From 5f5d958598c8fc681562ca1ead88d2253bff91ef Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sun, 21 Aug 2022 17:01:05 +0200 Subject: [PATCH] Created using Colaboratory --- tutorial.ipynb | 451 ++++++++++++++++++++++++++----------------------- 1 file changed, 243 insertions(+), 208 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index 8753a2310b1d..5b7b1f287d7e 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -17,110 +17,121 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "6d6b90ead2db49b3bdf624b6ba9b44e9": { + "da0946bcefd9414fa282977f7f609e36": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", - "model_module_version": "1.5.0", + "model_module_version": "2.0.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", + "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", + "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": [ - 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"_model_module_version": "1.5.0", + "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", + "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, - "c4c7dc45a1c24dc4b2c709e21271a37e": { + "30f22a3e42d24f10ad9851f40a6703f3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", - "model_module_version": "1.2.0", + "model_module_version": "2.0.0", "state": { "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", + "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", + "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, - "border": null, + "border_bottom": null, + "border_left": null, + "border_right": null, + "border_top": null, "bottom": null, "display": null, "flex": null, @@ -335,8 +352,6 @@ "object_position": null, "order": null, "overflow": null, - "overflow_x": null, - "overflow_y": null, "padding": null, "right": null, "top": null, @@ -344,19 +359,22 @@ "width": null } }, - "09c43ffe2c7e4bdc9489e83f9d82ab73": { + "648b3512bb7d4ccca5d75af36c133e92": { "model_module": "@jupyter-widgets/controls", - "model_name": "DescriptionStyleModel", - "model_module_version": "1.5.0", + "model_name": "HTMLStyleModel", + "model_module_version": "2.0.0", "state": { "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_module_version": "2.0.0", + "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", + "_view_module_version": "2.0.0", "_view_name": "StyleView", - "description_width": "" + "background": null, + "description_width": "", + "font_size": null, + "text_color": null } } } @@ -404,7 +422,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "508de90c-846e-495d-c7d6-50681af62a98" + "outputId": "4200fd6f-c6f5-4505-a4f9-a918f3ed1f86" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", @@ -415,13 +433,13 @@ "import utils\n", "display = utils.notebook_init() # checks" ], - "execution_count": null, + "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "YOLOv5 🚀 v6.2-15-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" ] }, { @@ -461,29 +479,29 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "93881540-331e-4890-cd38-4c2776933238" + "outputId": "1af15107-bcd1-4e8f-b5bd-0ee1a737e051" }, "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 🚀 v6.2-15-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", "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, 39.3MB/s]\n", + "100% 14.1M/14.1M [00:00<00:00, 41.7MB/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, 14.9ms\n", - "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 22.0ms\n", - "Speed: 0.6ms pre-process, 18.4ms inference, 24.1ms NMS per image at shape (1, 3, 640, 640)\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 14.5ms\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", "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n" ] } @@ -515,29 +533,29 @@ "id": "WQPtK1QYVaD_", "colab": { "base_uri": "https://localhost:8080/", - "height": 49, + "height": 17, "referenced_widgets": [ - "6d6b90ead2db49b3bdf624b6ba9b44e9", - "cb77443edb9e43328a56aaa4413a0df3", - "954c8b8699e143bf92be6bfc02fc52f6", - "a64775946e13477f83d8bba6086385b9", - "1413611b7f4f4ef99e4f541f5ca35ed6", - "00737f5558eb4fbd968172acb978e54a", - "f03e5ddfd1c04bedaf68ab02c3f6f0ea", - "6926db7e0035455f99e1dd4508c4b19c", - "a6a52c9f828b458e97ddf7a11ae9275f", - "c4c7dc45a1c24dc4b2c709e21271a37e", - "09c43ffe2c7e4bdc9489e83f9d82ab73" + "da0946bcefd9414fa282977f7f609e36", + "7838c0af44244ccc906c413cea0989d7", + "309ea78b3e814198b4080beb878d5329", + "b2d1d998e5db4ca1a36280902e1647c7", + "e7d7f56c77884717ba122f1d603c0852", + "abf60d6b8ea847f9bb358ae2b045458b", + "379196a2761b4a29aca8ef088dc60c10", + "52b546a356e54174a95049b30cb52c81", + "0889e134327e4aa0a8719d03a0d6941b", + "30f22a3e42d24f10ad9851f40a6703f3", + "648b3512bb7d4ccca5d75af36c133e92" ] }, - "outputId": "ed2ca46e-a1a9-4a16-c449-859278d8aa18" + "outputId": "5f129105-eca5-4f33-fb1d-981255f814ad" }, "source": [ "# Download COCO val\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" ], - "execution_count": null, + "execution_count": 3, "outputs": [ { "output_type": "display_data", @@ -548,7 +566,24 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "6d6b90ead2db49b3bdf624b6ba9b44e9" + "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 } }, "metadata": {} @@ -562,48 +597,48 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "19a590ef-363e-424c-d9ce-78bbe0593cd5" + "outputId": "40d5d000-abee-46a0-c07d-1066e1662e01" }, "source": [ "# Validate YOLOv5x on COCO val\n", "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --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 🚀 v6.2-15-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", "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt to yolov5x.pt...\n", - "100% 166M/166M [00:06<00:00, 28.1MB/s]\n", + "100% 166M/166M [00:10<00:00, 16.6MB/s]\n", "\n", "Fusing layers... \n", "YOLOv5x summary: 444 layers, 86705005 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, 47.3MB/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, 10756.32it/s]\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", "\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, 2.33it/s]\n", - " all 5000 36335 0.743 0.625 0.683 0.504\n", - "Speed: 0.1ms pre-process, 4.6ms inference, 1.2ms NMS per image at shape (32, 3, 640, 640)\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", "\n", "Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n", "loading annotations into memory...\n", - "Done (t=0.41s)\n", + "Done (t=0.38s)\n", "creating index...\n", "index created!\n", "Loading and preparing results...\n", - "DONE (t=5.64s)\n", + "DONE (t=5.49s)\n", "creating index...\n", "index created!\n", "Running per image evaluation...\n", "Evaluate annotation type *bbox*\n", - "DONE (t=76.80s).\n", + "DONE (t=72.10s).\n", "Accumulating evaluation results...\n", - "DONE (t=14.61s).\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", @@ -682,13 +717,13 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "47759d5e-34f0-4a6a-c714-ff533391cfff" + "outputId": "f0ce0354-7f50-4546-f3f9-672b4b522d59" }, "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", @@ -696,7 +731,7 @@ "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 🚀 v6.2-15-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", "\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", @@ -705,8 +740,8 @@ "\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, 75.3MB/s]\n", - "Dataset download success ✅ (0.7s), saved to \u001b[1m/content/datasets\u001b[0m\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", "\n", " from n params module arguments \n", " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", @@ -740,33 +775,33 @@ "\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, 7246.20it/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, 7984.87it/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, 986.21it/s]\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[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