From f2b8f3fe3a3ae2b601706e5bea9f25265eb2fcd9 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Thu, 25 Aug 2022 22:17:28 +0200 Subject: [PATCH] Created using Colaboratory --- tutorial.ipynb | 474 +++++++++++++++++++++++-------------------------- 1 file changed, 218 insertions(+), 256 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index 3af5517..1284006 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -16,121 +16,110 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "da0946bcefd9414fa282977f7f609e36": { + "9b8caa3522fc4cbab31e13b5dfc7808d": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", - "model_module_version": "2.0.0", + "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", + "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "2.0.0", + "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ - 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"border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, + "border": null, "bottom": null, "display": null, "flex": null, @@ -158,6 +147,8 @@ "object_position": null, "order": null, "overflow": null, + "overflow_x": null, + "overflow_y": null, "padding": null, "right": null, "top": null, @@ -165,25 +156,22 @@ "width": null } }, - "abf60d6b8ea847f9bb358ae2b045458b": { + "60b913d755b34d638478e30705a2dde1": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", - "model_module_version": "2.0.0", + "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", - "_model_module_version": "2.0.0", + "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", + "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, - "border_bottom": null, - "border_left": null, - 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"_view_module_version": "2.0.0", + "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, - "border_bottom": null, - "border_left": null, - "border_right": null, - "border_top": null, + "border": null, "bottom": null, "display": null, "flex": null, @@ -351,6 +334,8 @@ "object_position": null, "order": null, "overflow": null, + "overflow_x": null, + "overflow_y": null, "padding": null, "right": null, "top": null, @@ -358,22 +343,19 @@ "width": null } }, - "648b3512bb7d4ccca5d75af36c133e92": { + "5966ba6e6f114d8c9d8d1d6b1bd4f4c7": { "model_module": "@jupyter-widgets/controls", - "model_name": "HTMLStyleModel", - "model_module_version": "2.0.0", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "2.0.0", - "_model_name": "HTMLStyleModel", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", - "_view_module_version": "2.0.0", + "_view_module_version": "1.2.0", "_view_name": "StyleView", - "background": null, - "description_width": "", - "font_size": null, - "text_color": null + "description_width": "" } } } @@ -420,7 +402,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "4200fd6f-c6f5-4505-a4f9-a918f3ed1f86" + "outputId": "0f9ee467-cea4-48e8-9050-7a76ae1b6141" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", @@ -431,13 +413,13 @@ "import utils\n", "display = utils.notebook_init() # checks" ], - "execution_count": null, + "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "YOLOv5 🚀 v6.2-41-g8665d55 Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n" + "YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n" ] }, { @@ -477,29 +459,29 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "1af15107-bcd1-4e8f-b5bd-0ee1a737e051" + "outputId": "60647b99-e8d4-402c-f444-331bf6746da4" }, "source": [ "!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)" ], - "execution_count": null, + "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "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", - "YOLOv5 🚀 v6.2-41-g8665d55 Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", + "YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", "\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, 41.7MB/s]\n", + "100% 14.1M/14.1M [00:00<00:00, 27.8MB/s]\n", "\n", "Fusing layers... \n", "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n", - "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 14.5ms\n", - "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 18.9ms\n", - "Speed: 0.5ms pre-process, 16.7ms inference, 21.4ms NMS per image at shape (1, 3, 640, 640)\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 14.8ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 20.1ms\n", + "Speed: 0.6ms pre-process, 17.4ms inference, 21.6ms NMS per image at shape (1, 3, 640, 640)\n", "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n" ] } @@ -531,29 +513,29 @@ "id": "WQPtK1QYVaD_", "colab": { "base_uri": "https://localhost:8080/", - "height": 17, + "height": 49, "referenced_widgets": [ - "da0946bcefd9414fa282977f7f609e36", - "7838c0af44244ccc906c413cea0989d7", - "309ea78b3e814198b4080beb878d5329", - "b2d1d998e5db4ca1a36280902e1647c7", - "e7d7f56c77884717ba122f1d603c0852", - "abf60d6b8ea847f9bb358ae2b045458b", - "379196a2761b4a29aca8ef088dc60c10", - 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"model_id": "da0946bcefd9414fa282977f7f609e36" - }, - "application/json": { - "n": 0, - "total": 818322941, - "elapsed": 0.020366430282592773, - "ncols": null, - "nrows": null, - "prefix": "", - "ascii": false, - "unit": "B", - "unit_scale": true, - "rate": null, - "bar_format": null, - "postfix": null, - "unit_divisor": 1024, - "initial": 0, - "colour": null + "model_id": "9b8caa3522fc4cbab31e13b5dfc7808d" } }, "metadata": {} @@ -595,60 +560,57 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "40d5d000-abee-46a0-c07d-1066e1662e01" + "outputId": "daf60b1b-b098-4657-c863-584f4c9cf078" }, "source": [ - "# Validate YOLOv5x on COCO val\n", - "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half" + "# Validate YOLOv5s on COCO val\n", + "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half" ], - "execution_count": null, + "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "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", - "YOLOv5 🚀 v6.2-41-g8665d55 Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", - "\n", - "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt to yolov5x.pt...\n", - "100% 166M/166M [00:10<00:00, 16.6MB/s]\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, 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 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", "\n", "Fusing layers... \n", - "YOLOv5x summary: 444 layers, 86705005 parameters, 0 gradients\n", + "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n", "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n", - "100% 755k/755k [00:00<00:00, 1.39MB/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, 10506.48it/s]\n", + "100% 755k/755k [00:00<00:00, 52.7MB/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, 10509.20it/s]\n", "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n", - " Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:06<00:00, 2.36it/s]\n", - " all 5000 36335 0.743 0.625 0.683 0.504\n", - "Speed: 0.1ms pre-process, 4.6ms inference, 1.1ms NMS per image at shape (32, 3, 640, 640)\n", + " Class Images Instances P R mAP@.5 mAP@.5:.95: 100% 157/157 [00:50<00:00, 3.10it/s]\n", + " all 5000 36335 0.67 0.521 0.566 0.371\n", + "Speed: 0.1ms pre-process, 1.0ms inference, 1.5ms NMS per image at shape (32, 3, 640, 640)\n", "\n", - "Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n", + "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n", "loading annotations into memory...\n", - "Done (t=0.38s)\n", + "Done (t=0.81s)\n", "creating index...\n", "index created!\n", "Loading and preparing results...\n", - "DONE (t=5.49s)\n", + "DONE (t=5.62s)\n", "creating index...\n", "index created!\n", "Running per image evaluation...\n", "Evaluate annotation type *bbox*\n", - "DONE (t=72.10s).\n", + "DONE (t=77.03s).\n", "Accumulating evaluation results...\n", - "DONE (t=13.94s).\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=0.75 | area= all | maxDets=100 ] = 0.549\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.340\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.558\n", - " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.651\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.382\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.631\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.684\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.528\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.737\n", - " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.833\n", + "DONE (t=14.63s).\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=0.75 | area= all | maxDets=100 ] = 0.402\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.724\n", "Results saved to \u001b[1mruns/val/exp\u001b[0m\n" ] } @@ -715,13 +677,13 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "f0ce0354-7f50-4546-f3f9-672b4b522d59" + "outputId": "baa6d4be-3379-4aab-844a-d5a5396c0e49" }, "source": [ "# Train YOLOv5s on COCO128 for 3 epochs\n", "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache" ], - "execution_count": null, + "execution_count": 5, "outputs": [ { "output_type": "stream", @@ -729,7 +691,7 @@ "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[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", - "YOLOv5 🚀 v6.2-41-g8665d55 Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", + "YOLOv5 🚀 v6.2-56-g30e674b Python-3.7.13 torch-1.12.1+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\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[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs in Weights & Biases\n", @@ -738,8 +700,8 @@ "\n", "Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n", "Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n", - "100% 6.66M/6.66M [00:00<00:00, 76.7MB/s]\n", - "Dataset download success ✅ (0.5s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "100% 6.66M/6.66M [00:00<00:00, 41.1MB/s]\n", + "Dataset download success ✅ (0.8s), saved to \u001b[1m/content/datasets\u001b[0m\n", "\n", " from n params module arguments \n", " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", @@ -773,11 +735,11 @@ "\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[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, 7984.87it/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, 9659.25it/s]\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, 1018.19it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 951.31it/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