格式准备数据 VOC标签格式转yolo格式并划分训练集和测试集_爱钓鱼的歪猴的博客-CSDN博客   目录 1、修改数据配置文件 2、修改模型配置文件 3、训练 1、修改数据配置文件 coco.yaml 拷贝data/scripts/coco.yaml文件 path 修改为VOCdevkit文件夹所在目录 train修改为yolov5_train.txt val 修改为yolov5_val.txt names也进行修改保存为my-anther.yaml 具体如下 这样模仿VOC数据集的目录结构与yolov5项目里的代码所匹配 2、修改模型配置文件 把项目models/yolov5n.yaml文件拷贝一份只修改类别数量num of classes就成保存为my-yolov5n.yaml 3、训练 在项目目录下开启终端 运行 python train.py –data my-anther.yaml –epochs 1 –weights yolov5n.pt –cfg my-yolov5n.yaml –batch-size 24如果有GPU 命令后面添加 –device 0。这里没有GPU所以只跑一个 epoch。 batch可选择调为16、24、40、64、128….。先选一个小的保证能跑起来然后慢慢加大如果内存不足报错就返回上一个batch大小。 输出信息 (yolo) jasonhonor:~/PycharmProjects/pytorch_learn/yolo/yolov5-7.0$ python train.py –data my-anther.yaml –epochs 1 –weights yolov5n.pt –cfg my-yolov5n.yaml –batch-size 24 train: weightsyolov5n.pt, cfgmy-yolov5n.yaml, datamy-anther.yaml, hypdata/hyps/hyp.scratch-low.yaml, epochs1, batch_size24, imgsz640, rectFalse, resumeFalse, nosaveFalse, novalFalse, noautoanchorFalse, noplotsFalse, evolveNone, bucket, cacheNone, image_weightsFalse, device, multi_scaleFalse, single_clsFalse, optimizerSGD, sync_bnFalse, workers8, projectruns/train, nameexp, exist_okFalse, quadFalse, cos_lrFalse, label_smoothing0.0, patience100, freeze[0], save_period-1, seed0, local_rank-1, entityNone, upload_datasetFalse, bbox_interval-1, artifact_aliaslatest github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5 YOLOv5 2022-11-22 Python-3.8.13 torch-2.0.0cu117 CPUhyperparameters: lr00.01, lrf0.01, momentum0.937, weight_decay0.0005, warmup_epochs3.0, warmup_momentum0.8, warmup_bias_lr0.1, box0.05, cls0.5, cls_pw1.0, obj1.0, obj_pw1.0, iou_t0.2, anchor_t4.0, fl_gamma0.0, hsv_h0.015, hsv_s0.7, hsv_v0.4, degrees0.0, translate0.1, scale0.5, shear0.0, perspective0.0, flipud0.0, fliplr0.5, mosaic1.0, mixup0.0, copy_paste0.0 ClearML: run pip install clearml to automatically track, visualize and remotely train YOLOv5 in ClearML 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/from n params module arguments 0 -1 1 1760 models.common.Conv [3, 16, 6, 2, 2] 1 -1 1 4672 models.common.Conv [16, 32, 3, 2] 2 -1 1 4800 models.common.C3 [32, 32, 1] 3 -1 1 18560 models.common.Conv [32, 64, 3, 2] 4 -1 2 29184 models.common.C3 [64, 64, 2] 5 -1 1 73984 models.common.Conv [64, 128, 3, 2] 6 -1 3 156928 models.common.C3 [128, 128, 3] 7 -1 1 295424 models.common.Conv [128, 256, 3, 2] 8 -1 1 296448 models.common.C3 [256, 256, 1] 9 -1 1 164608 models.common.SPPF [256, 256, 5] 10 -1 1 33024 models.common.Conv [256, 128, 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 90880 models.common.C3 [256, 128, 1, False] 14 -1 1 8320 models.common.Conv [128, 64, 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 22912 models.common.C3 [128, 64, 1, False] 18 -1 1 36992 models.common.Conv [64, 64, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 74496 models.common.C3 [128, 128, 1, False] 21 -1 1 147712 models.common.Conv [128, 128, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 296448 models.common.C3 [256, 256, 1, False] 24 [17, 20, 23] 1 9471 models.yolo.Detect [2, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [64, 128, 256]] my-YOLOv5n summary: 214 layers, 1766623 parameters, 1766623 gradients, 4.2 GFLOPsTransferred ³⁴²⁄₃₄₉ items from yolov5n.pt optimizer: SGD(lr0.01) with parameter groups 57 weight(decay0.0), 60 weight(decay0.0005625000000000001), 60 bias train: Scanning /home/jason/work/my-datasets/yolov5_train.cache… 2276 images, 0 backgrounds, 0 corrupt: 100%|██████████| ²²⁷⁶⁄₂₂₇₆ 00:00 val: Scanning /home/jason/work/my-datasets/yolov5_val.cache… 568 images, 0 backgrounds, 0 corrupt: 100%|██████████| ⁵⁶⁸⁄₅₆₈ 00:00AutoAnchor: 6.38 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Plotting labels to runs/train/exp2/labels.jpg… Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/train/exp2 Starting training for 1 epochs…Epoch GPU_mem box_loss obj_loss cls_loss Instances Size0/0 0G 0.09858 0.3112 0.01833 1331 640: 100%|██████████| ⁹⁵⁄₉₅ 10:55Class Images Instances P R mAP50 mAP50-95: 0%| | 0/12 00:00WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 8%|▊ | ¹⁄₁₂ 00:08WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 17%|█▋ | ²⁄₁₂ 00:15WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 25%|██▌ | ³⁄₁₂ 00:23WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 33%|███▎ | ⁴⁄₁₂ 00:30WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 42%|████▏ | ⁵⁄₁₂ 00:38WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 50%|█████ | ⁶⁄₁₂ 00:47WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 58%|█████▊ | ⁷⁄₁₂ 00:54WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 67%|██████▋ | ⁸⁄₁₂ 01:00WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 75%|███████▌ | ⁹⁄₁₂ 01:07WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 83%|████████▎ | ¹⁰⁄₁₂ 01:14WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 92%|█████████▏| ¹¹⁄₁₂ 01:21WARNING ⚠️ NMS time limit 2.500s exceededClass Images Instances P R mAP50 mAP50-95: 100%|██████████| ¹²⁄₁₂ 01:28all 568 28591 0.613 0.262 0.108 0.03511 epochs completed in 0.207 hours. Optimizer stripped from runs/train/exp2/weights/last.pt, 3.8MB Optimizer stripped from runs/train/exp2/weights/best.pt, 3.8MBValidating runs/train/exp2/weights/best.pt… Fusing layers… my-YOLOv5n summary: 157 layers, 1761871 parameters, 0 gradients, 4.1 GFLOPsClass Images Instances P R mAP50 mAP50-95: 0%| | 0/12 00:00WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 8%|▊ | ¹⁄₁₂ 00:08WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 17%|█▋ | ²⁄₁₂ 00:44WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 25%|██▌ | ³⁄₁₂ 01:07WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 33%|███▎ | ⁴⁄₁₂ 01:33WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 42%|████▏ | ⁵⁄₁₂ 01:42WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 50%|█████ | ⁶⁄₁₂ 01:49WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 58%|█████▊ | ⁷⁄₁₂ 01:57WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 67%|██████▋ | ⁸⁄₁₂ 02:04WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 75%|███████▌ | ⁹⁄₁₂ 02:10WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 83%|████████▎ | ¹⁰⁄₁₂ 02:18WARNING ⚠️ NMS time limit 2.900s exceededClass Images Instances P R mAP50 mAP50-95: 92%|█████████▏| ¹¹⁄₁₂ 02:24WARNING ⚠️ NMS time limit 2.500s exceededClass Images Instances P R mAP50 mAP50-95: 100%|██████████| ¹²⁄₁₂ 02:30all 568 28591 0.61 0.228 0.0949 0.0317open 568 2512 1 0 0.00273 0.00136close 568 26079 0.219 0.456 0.187 0.062 Results saved to runs/train/exp2