From c9d47ae05632e2a42e560fbfeb22d3780224546c Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Tue, 22 Nov 2022 20:37:45 +0100 Subject: [PATCH] Created using Colaboratory --- tutorial.ipynb | 142 ++++++++++++++++++++++++------------------------- 1 file changed, 71 insertions(+), 71 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index 7d7f1649cc8..657dc266da9 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -14,7 +14,7 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "300b4d5355ef4967bd5246afeef6eef5": { + "1f7df330663048998adcf8a45bc8f69b": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -29,14 +29,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_84e6829bb88845a8a4f42700b8496925", - "IPY_MODEL_c038e52d41bf4d5b9602930c3d074087", - "IPY_MODEL_2667604641764341b0bc8c6afea438fd" + "IPY_MODEL_e896e6096dd244c59d7955e2035cd729", + "IPY_MODEL_a6ff238c29984b24bf6d0bd175c19430", + "IPY_MODEL_3c085ba3f3fd4c3c8a6bb41b41ce1479" ], - "layout": "IPY_MODEL_98b3a4806ed14102b0d75e6c571d6134" + "layout": "IPY_MODEL_16b0c8aa6e0f427e8a54d3791abb7504" } }, - "84e6829bb88845a8a4f42700b8496925": { + "e896e6096dd244c59d7955e2035cd729": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -51,13 +51,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c66a77395e42424d904699edcbb67291", + "layout": "IPY_MODEL_c7b2dd0f78384cad8e400b282996cdf5", "placeholder": "​", - "style": "IPY_MODEL_c4bbc15bf853439399dbcf1d40a5a407", + "style": "IPY_MODEL_6a27e43b0e434edd82ee63f0a91036ca", "value": "100%" } }, - "c038e52d41bf4d5b9602930c3d074087": { + "a6ff238c29984b24bf6d0bd175c19430": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -73,15 +73,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_0aaabfac395b43afbdd6d752c502bbf6", + "layout": "IPY_MODEL_cce0e6c0c4ec442cb47e65c674e02e92", "max": 818322941, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_3786d970492b4aa38f886f2572fd958c", + "style": "IPY_MODEL_c5b9f38e2f0d4f9aa97fe87265263743", "value": 818322941 } }, - "2667604641764341b0bc8c6afea438fd": { + "3c085ba3f3fd4c3c8a6bb41b41ce1479": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -96,13 +96,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_b86d0f2d7be74cebbcaa884b53123eeb", + "layout": "IPY_MODEL_df554fb955c7454696beac5a82889386", "placeholder": "​", - "style": "IPY_MODEL_fa7b1497925a457f89286a71f073f416", - "value": " 780M/780M [00:57<00:00, 10.1MB/s]" + "style": "IPY_MODEL_74e9112a87a242f4831b7d68c7da6333", + "value": " 780M/780M [00:05<00:00, 126MB/s]" } }, - "98b3a4806ed14102b0d75e6c571d6134": { + "16b0c8aa6e0f427e8a54d3791abb7504": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -154,7 +154,7 @@ "width": null } }, - "c66a77395e42424d904699edcbb67291": { + "c7b2dd0f78384cad8e400b282996cdf5": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -206,7 +206,7 @@ "width": null } }, - "c4bbc15bf853439399dbcf1d40a5a407": { + "6a27e43b0e434edd82ee63f0a91036ca": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -221,7 +221,7 @@ "description_width": "" } }, - "0aaabfac395b43afbdd6d752c502bbf6": { + "cce0e6c0c4ec442cb47e65c674e02e92": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -273,7 +273,7 @@ "width": null } }, - "3786d970492b4aa38f886f2572fd958c": { + "c5b9f38e2f0d4f9aa97fe87265263743": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -289,7 +289,7 @@ "description_width": "" } }, - "b86d0f2d7be74cebbcaa884b53123eeb": { + "df554fb955c7454696beac5a82889386": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -341,7 +341,7 @@ "width": null } }, - "fa7b1497925a457f89286a71f073f416": { + "74e9112a87a242f4831b7d68c7da6333": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -401,7 +401,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "32e3bc15-6d02-4352-f0a3-912059d134a5" + "outputId": "f9f016ad-3dcf-4bd2-e1c3-d5b79efc6f32" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", @@ -418,7 +418,7 @@ "output_type": "stream", "name": "stderr", "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" ] }, { @@ -446,9 +446,9 @@ " vid.mp4 # video\n", " screen # screenshot\n", " path/ # directory\n", - " 'path/*.jpg' # glob\n", - " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", - " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", "```" ] }, @@ -459,7 +459,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "8e81d6e9-0360-4212-cd61-9a5a58d3f703" + "outputId": "b4db5c49-f501-4505-cf0d-a1d35236c485" }, "source": [ "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n", @@ -472,16 +472,16 @@ "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, 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", - "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, 19.5MB/s]\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, 116MB/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, 17.5ms\n", - "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 18.0ms\n", - "Speed: 0.5ms pre-process, 17.8ms inference, 17.6ms NMS per image at shape (1, 3, 640, 640)\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, 14.3ms\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" ] } @@ -515,20 +515,20 @@ "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ - "300b4d5355ef4967bd5246afeef6eef5", - "84e6829bb88845a8a4f42700b8496925", - "c038e52d41bf4d5b9602930c3d074087", - "2667604641764341b0bc8c6afea438fd", - "98b3a4806ed14102b0d75e6c571d6134", - "c66a77395e42424d904699edcbb67291", - "c4bbc15bf853439399dbcf1d40a5a407", - "0aaabfac395b43afbdd6d752c502bbf6", - "3786d970492b4aa38f886f2572fd958c", - "b86d0f2d7be74cebbcaa884b53123eeb", - "fa7b1497925a457f89286a71f073f416" + "1f7df330663048998adcf8a45bc8f69b", + "e896e6096dd244c59d7955e2035cd729", + "a6ff238c29984b24bf6d0bd175c19430", + "3c085ba3f3fd4c3c8a6bb41b41ce1479", + "16b0c8aa6e0f427e8a54d3791abb7504", + "c7b2dd0f78384cad8e400b282996cdf5", + "6a27e43b0e434edd82ee63f0a91036ca", + "cce0e6c0c4ec442cb47e65c674e02e92", + "c5b9f38e2f0d4f9aa97fe87265263743", + "df554fb955c7454696beac5a82889386", + "74e9112a87a242f4831b7d68c7da6333" ] }, - "outputId": "61ffec5e-90ea-44f6-c0ea-b006e6e7072f" + "outputId": "c7d0a0d2-abfb-44c3-d60d-f99d0e7aabad" }, "source": [ "# Download COCO val\n", @@ -546,7 +546,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "300b4d5355ef4967bd5246afeef6eef5" + "model_id": "1f7df330663048998adcf8a45bc8f69b" } }, "metadata": {} @@ -560,7 +560,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "aa5d5cea-14c1-4a19-bfdf-95b7164962cf" + "outputId": "5fc61358-7bc5-4310-a310-9059f66c6322" }, "source": [ "# Validate YOLOv5s on COCO val\n", @@ -573,30 +573,30 @@ "name": "stdout", "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", - "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", "Fusing layers... \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", - " 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", - "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", "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n", "loading annotations into memory...\n", - "Done (t=0.82s)\n", + "Done (t=0.43s)\n", "creating index...\n", "index created!\n", "Loading and preparing results...\n", - "DONE (t=5.49s)\n", + "DONE (t=5.85s)\n", "creating index...\n", "index created!\n", "Running per image evaluation...\n", "Evaluate annotation type *bbox*\n", - "DONE (t=74.26s).\n", + "DONE (t=82.22s).\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 | area= all | maxDets=100 ] = 0.572\n", " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n", @@ -676,7 +676,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "f0fcdc77-5326-41e1-bacc-be5432eefa2a" + "outputId": "721b9028-767f-4a05-c964-692c245f7398" }, "source": [ "# Train YOLOv5s on COCO128 for 3 epochs\n", @@ -690,7 +690,7 @@ "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 🚀 v6.2-256-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", "\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", @@ -699,8 +699,8 @@ "\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, 39.8MB/s]\n", - "Dataset download success ✅ (0.8s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "100% 6.66M/6.66M [00:00<00:00, 261MB/s]\n", + "Dataset download success ✅ (0.3s), 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", @@ -734,11 +734,11 @@ "\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... 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[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