Skip to content

Y
yolov5
提交 c9d47ae0 authored 作者: Glenn Jocher's avatar Glenn Jocher
浏览文件
操作

Created using Colaboratory

上级 b32f67f6
显示空白字符变更
内嵌 并排
正在显示 包含 67 行增加 和 67 行删除
tutorial.ipynb
浏览文件 @ c9d47ae0
...@@ -14,7 +14,7 @@ ...@@ -14,7 +14,7 @@
"accelerator": "GPU", "accelerator": "GPU",
"widgets": { "widgets": {
"application/vnd.jupyter.widget-state+json": { "application/vnd.jupyter.widget-state+json": {
"300b4d5355ef4967bd5246afeef6eef5": { "1f7df330663048998adcf8a45bc8f69b": {
"model_module": "@jupyter-widgets/controls", "model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel", "model_name": "HBoxModel",
"model_module_version": "1.5.0", "model_module_version": "1.5.0",
...@@ -29,14 +29,14 @@ ...@@ -29,14 +29,14 @@
"_view_name": "HBoxView", "_view_name": "HBoxView",
"box_style": "", "box_style": "",
"children": [ "children": [
"IPY_MODEL_84e6829bb88845a8a4f42700b8496925", "IPY_MODEL_e896e6096dd244c59d7955e2035cd729",
"IPY_MODEL_c038e52d41bf4d5b9602930c3d074087", "IPY_MODEL_a6ff238c29984b24bf6d0bd175c19430",
"IPY_MODEL_2667604641764341b0bc8c6afea438fd" "IPY_MODEL_3c085ba3f3fd4c3c8a6bb41b41ce1479"
], ],
"layout": "IPY_MODEL_98b3a4806ed14102b0d75e6c571d6134" "layout": "IPY_MODEL_16b0c8aa6e0f427e8a54d3791abb7504"
} }
}, },
"84e6829bb88845a8a4f42700b8496925": { "e896e6096dd244c59d7955e2035cd729": {
"model_module": "@jupyter-widgets/controls", "model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel", "model_name": "HTMLModel",
"model_module_version": "1.5.0", "model_module_version": "1.5.0",
...@@ -51,13 +51,13 @@ ...@@ -51,13 +51,13 @@
"_view_name": "HTMLView", "_view_name": "HTMLView",
"description": "", "description": "",
"description_tooltip": null, "description_tooltip": null,
"layout": "IPY_MODEL_c66a77395e42424d904699edcbb67291", "layout": "IPY_MODEL_c7b2dd0f78384cad8e400b282996cdf5",
"placeholder": "​", "placeholder": "​",
"style": "IPY_MODEL_c4bbc15bf853439399dbcf1d40a5a407", "style": "IPY_MODEL_6a27e43b0e434edd82ee63f0a91036ca",
"value": "100%" "value": "100%"
} }
}, },
"c038e52d41bf4d5b9602930c3d074087": { "a6ff238c29984b24bf6d0bd175c19430": {
"model_module": "@jupyter-widgets/controls", "model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel", "model_name": "FloatProgressModel",
"model_module_version": "1.5.0", "model_module_version": "1.5.0",
...@@ -73,15 +73,15 @@ ...@@ -73,15 +73,15 @@
"bar_style": "success", "bar_style": "success",
"description": "", "description": "",
"description_tooltip": null, "description_tooltip": null,
"layout": "IPY_MODEL_0aaabfac395b43afbdd6d752c502bbf6", "layout": "IPY_MODEL_cce0e6c0c4ec442cb47e65c674e02e92",
"max": 818322941, "max": 818322941,
"min": 0, "min": 0,
"orientation": "horizontal", "orientation": "horizontal",
"style": "IPY_MODEL_3786d970492b4aa38f886f2572fd958c", "style": "IPY_MODEL_c5b9f38e2f0d4f9aa97fe87265263743",
"value": 818322941 "value": 818322941
} }
}, },
"2667604641764341b0bc8c6afea438fd": { "3c085ba3f3fd4c3c8a6bb41b41ce1479": {
"model_module": "@jupyter-widgets/controls", "model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel", "model_name": "HTMLModel",
"model_module_version": "1.5.0", "model_module_version": "1.5.0",
...@@ -96,13 +96,13 @@ ...@@ -96,13 +96,13 @@
"_view_name": "HTMLView", "_view_name": "HTMLView",
"description": "", "description": "",
"description_tooltip": null, "description_tooltip": null,
"layout": "IPY_MODEL_b86d0f2d7be74cebbcaa884b53123eeb", "layout": "IPY_MODEL_df554fb955c7454696beac5a82889386",
"placeholder": "​", "placeholder": "​",
"style": "IPY_MODEL_fa7b1497925a457f89286a71f073f416", "style": "IPY_MODEL_74e9112a87a242f4831b7d68c7da6333",
"value": " 780M/780M [00:57<00:00, 10.1MB/s]" "value": " 780M/780M [00:05<00:00, 126MB/s]"
} }
}, },
"98b3a4806ed14102b0d75e6c571d6134": { "16b0c8aa6e0f427e8a54d3791abb7504": {
"model_module": "@jupyter-widgets/base", "model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel", "model_name": "LayoutModel",
"model_module_version": "1.2.0", "model_module_version": "1.2.0",
...@@ -154,7 +154,7 @@ ...@@ -154,7 +154,7 @@
"width": null "width": null
} }
}, },
"c66a77395e42424d904699edcbb67291": { "c7b2dd0f78384cad8e400b282996cdf5": {
"model_module": "@jupyter-widgets/base", "model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel", "model_name": "LayoutModel",
"model_module_version": "1.2.0", "model_module_version": "1.2.0",
...@@ -206,7 +206,7 @@ ...@@ -206,7 +206,7 @@
"width": null "width": null
} }
}, },
"c4bbc15bf853439399dbcf1d40a5a407": { "6a27e43b0e434edd82ee63f0a91036ca": {
"model_module": "@jupyter-widgets/controls", "model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel", "model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0", "model_module_version": "1.5.0",
...@@ -221,7 +221,7 @@ ...@@ -221,7 +221,7 @@
"description_width": "" "description_width": ""
} }
}, },
"0aaabfac395b43afbdd6d752c502bbf6": { "cce0e6c0c4ec442cb47e65c674e02e92": {
"model_module": "@jupyter-widgets/base", "model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel", "model_name": "LayoutModel",
"model_module_version": "1.2.0", "model_module_version": "1.2.0",
...@@ -273,7 +273,7 @@ ...@@ -273,7 +273,7 @@
"width": null "width": null
} }
}, },
"3786d970492b4aa38f886f2572fd958c": { "c5b9f38e2f0d4f9aa97fe87265263743": {
"model_module": "@jupyter-widgets/controls", "model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel", "model_name": "ProgressStyleModel",
"model_module_version": "1.5.0", "model_module_version": "1.5.0",
...@@ -289,7 +289,7 @@ ...@@ -289,7 +289,7 @@
"description_width": "" "description_width": ""
} }
}, },
"b86d0f2d7be74cebbcaa884b53123eeb": { "df554fb955c7454696beac5a82889386": {
"model_module": "@jupyter-widgets/base", "model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel", "model_name": "LayoutModel",
"model_module_version": "1.2.0", "model_module_version": "1.2.0",
...@@ -341,7 +341,7 @@ ...@@ -341,7 +341,7 @@
"width": null "width": null
} }
}, },
"fa7b1497925a457f89286a71f073f416": { "74e9112a87a242f4831b7d68c7da6333": {
"model_module": "@jupyter-widgets/controls", "model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel", "model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0", "model_module_version": "1.5.0",
...@@ -401,7 +401,7 @@ ...@@ -401,7 +401,7 @@
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"outputId": "32e3bc15-6d02-4352-f0a3-912059d134a5" "outputId": "f9f016ad-3dcf-4bd2-e1c3-d5b79efc6f32"
}, },
"source": [ "source": [
"!git clone https://github.com/ultralytics/yolov5 # clone\n", "!git clone https://github.com/ultralytics/yolov5 # clone\n",
...@@ -418,7 +418,7 @@ ...@@ -418,7 +418,7 @@
"output_type": "stream", "output_type": "stream",
"name": "stderr", "name": "stderr",
"text": [ "text": [
"YOLOv5 🚀 v6.2-256-g0051615 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
] ]
}, },
{ {
...@@ -459,7 +459,7 @@ ...@@ -459,7 +459,7 @@
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"outputId": "8e81d6e9-0360-4212-cd61-9a5a58d3f703" "outputId": "b4db5c49-f501-4505-cf0d-a1d35236c485"
}, },
"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",
...@@ -472,16 +472,16 @@ ...@@ -472,16 +472,16 @@
"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, vid_stride=1\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, vid_stride=1\n",
"YOLOv5 🚀 v6.2-256-g0051615 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\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/v7.0/yolov5s.pt to yolov5s.pt...\n",
"100% 14.1M/14.1M [00:00<00:00, 19.5MB/s]\n", "100% 14.1M/14.1M [00:00<00:00, 116MB/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, 17.5ms\n", "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 17.0ms\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 18.0ms\n", "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 14.3ms\n",
"Speed: 0.5ms pre-process, 17.8ms inference, 17.6ms NMS per image at shape (1, 3, 640, 640)\n", "Speed: 0.5ms pre-process, 15.7ms inference, 18.6ms 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"
] ]
} }
...@@ -515,20 +515,20 @@ ...@@ -515,20 +515,20 @@
"base_uri": "https://localhost:8080/", "base_uri": "https://localhost:8080/",
"height": 49, "height": 49,
"referenced_widgets": [ "referenced_widgets": [
"300b4d5355ef4967bd5246afeef6eef5", "1f7df330663048998adcf8a45bc8f69b",
"84e6829bb88845a8a4f42700b8496925", "e896e6096dd244c59d7955e2035cd729",
"c038e52d41bf4d5b9602930c3d074087", "a6ff238c29984b24bf6d0bd175c19430",
"2667604641764341b0bc8c6afea438fd", "3c085ba3f3fd4c3c8a6bb41b41ce1479",
"98b3a4806ed14102b0d75e6c571d6134", "16b0c8aa6e0f427e8a54d3791abb7504",
"c66a77395e42424d904699edcbb67291", "c7b2dd0f78384cad8e400b282996cdf5",
"c4bbc15bf853439399dbcf1d40a5a407", "6a27e43b0e434edd82ee63f0a91036ca",
"0aaabfac395b43afbdd6d752c502bbf6", "cce0e6c0c4ec442cb47e65c674e02e92",
"3786d970492b4aa38f886f2572fd958c", "c5b9f38e2f0d4f9aa97fe87265263743",
"b86d0f2d7be74cebbcaa884b53123eeb", "df554fb955c7454696beac5a82889386",
"fa7b1497925a457f89286a71f073f416" "74e9112a87a242f4831b7d68c7da6333"
] ]
}, },
"outputId": "61ffec5e-90ea-44f6-c0ea-b006e6e7072f" "outputId": "c7d0a0d2-abfb-44c3-d60d-f99d0e7aabad"
}, },
"source": [ "source": [
"# Download COCO val\n", "# Download COCO val\n",
...@@ -546,7 +546,7 @@ ...@@ -546,7 +546,7 @@
"application/vnd.jupyter.widget-view+json": { "application/vnd.jupyter.widget-view+json": {
"version_major": 2, "version_major": 2,
"version_minor": 0, "version_minor": 0,
"model_id": "300b4d5355ef4967bd5246afeef6eef5" "model_id": "1f7df330663048998adcf8a45bc8f69b"
} }
}, },
"metadata": {} "metadata": {}
...@@ -560,7 +560,7 @@ ...@@ -560,7 +560,7 @@
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"outputId": "aa5d5cea-14c1-4a19-bfdf-95b7164962cf" "outputId": "5fc61358-7bc5-4310-a310-9059f66c6322"
}, },
"source": [ "source": [
"# Validate YOLOv5s on COCO val\n", "# Validate YOLOv5s on COCO val\n",
...@@ -573,30 +573,30 @@ ...@@ -573,30 +573,30 @@
"name": "stdout", "name": "stdout",
"text": [ "text": [
"\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, 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=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, 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 🚀 v6.2-256-g0051615 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\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",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2066.57it/s]\n", "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 1977.30it/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 Instances P R mAP50 mAP50-95: 100% 157/157 [01:09<00:00, 2.26it/s]\n", " Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:12<00:00, 2.17it/s]\n",
" all 5000 36335 0.67 0.521 0.566 0.371\n", " all 5000 36335 0.67 0.521 0.566 0.371\n",
"Speed: 0.1ms pre-process, 2.7ms inference, 1.9ms NMS per image at shape (32, 3, 640, 640)\n", "Speed: 0.1ms pre-process, 2.9ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640)\n",
"\n", "\n",
"Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n", "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n",
"loading annotations into memory...\n", "loading annotations into memory...\n",
"Done (t=0.82s)\n", "Done (t=0.43s)\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=5.49s)\n", "DONE (t=5.85s)\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=74.26s).\n", "DONE (t=82.22s).\n",
"Accumulating evaluation results...\n", "Accumulating evaluation results...\n",
"DONE (t=13.46s).\n", "DONE (t=14.92s).\n",
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n", " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n",
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n", " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n",
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n", " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n",
...@@ -676,7 +676,7 @@ ...@@ -676,7 +676,7 @@
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"outputId": "f0fcdc77-5326-41e1-bacc-be5432eefa2a" "outputId": "721b9028-767f-4a05-c964-692c245f7398"
}, },
"source": [ "source": [
"# Train YOLOv5s on COCO128 for 3 epochs\n", "# Train YOLOv5s on COCO128 for 3 epochs\n",
...@@ -690,7 +690,7 @@ ...@@ -690,7 +690,7 @@
"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 🚀 v6.2-256-g0051615 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\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[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n", "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
...@@ -699,8 +699,8 @@ ...@@ -699,8 +699,8 @@
"\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, 39.8MB/s]\n", "100% 6.66M/6.66M [00:00<00:00, 261MB/s]\n",
"Dataset download success ✅ (0.8s), saved to \u001b[1m/content/datasets\u001b[0m\n", "Dataset download success ✅ (0.3s), 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",
...@@ -734,11 +734,11 @@ ...@@ -734,11 +734,11 @@
"\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... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 2084.63it/s]\n", "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1911.57it/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, 255.09it/s]\n", "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 229.69it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n", "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 106.58it/s]\n", "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 97.70it/s] \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", "Plotting labels to runs/train/exp/labels.jpg... \n",
...@@ -748,18 +748,18 @@ ...@@ -748,18 +748,18 @@
"Starting training for 3 epochs...\n", "Starting training for 3 epochs...\n",
"\n", "\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n", " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 0/2 3.74G 0.04618 0.07207 0.017 232 640: 100% 8/8 [00:06<00:00, 1.33it/s]\n", " 0/2 3.74G 0.04618 0.07207 0.017 232 640: 100% 8/8 [00:07<00:00, 1.10it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.99it/s]\n", " Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.28it/s]\n",
" all 128 929 0.672 0.594 0.682 0.451\n", " all 128 929 0.672 0.594 0.682 0.451\n",
"\n", "\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n", " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 1/2 5.36G 0.04623 0.06888 0.01821 201 640: 100% 8/8 [00:02<00:00, 3.28it/s]\n", " 1/2 5.36G 0.04623 0.06888 0.01821 201 640: 100% 8/8 [00:02<00:00, 3.29it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 3.02it/s]\n", " Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 3.17it/s]\n",
" all 128 929 0.721 0.639 0.724 0.48\n", " all 128 929 0.721 0.639 0.724 0.48\n",
"\n", "\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n", " Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 2/2 5.36G 0.04361 0.06479 0.01698 227 640: 100% 8/8 [00:02<00:00, 3.50it/s]\n", " 2/2 5.36G 0.04361 0.06479 0.01698 227 640: 100% 8/8 [00:02<00:00, 3.46it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 3.05it/s]\n", " Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 3.11it/s]\n",
" all 128 929 0.758 0.641 0.731 0.487\n", " all 128 929 0.758 0.641 0.731 0.487\n",
"\n", "\n",
"3 epochs completed in 0.005 hours.\n", "3 epochs completed in 0.005 hours.\n",
...@@ -769,7 +769,7 @@ ...@@ -769,7 +769,7 @@
"Validating runs/train/exp/weights/best.pt...\n", "Validating runs/train/exp/weights/best.pt...\n",
"Fusing layers... \n", "Fusing layers... \n",
"Model summary: 157 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n", "Model summary: 157 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:03<00:00, 1.09it/s]\n", " Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:03<00:00, 1.05it/s]\n",
" all 128 929 0.757 0.641 0.732 0.487\n", " all 128 929 0.757 0.641 0.732 0.487\n",
" person 128 254 0.86 0.705 0.804 0.528\n", " person 128 254 0.86 0.705 0.804 0.528\n",
" bicycle 128 6 0.773 0.578 0.725 0.426\n", " bicycle 128 6 0.773 0.578 0.725 0.426\n",
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论