快站优惠券app,家具品牌网站,asp网站开发需要什么,营商环境网站建设#x1f4cc;本周任务#xff1a;模型改进#x1f4cc; 注#xff1a;对yolov5l.yaml文件中的backbone模块和head模块进行改进。 任务结构图#xff1a; YOLOv5s网络结构图:
原始模型代码#xff1a;
# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args]…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-masterpython train.py --img 640 --batch 8 --epoch 1 --data data/ab.yaml --cfg models/yolov5s.yaml train: weightsyolov5s.pt, cfgmodels/yolov5s.yaml, datadata/ab.yaml, hypdata\hyps\hyp.scratch-low.yaml, epochs1, batch_size8, 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 2023-6-27 Python-3.10.3 torch-2.0.1cpu CPU
hyperparameters: 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 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 nc80 with nc4 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(lr0.01) with parameter groups 61 weight(decay0.0), 64 weight(decay0.0005), 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 [02:4400:00, 8. Class Images Instances P R mAP50 mAP50-95: 100%|██████████| 2/2 [00:050 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 [00:050 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
