临沂市建设工程多图联审系统 网站,wordpress知识库主题,专业建设总结,广西住房城乡建设厅使用的命令#xff1a;iostat -x 5 可以看到 ssd的利用率已经满了。
之前在的数据集放在了 hdd上#xff0c;训练结果特别慢。 所以我把它移动到了ssd上#xff0c;然后训练参数用的 resume#xff0c; 但是#xff01;#xff01;#xff01;#xff01;它把历史记住… 使用的命令iostat -x 5 可以看到 ssd的利用率已经满了。
之前在的数据集放在了 hdd上训练结果特别慢。 所以我把它移动到了ssd上然后训练参数用的 resume 但是它把历史记住了仍然不从ssd上来取数据。 配置文件的路径也换了但它还是会去找旧的。 现在的100% 是扫描数据的100% 因数数据集15G~20G还是比较多的。
engine/trainer: taskdetect, modetrain, model/home/justin/Desktop/code/python_project/Jersey-Number/yolov8n.pt, data/home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/data.yaml, epochs1000, timeNone, patience100, batch64, imgsz640, saveTrue, save_period-1, cacheFalse, device[0, 1], workers8, projectNone, nametrain70, exist_okFalse, pretrainedTrue, optimizerauto, verboseTrue, seed0, deterministicTrue, single_clsFalse, rectFalse, cos_lrFalse, close_mosaic10, resumeFalse, ampTrue, fraction1.0, profileFalse, freezeNone, multi_scaleFalse, overlap_maskTrue, mask_ratio4, dropout0.0, valTrue, splitval, save_jsonFalse, save_hybridFalse, confNone, iou0.7, max_det300, halfFalse, dnnFalse, plotsTrue, sourceNone, vid_stride1, stream_bufferFalse, visualizeFalse, augmentFalse, agnostic_nmsFalse, classesNone, retina_masksFalse, embedNone, showFalse, save_framesFalse, save_txtFalse, save_confFalse, save_cropFalse, show_labelsTrue, show_confTrue, show_boxesTrue, line_widthNone, formattorchscript, kerasFalse, optimizeFalse, int8False, dynamicFalse, simplifyFalse, opsetNone, workspace4, nmsFalse, lr00.01, lrf0.01, momentum0.937, weight_decay0.0005, warmup_epochs3.0, warmup_momentum0.8, warmup_bias_lr0.1, box7.5, cls0.5, dfl1.5, pose12.0, kobj1.0, label_smoothing0.0, nbs64, hsv_h0.015, hsv_s0.7, hsv_v0.4, degrees0.0, translate0.1, scale0.5, shear0.0, perspective0.0, flipud0.0, fliplr0.5, bgr0.0, mosaic1.0, mixup0.0, copy_paste0.0, auto_augmentrandaugment, erasing0.4, crop_fraction1.0, cfgNone, trackerbotsort.yaml, save_dirruns/detect/train70
Overriding model.yaml nc80 with nc4from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, nearest] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 22 [15, 18, 21] 1 752092 ultralytics.nn.modules.head.Detect [4, [64, 128, 256]]
Model summary: 225 layers, 3011628 parameters, 3011612 gradients, 8.2 GFLOPsTransferred 319/355 items from pretrained weights
DDP: debug command /home/justin/miniconda3/bin/python -m torch.distributed.run --nproc_per_node 2 --master_port 41127 /home/justin/.config/Ultralytics/DDP/_temp_uog7ddsr140402595641744.py
WARNING:__main__:
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Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
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Ultralytics YOLOv8.2.1 Python-3.11.0 torch-2.3.0cu121 CUDA:0 (NVIDIA GeForce RTX 4090, 24210MiB)CUDA:1 (NVIDIA GeForce RTX 4090, 24188MiB)
TensorBoard: Start with tensorboard --logdir runs/detect/train70, view at http://localhost:6006/
Overriding model.yaml nc80 with nc4
Transferred 319/355 items from pretrained weights
Freezing layer model.22.dfl.conv.weight
AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n...
/home/justin/miniconda3/lib/python3.11/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)return F.conv2d(input, weight, bias, self.stride,
AMP: checks passed ✅
train: Scanning /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/
train: Scanning /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/train: Scanning /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/train: Scanning /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/
train: Scanning /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/
train: Scanning /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/train: Scanning /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/train: Scanning /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/我就是看这里
train: WARNING ⚠️ /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/images/284193,42a000df17be3d.jpg: 1 duplicate labels removed
train: WARNING ⚠️ /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/images/284193,575c000f3f01e40.jpg: 1 duplicate labels removed
train: WARNING ⚠️ /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/images/284193,70d2000c58fbf86.jpg: 1 duplicate labels removed
train: WARNING ⚠️ /home/justin/Desktop/code/python_project/Jersey-Number/datasets/20240511_four_in_1/data_head_person_hoop_number/train/images/284193,880000198e8148.jpg: 1 duplicate labels removed看出路径不对了然后from scratch开始训练就好使了。
然而并无卵用确实换到ssd上了还是很差应该是碎文件所致哎。。。所以深度学习级别的hello world 用plk存储文件是有道理的为了不让他那么碎啊 个人理解啊。
