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yolov5
Commits
5373a28c
提交
5373a28c
authored
8月 21, 2022
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
上级
8665d557
隐藏空白字符变更
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并排
正在显示
1 个修改的文件
包含
243 行增加
和
208 行删除
+243
-208
tutorial.ipynb
tutorial.ipynb
+243
-208
没有找到文件。
tutorial.ipynb
浏览文件 @
5373a28c
...
@@ -17,110 +17,121 @@
...
@@ -17,110 +17,121 @@
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...
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93881540-331e-4890-cd38-4c2776933238
"
"outputId": "
1af15107-bcd1-4e8f-b5bd-0ee1a737e051
"
},
},
"source": [
"source": [
"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
"# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
"# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
],
],
"execution_count":
null
,
"execution_count":
2
,
"outputs": [
"outputs": [
{
{
"output_type": "stream",
"output_type": "stream",
"name": "stdout",
"name": "stdout",
"text": [
"text": [
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False\n",
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False\n",
"YOLOv5 🚀 v6.2-
15-g61adf01
Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
"YOLOv5 🚀 v6.2-
41-g8665d55
Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
"\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...\n",
"100% 14.1M/14.1M [00:00<00:00,
39.3
MB/s]\n",
"100% 14.1M/14.1M [00:00<00:00,
41.7
MB/s]\n",
"\n",
"\n",
"Fusing layers... \n",
"Fusing layers... \n",
"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 14.
9
ms\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 14.
5
ms\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties,
22.0
ms\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties,
18.9
ms\n",
"Speed: 0.
6ms pre-process, 18.4ms inference, 24.1
ms NMS per image at shape (1, 3, 640, 640)\n",
"Speed: 0.
5ms pre-process, 16.7ms inference, 21.4
ms NMS per image at shape (1, 3, 640, 640)\n",
"Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
"Results saved to \u001b[1mruns/detect/exp\u001b[0m\n"
]
]
}
}
...
@@ -515,29 +533,29 @@
...
@@ -515,29 +533,29 @@
"id": "WQPtK1QYVaD_",
"id": "WQPtK1QYVaD_",
"colab": {
"colab": {
"base_uri": "https://localhost:8080/",
"base_uri": "https://localhost:8080/",
"height":
49
,
"height":
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,
"referenced_widgets": [
"referenced_widgets": [
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},
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"outputId": "
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"
"outputId": "
5f129105-eca5-4f33-fb1d-981255f814ad
"
},
},
"source": [
"source": [
"# Download COCO val\n",
"# Download COCO val\n",
"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
],
],
"execution_count":
null
,
"execution_count":
3
,
"outputs": [
"outputs": [
{
{
"output_type": "display_data",
"output_type": "display_data",
...
@@ -548,7 +566,24 @@
...
@@ -548,7 +566,24 @@
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"application/vnd.jupyter.widget-view+json": {
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"version_major": 2,
"version_minor": 0,
"version_minor": 0,
"model_id": "6d6b90ead2db49b3bdf624b6ba9b44e9"
"model_id": "da0946bcefd9414fa282977f7f609e36"
},
"application/json": {
"n": 0,
"total": 818322941,
"elapsed": 0.020366430282592773,
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"nrows": null,
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"unit": "B",
"unit_scale": true,
"rate": null,
"bar_format": null,
"postfix": null,
"unit_divisor": 1024,
"initial": 0,
"colour": null
}
}
},
},
"metadata": {}
"metadata": {}
...
@@ -562,48 +597,48 @@
...
@@ -562,48 +597,48 @@
"colab": {
"colab": {
"base_uri": "https://localhost:8080/"
"base_uri": "https://localhost:8080/"
},
},
"outputId": "
19a590ef-363e-424c-d9ce-78bbe0593cd5
"
"outputId": "
40d5d000-abee-46a0-c07d-1066e1662e01
"
},
},
"source": [
"source": [
"# Validate YOLOv5x on COCO val\n",
"# Validate YOLOv5x on COCO val\n",
"!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
"!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"
],
],
"execution_count":
null
,
"execution_count":
4
,
"outputs": [
"outputs": [
{
{
"output_type": "stream",
"output_type": "stream",
"name": "stdout",
"name": "stdout",
"text": [
"text": [
"\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
"\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
"YOLOv5 🚀 v6.2-
15-g61adf01
Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
"YOLOv5 🚀 v6.2-
41-g8665d55
Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
"\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt to yolov5x.pt...\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt to yolov5x.pt...\n",
"100% 166M/166M [00:
06<00:00, 28.1
MB/s]\n",
"100% 166M/166M [00:
10<00:00, 16.6
MB/s]\n",
"\n",
"\n",
"Fusing layers... \n",
"Fusing layers... \n",
"YOLOv5x summary: 444 layers, 86705005 parameters, 0 gradients\n",
"YOLOv5x summary: 444 layers, 86705005 parameters, 0 gradients\n",
"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
"Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
"100% 755k/755k [00:00<00:00,
47.3
MB/s]\n",
"100% 755k/755k [00:00<00:00,
1.39
MB/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<00:00, 10
756.32
it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<00:00, 10
506.48
it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
"
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:07<00:00, 2.33
it/s]\n",
"
Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:06<00:00, 2.36
it/s]\n",
" all 5000 36335 0.743 0.625 0.683 0.504\n",
"
all 5000 36335 0.743 0.625 0.683 0.504\n",
"Speed: 0.1ms pre-process, 4.6ms inference, 1.
2
ms NMS per image at shape (32, 3, 640, 640)\n",
"Speed: 0.1ms pre-process, 4.6ms inference, 1.
1
ms NMS per image at shape (32, 3, 640, 640)\n",
"\n",
"\n",
"Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n",
"Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n",
"loading annotations into memory...\n",
"loading annotations into memory...\n",
"Done (t=0.
41
s)\n",
"Done (t=0.
38
s)\n",
"creating index...\n",
"creating index...\n",
"index created!\n",
"index created!\n",
"Loading and preparing results...\n",
"Loading and preparing results...\n",
"DONE (t=5.
64
s)\n",
"DONE (t=5.
49
s)\n",
"creating index...\n",
"creating index...\n",
"index created!\n",
"index created!\n",
"Running per image evaluation...\n",
"Running per image evaluation...\n",
"Evaluate annotation type *bbox*\n",
"Evaluate annotation type *bbox*\n",
"DONE (t=7
6.8
0s).\n",
"DONE (t=7
2.1
0s).\n",
"Accumulating evaluation results...\n",
"Accumulating evaluation results...\n",
"DONE (t=1
4.61
s).\n",
"DONE (t=1
3.94
s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.506\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.506\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.688\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.549\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.549\n",
...
@@ -682,13 +717,13 @@
...
@@ -682,13 +717,13 @@
"colab": {
"colab": {
"base_uri": "https://localhost:8080/"
"base_uri": "https://localhost:8080/"
},
},
"outputId": "
47759d5e-34f0-4a6a-c714-ff533391cfff
"
"outputId": "
f0ce0354-7f50-4546-f3f9-672b4b522d59
"
},
},
"source": [
"source": [
"# Train YOLOv5s on COCO128 for 3 epochs\n",
"# Train YOLOv5s on COCO128 for 3 epochs\n",
"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
],
],
"execution_count":
null
,
"execution_count":
5
,
"outputs": [
"outputs": [
{
{
"output_type": "stream",
"output_type": "stream",
...
@@ -696,7 +731,7 @@
...
@@ -696,7 +731,7 @@
"text": [
"text": [
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v6.2-
15-g61adf01
Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
"YOLOv5 🚀 v6.2-
41-g8665d55
Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n",
"\n",
"\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, 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, copy_paste=0.0\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, 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, copy_paste=0.0\n",
"\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases\n",
"\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases\n",
...
@@ -705,8 +740,8 @@
...
@@ -705,8 +740,8 @@
"\n",
"\n",
"Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
"Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n",
"Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n",
"Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n",
"100% 6.66M/6.66M [00:00<00:00, 7
5.3
MB/s]\n",
"100% 6.66M/6.66M [00:00<00:00, 7
6.7
MB/s]\n",
"Dataset download success ✅ (0.
7
s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"Dataset download success ✅ (0.
5
s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"\n",
"\n",
" from n params module arguments \n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
...
@@ -740,33 +775,33 @@
...
@@ -740,33 +775,33 @@
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<00:00, 7
246.20
it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<00:00, 7
984.87
it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00,
986.21
it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00,
1018.19
it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 269.10it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 246.87it/s]\n",
"Plotting labels to runs/train/exp/labels.jpg... \n",
"\n",
"\n",
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
"Plotting labels to runs/train/exp/labels.jpg... \n",
"Image sizes 640 train, 640 val\n",
"Image sizes 640 train, 640 val\n",
"Using 8 dataloader workers\n",
"Using 8 dataloader workers\n",
"Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
"Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
"Starting training for 3 epochs...\n",
"Starting training for 3 epochs...\n",
"\n",
"\n",
"
Epoch gpu_mem box obj cls labels img_s
ize\n",
"
Epoch GPU_mem box_loss obj_loss cls_loss Instances S
ize\n",
"
0/2 3.76G 0.04529 0.06712 0.01835 323 640: 100% 8/8 [00:04<00:00, 1.65
it/s]\n",
"
0/2 3.77G 0.04529 0.06712 0.01835 323 640: 100% 8/8 [00:04<00:00, 1.96
it/s]\n",
"
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.07
it/s]\n",
"
Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.12
it/s]\n",
"
all 128 929 0.666 0.611 0.684 0.452
\n",
"
all 128 929 0.647 0.611 0.68 0.449
\n",
"\n",
"\n",
"
Epoch gpu_mem box obj cls labels img_s
ize\n",
"
Epoch GPU_mem box_loss obj_loss cls_loss Instances S
ize\n",
"
1/2 4.79G 0.04244 0.06423 0.01611 236 640: 100% 8/8 [00:01<00:00, 7.60
it/s]\n",
"
1/2 4.79G 0.04244 0.06423 0.01611 236 640: 100% 8/8 [00:00<00:00, 8.08
it/s]\n",
"
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00, 3.90
it/s]\n",
"
Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.43
it/s]\n",
"
all 128 929 0.746 0.626 0.722 0.481
\n",
"
all 128 929 0.737 0.623 0.72 0.482
\n",
"\n",
"\n",
"
Epoch gpu_mem box obj cls labels img_s
ize\n",
"
Epoch GPU_mem box_loss obj_loss cls_loss Instances S
ize\n",
"
2/2 4.79G 0.04695 0.06875 0.0173 189 640: 100% 8/8 [00:00<00:00, 8.49
it/s]\n",
"
2/2 4.79G 0.04695 0.06875 0.0173 189 640: 100% 8/8 [00:00<00:00, 8.87
it/s]\n",
"
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.24
it/s]\n",
"
Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:00<00:00, 4.57
it/s]\n",
"
all 128 929 0.774 0.647 0.746 0.499
\n",
"
all 128 929 0.76 0.631 0.733 0.497
\n",
"\n",
"\n",
"3 epochs completed in 0.003 hours.\n",
"3 epochs completed in 0.003 hours.\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB\n",
"Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB\n",
...
@@ -775,79 +810,79 @@
...
@@ -775,79 +810,79 @@
"Validating runs/train/exp/weights/best.pt...\n",
"Validating runs/train/exp/weights/best.pt...\n",
"Fusing layers... \n",
"Fusing layers... \n",
"Model summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
"Model summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
"
Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.11
it/s]\n",
"
Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 4/4 [00:03<00:00, 1.25
it/s]\n",
"
all 128 929 0.774 0.647 0.746 0.499
\n",
"
all 128 929 0.76 0.631 0.733 0.497
\n",
"
person 128 254 0.87 0.697 0.806 0.534
\n",
"
person 128 254 0.872 0.699 0.807 0.533
\n",
"
bicycle 128 6 0.759 0.528
0.725 0.444\n",
"
bicycle 128 6 0.761 0.536
0.725 0.444\n",
"
car 128 46 0.774 0.413 0.554 0.239
\n",
"
car 128 46 0.771 0.413 0.553 0.242
\n",
"
motorcycle 128 5 0.791 1 0.962 0.595
\n",
"
motorcycle 128 5 0.795 1 0.928 0.592
\n",
"
airplane 128 6 0.981
1 0.995 0.689\n",
"
airplane 128 6 0.983
1 0.995 0.689\n",
"
bus 128 7 0.65
0.714 0.755 0.691\n",
"
bus 128 7 0.648
0.714 0.755 0.691\n",
"
train 128 3 1 0.573 0.995 0.602
\n",
"
train 128 3 1 0.586 0.995 0.603
\n",
"
truck 128 12 0.613 0.333 0.489 0.263
\n",
"
truck 128 12 0.616 0.333 0.482 0.259
\n",
"
boat 128 6 0.933 0.333 0.507 0.209
\n",
"
boat 128 6 0.921 0.333 0.524 0.211
\n",
"
traffic light 128 14 0.76 0.228 0.367 0.209
\n",
"
traffic light 128 14 0.76 0.229 0.374 0.21
\n",
"
stop sign 128 2 0.821
1 0.995 0.821\n",
"
stop sign 128 2 0.824
1 0.995 0.821\n",
"
bench 128 9 0.824 0.526 0.676 0.31
\n",
"
bench 128 9 0.822 0.519 0.674 0.316
\n",
"
bird 128 16 0.974 1 0.995 0.611
\n",
"
bird 128 16 0.973 1 0.995 0.6
\n",
"
cat 128 4 0.859
1 0.995 0.772\n",
"
cat 128 4 0.861
1 0.995 0.772\n",
"
dog 128 9 1 0.666 0.883 0.647
\n",
"
dog 128 9 1 0.666 0.88 0.645
\n",
"
horse 128 2 0.84
1 0.995 0.622\n",
"
horse 128 2 0.845
1 0.995 0.622\n",
"
elephant 128 17 0.926
0.882 0.93 0.716\n",
"
elephant 128 17 0.923
0.882 0.93 0.716\n",
"
bear 128 1 0.709
1 0.995 0.995\n",
"
bear 128 1 0.71
1 0.995 0.995\n",
" zebra 128 4 0.866 1 0.995 0.922\n",
"
zebra 128 4 0.866 1 0.995 0.922\n",
"
giraffe 128 9 0.777 0.778
0.891 0.705\n",
"
giraffe 128 9 0.771 0.752
0.891 0.705\n",
"
backpack 128 6 0.894
0.5 0.753 0.294\n",
"
backpack 128 6 0.888
0.5 0.753 0.294\n",
"
umbrella 128 18 0.876 0.783 0.899 0.54
\n",
"
umbrella 128 18 0.876 0.784 0.899 0.539
\n",
"
handbag 128 19 0.799 0.209 0.335 0.179
\n",
"
handbag 128 19 0.8 0.21 0.335 0.181
\n",
"
tie 128 7 0.782
0.714 0.787 0.478\n",
"
tie 128 7 0.798
0.714 0.787 0.478\n",
"
suitcase 128 4 0.658
1 0.945 0.581\n",
"
suitcase 128 4 0.662
1 0.945 0.581\n",
"
frisbee 128 5 0.726 0.8 0.76
0.701\n",
"
frisbee 128 5 0.727 0.8 0.759
0.701\n",
"
skis 128 1 0.8 1 0.995 0.103
\n",
"
skis 128 1 0 0 0.0585 0.0139
\n",
"
snowboard 128 7 0.815 0.714 0.852 0.574
\n",
"
snowboard 128 7 0.807 0.714 0.853 0.591
\n",
" sports ball 128 6 0.649 0.667 0.602 0.307\n",
"
sports ball 128 6 0.649 0.667 0.602 0.307\n",
"
kite 128 10 0.7 0.47 0.546 0.206
\n",
"
kite 128 10 0.7 0.47 0.543 0.212
\n",
"
baseball bat 128 4 1 0.497 0.544 0.182
\n",
"
baseball bat 128 4 1 0.496 0.544 0.208
\n",
"
baseball glove 128 7 0.598 0.429 0.47 0.31
\n",
"
baseball glove 128 7 0.619 0.429 0.47 0.313
\n",
"
skateboard 128 5 0.851 0.6 0.685 0.495
\n",
"
skateboard 128 5 0.847 0.6 0.712 0.496
\n",
"
tennis racket 128 7 0.754
0.429 0.544 0.34\n",
"
tennis racket 128 7 0.757
0.429 0.544 0.34\n",
"
bottle 128 18 0.564 0.333 0.53 0.264
\n",
"
bottle 128 18 0.546 0.334 0.53 0.259
\n",
"
wine glass 128 16 0.715
0.875 0.907 0.528\n",
"
wine glass 128 16 0.716
0.875 0.907 0.528\n",
"
cup 128 36 0.825 0.639 0.803 0.535
\n",
"
cup 128 36 0.826 0.639 0.802 0.538
\n",
"
fork 128 6 1 0.329 0.5 0.38
4\n",
"
fork 128 6 1 0.329 0.496 0.36
4\n",
"
knife 128 16 0.706 0.625 0.666 0.405
\n",
"
knife 128 16 0.706 0.625 0.604 0.382
\n",
"
spoon 128 22 0.836 0.464 0.619 0.379
\n",
"
spoon 128 22 0.837 0.467 0.618 0.38
\n",
"
bowl 128 28 0.763 0.607 0.717 0.516
\n",
"
bowl 128 28 0.757 0.607 0.714 0.519
\n",
"
banana 128 1 0.886
1 0.995 0.399\n",
"
banana 128 1 0.889
1 0.995 0.399\n",
"
sandwich 128 2 1 0 0.62 0.54
6\n",
"
sandwich 128 2 1 0 0.638 0.5
6\n",
"
orange 128 4 1 0.75 0.995 0.62
2\n",
"
orange 128 4 1 0.663 0.945 0.59
2\n",
"
broccoli 128 11 0.548 0.443 0.467 0.35
\n",
"
broccoli 128 11 0.545 0.437 0.471 0.351
\n",
"
carrot 128 24 0.7 0.585 0.699 0.458
\n",
"
carrot 128 24 0.701 0.585 0.697 0.454
\n",
"
hot dog 128 2 0.502
1 0.995 0.995\n",
"
hot dog 128 2 0.501
1 0.995 0.995\n",
"
pizza 128 5 0.813
1 0.962 0.747\n",
"
pizza 128 5 0.809
1 0.962 0.747\n",
"
donut 128 14 0.662 1 0.96 0.838
\n",
"
donut 128 14 0.66 1 0.96 0.837
\n",
"
cake 128 4 0.868
1 0.995 0.822\n",
"
cake 128 4 0.871
1 0.995 0.822\n",
"
chair 128 35 0.538 0.571 0.594 0.322
\n",
"
chair 128 35 0.536 0.561 0.595 0.325
\n",
"
couch 128 6 0.924 0.667 0.828 0.538
\n",
"
couch 128 6 0.931 0.667 0.828 0.539
\n",
"
potted plant 128 14 0.731 0.786 0.824
0.495\n",
"
potted plant 128 14 0.733 0.786 0.823
0.495\n",
"
bed 128 3 0.736 0.333 0.83 0.425
\n",
"
bed 128 3 0.691 0.333 0.83 0.422
\n",
"
dining table 128 13 0.624 0.259 0.494 0.336
\n",
"
dining table 128 13 0.621 0.255 0.513 0.34
\n",
"
toilet 128 2 0.79
1 0.995 0.846\n",
"
toilet 128 2 0.797
1 0.995 0.846\n",
"
tv 128 2 0.574
1 0.995 0.796\n",
"
tv 128 2 0.57
1 0.995 0.796\n",
"
laptop 128 3 1 0 0.695 0.367
\n",
"
laptop 128 3 1 0 0.694 0.316
\n",
"
mouse 128 2 1 0 0.173 0.0864
\n",
"
mouse 128 2 1 0 0.172 0.0862
\n",
"
remote 128 8 1 0.62 0.634 0.557
\n",
"
remote 128 8 1 0.62 0.634 0.551
\n",
"
cell phone 128 8 0.612 0.397 0.437 0.221
\n",
"
cell phone 128 8 0.591 0.375 0.425 0.216
\n",
"
microwave 128 3 0.741
1 0.995 0.766\n",
"
microwave 128 3 0.736
1 0.995 0.766\n",
"
oven 128 5 0.33 0.4 0.449 0.3
\n",
"
oven 128 5 0.333 0.4 0.438 0.299
\n",
"
sink 128 6 0.444 0.333 0.331 0.231
\n",
"
sink 128 6 0.427 0.333 0.329 0.23
\n",
"
refrigerator 128 5 0.561 0.8 0.798 0.546
\n",
"
refrigerator 128 5 0.559 0.8 0.798 0.565
\n",
"
book 128 29 0.635 0.276 0.355 0.164
\n",
"
book 128 29 0.558 0.241 0.307 0.155
\n",
"
clock 128 9 0.766 0.889 0.888 0.73
\n",
"
clock 128 9 0.761 0.889 0.888 0.711
\n",
"
vase 128 2 0.303
1 0.995 0.895\n",
"
vase 128 2 0.287
1 0.995 0.895\n",
"
scissors 128 1 1 0 0.332 0.0397
\n",
"
scissors 128 1 1 0 0.497 0.0574
\n",
"
teddy bear 128 21 0.842 0.508 0.739 0.49
9\n",
"
teddy bear 128 21 0.838 0.493 0.745 0.50
9\n",
"
toothbrush 128 5 0.787
1 0.928 0.59\n",
"
toothbrush 128 5 0.789
1 0.928 0.59\n",
"Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
"Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
]
]
}
}
...
...
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