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yolov5
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3a42abd1
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3a42abd1
authored
1月 17, 2021
作者:
Glenn Jocher
浏览文件
操作
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电子邮件补丁
差异文件
Created using Colaboratory
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b26a2f62
隐藏空白字符变更
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1 个修改的文件
包含
131 行增加
和
125 行删除
+131
-125
tutorial.ipynb
tutorial.ipynb
+131
-125
没有找到文件。
tutorial.ipynb
浏览文件 @
3a42abd1
...
...
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"source": [
"!git clone https://github.com/ultralytics/yolov5 # clone repo\n",
...
...
@@ -563,7 +563,7 @@
"clear_output()\n",
"print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
],
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,
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...
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"source": [
"# Download COCO val2017\n",
"torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n",
"!unzip -q tmp.zip -d ../ && rm tmp.zip"
],
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null
,
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{
"output_type": "display_data",
"data": {
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...
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"source": [
"# Run YOLOv5x on COCO val2017\n",
"!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65"
],
"execution_count":
null
,
"execution_count":
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,
"outputs": [
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"text": [
"Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
"
Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130
MB)\n",
"Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_
hybrid=False, save_
json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
"
YOLOv5 v4.0-21-gb26a2f6 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130.5
MB)\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v
3.1
/yolov5x.pt to yolov5x.pt...\n",
"100% 1
70M/170M [00:05<00:00, 32.6
MB/s]\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v
4.0
/yolov5x.pt to yolov5x.pt...\n",
"100% 1
68M/168M [00:05<00:00, 31.9
MB/s]\n",
"\n",
"Fusing layers... \n",
"Model Summary: 484 layers, 88922205 parameters, 0 gradients\n",
"Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 14785.71it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:30<00:00, 1.74it/s]\n",
" all 5e+03 3.63e+04 0.409 0.754 0.672 0.484\n",
"Speed: 5.9/2.1/7.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
"Model Summary: 476 layers, 87730285 parameters, 0 gradients, 218.8 GFLOPS\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/labels/val2017' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2791.81it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../coco/labels/val2017.cache\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco/labels/val2017.cache' for images and labels... 4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:00<00:00, 13332180.55it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:30<00:00, 1.73it/s]\n",
" all 5e+03 3.63e+04 0.419 0.765 0.68 0.486\n",
"Speed: 5.2/2.0/7.2 ms inference/NMS/total per 640x640 image at batch-size 32\n",
"\n",
"Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n",
"loading annotations into memory...\n",
"Done (t=0.4
3
s)\n",
"Done (t=0.4
1
s)\n",
"creating index...\n",
"index created!\n",
"Loading and preparing results...\n",
"DONE (t=
4.67
s)\n",
"DONE (t=
5.26
s)\n",
"creating index...\n",
"index created!\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=9
2.11
s).\n",
"DONE (t=9
3.97
s).\n",
"Accumulating evaluation results...\n",
"DONE (t=1
3.24
s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.
492
\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.6
76
\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.5
3
4\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.3
1
8\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.54
1
\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.63
3
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.37
6
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.6
17
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.6
7
0\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.
493
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.72
3
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.8
12
\n",
"DONE (t=1
5.06
s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.
501
\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.6
87
\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.5
4
4\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.3
3
8\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.54
8
\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.63
7
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.37
8
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.6
28
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.6
8
0\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.
520
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.72
9
\n",
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.8
26
\n",
"Results saved to runs/test/exp\n"
],
"name": "stdout"
...
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"outputId": "
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},
"source": [
"# Download COCO128\n",
"torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n",
"!unzip -q tmp.zip -d ../ && rm tmp.zip"
],
"execution_count":
null
,
"execution_count":
4
,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "
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",
"model_id": "
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},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=2209
0455
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"HBox(children=(FloatProgress(value=0.0, max=2209
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},
"metadata": {
...
...
@@ -923,86 +925,90 @@
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "
138f2d1d-364c-405a-cf13-ea91a2aff915
"
"outputId": "
6af7116a-01ab-4b94-e5d7-b37c17dc95de
"
},
"source": [
"# Train YOLOv5s on COCO128 for 3 epochs\n",
"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --nosave --cache"
],
"execution_count":
null
,
"execution_count":
5
,
"outputs": [
{
"output_type": "stream",
"text": [
"Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 v4.0-21-gb26a2f6 torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130.5MB)\n",
"\n",
"Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
"Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_artifacts=False, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', quad=False, rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
"\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n",
"Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n",
"202
0-11-20 11:45:17.042357: I tensorflow/stream_executor/platform/default/dso_loader.cc:48
] Successfully opened dynamic library libcudart.so.10.1\n",
"
Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'cls': 0.5, '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': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0}
\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v
3.1
/yolov5s.pt to yolov5s.pt...\n",
"100% 14.
5M/14.5M [00:01<00:00, 14
.8MB/s]\n",
"202
1-01-17 19:56:03.945851: I tensorflow/stream_executor/platform/default/dso_loader.cc:49
] Successfully opened dynamic library libcudart.so.10.1\n",
"
\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, 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=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0
\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v
4.0
/yolov5s.pt to yolov5s.pt...\n",
"100% 14.
1M/14.1M [00:00<00:00, 15
.8MB/s]\n",
"\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Focus [3, 32, 3] \n",
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
" 2 -1 1 1
9904 models.common.BottleneckCSP
[64, 64, 1] \n",
" 2 -1 1 1
8816 models.common.C3
[64, 64, 1] \n",
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
" 4 -1 1 1
61152 models.common.BottleneckCSP
[128, 128, 3] \n",
" 4 -1 1 1
56928 models.common.C3
[128, 128, 3] \n",
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
" 6 -1 1 6
41792 models.common.BottleneckCSP
[256, 256, 3] \n",
" 6 -1 1 6
25152 models.common.C3
[256, 256, 3] \n",
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
" 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n",
" 9 -1 1 1
248768 models.common.BottleneckCSP
[512, 512, 1, False] \n",
" 9 -1 1 1
182720 models.common.C3
[512, 512, 1, False] \n",
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 12 [-1, 6] 1 0 models.common.Concat [1] \n",
" 13 -1 1 3
78624 models.common.BottleneckCSP
[512, 256, 1, False] \n",
" 13 -1 1 3
61984 models.common.C3
[512, 256, 1, False] \n",
" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 16 [-1, 4] 1 0 models.common.Concat [1] \n",
" 17 -1 1 9
5104 models.common.BottleneckCSP
[256, 128, 1, False] \n",
" 17 -1 1 9
0880 models.common.C3
[256, 128, 1, False] \n",
" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
" 19 [-1, 14] 1 0 models.common.Concat [1] \n",
" 20 -1 1
313088 models.common.BottleneckCSP
[256, 256, 1, False] \n",
" 20 -1 1
296448 models.common.C3
[256, 256, 1, False] \n",
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
" 23 -1 1 1
248768 models.common.BottleneckCSP
[512, 512, 1, False] \n",
" 23 -1 1 1
182720 models.common.C3
[512, 512, 1, False] \n",
" 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
"Model Summary: 283 layers, 7
468157 parameters, 7468157 gradients
\n",
"Model Summary: 283 layers, 7
276605 parameters, 7276605 gradients, 17.1 GFLOPS
\n",
"\n",
"Transferred 370/370 items from yolov5s.pt\n",
"Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n",
"Scanning images: 100% 128/128 [00:00<00:00, 5395.63it/s]\n",
"Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 13972.28it/s]\n",
"Caching images (0.1GB): 100% 128/128 [00:00<00:00, 173.55it/s]\n",
"Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 8693.98it/s]\n",
"Caching images (0.1GB): 100% 128/128 [00:00<00:00, 133.30it/s]\n",
"NumExpr defaulting to 2 threads.\n",
"Transferred 362/362 items from yolov5s.pt\n",
"Scaled weight_decay = 0.0005\n",
"Optimizer groups: 62 .bias, 62 conv.weight, 59 other\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../coco128/labels/train2017' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2647.74it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../coco128/labels/train2017.cache\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 1503840.09it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 176.03it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '../coco128/labels/train2017.cache' for images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 24200.82it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:01<00:00, 123.25it/s]\n",
"Plotting labels... \n",
"\n",
"Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
"
\u001b[34m\u001b[1mautoanchor: \u001b[0m
Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n",
"Image sizes 640 train, 640 test\n",
"Using 2 dataloader workers\n",
"Logging results to runs/train/exp\n",
"Starting training for 3 epochs...\n",
"\n",
" Epoch gpu_mem box obj cls total targets img_size\n",
" 0/2
5.24G 0.04202 0.06745 0.01503 0.1245 194 640: 100% 8/8 [00:03<00:00, 2.01
it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:0
3<00:00, 2.40
it/s]\n",
" all 128 929 0.
404 0.758 0.701 0.45
\n",
" 0/2
3.27G 0.04357 0.06779 0.01869 0.1301 207 640: 100% 8/8 [00:04<00:00, 1.95
it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:0
5<00:00, 1.36
it/s]\n",
" all 128 929 0.
392 0.732 0.657 0.428
\n",
"\n",
" Epoch gpu_mem box obj cls total targets img_size\n",
" 1/2
5.12G 0.04461 0.05874 0.0169 0.1202 142 640: 100% 8/8 [00:01<00:00, 4.14
it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:01<00:00, 5.
75
it/s]\n",
" all 128 929 0.
403 0.772 0.703 0.453
\n",
" 1/2
7.47G 0.04308 0.06636 0.02083 0.1303 227 640: 100% 8/8 [00:02<00:00, 3.88
it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:01<00:00, 5.
07
it/s]\n",
" all 128 929 0.
387 0.737 0.657 0.432
\n",
"\n",
" Epoch gpu_mem box obj cls total targets img_size\n",
" 2/2
5.12G 0.04445 0.06545 0.01667 0.1266 149 640: 100% 8/8 [00:01<00:00, 4.15
it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:0
6<00:00, 1.18
it/s]\n",
" all 128 929 0.3
95 0.767 0.702 0.452
\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 1
5.2
MB\n",
"3 epochs completed in 0.00
6
hours.\n",
" 2/2
7.48G 0.04461 0.06864 0.01866 0.1319 191 640: 100% 8/8 [00:02<00:00, 3.57
it/s]\n",
" Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:0
2<00:00, 2.82
it/s]\n",
" all 128 929 0.3
85 0.742 0.658 0.431
\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 1
4.8
MB\n",
"3 epochs completed in 0.00
7
hours.\n",
"\n"
],
"name": "stdout"
...
...
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