From 2ecaa96c847c2b117bf1057d6caec54520fd592a Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Fri, 18 Nov 2022 22:17:52 +0100 Subject: [PATCH] Created using Colaboratory --- tutorial.ipynb | 134 ++++++++++++++++++++++++------------------------- 1 file changed, 67 insertions(+), 67 deletions(-) diff --git a/tutorial.ipynb b/tutorial.ipynb index eb5b675db..9d5aa9c85 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -14,7 +14,7 @@ "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { - "13e0e8b77bf54b25b8893f0b4164315f": { + "300b4d5355ef4967bd5246afeef6eef5": { "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_48037f2f7fea4012b9b341f6aee75297", - "IPY_MODEL_3f3b925287274893baf5ed7bb0cf6635", - "IPY_MODEL_c44bdca7c9784b20ba2146250ee744d6" + "IPY_MODEL_84e6829bb88845a8a4f42700b8496925", + "IPY_MODEL_c038e52d41bf4d5b9602930c3d074087", + "IPY_MODEL_2667604641764341b0bc8c6afea438fd" ], - "layout": "IPY_MODEL_5b0ed23cd32c4c7d8d9467b7425684ad" + "layout": "IPY_MODEL_98b3a4806ed14102b0d75e6c571d6134" } }, - "48037f2f7fea4012b9b341f6aee75297": { + "84e6829bb88845a8a4f42700b8496925": { "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_1e10b4db5d644cb78bd6e005bb34038a", + "layout": "IPY_MODEL_c66a77395e42424d904699edcbb67291", "placeholder": "​", - "style": "IPY_MODEL_a58728093ecb4eafb826bee11a84c549", + "style": "IPY_MODEL_c4bbc15bf853439399dbcf1d40a5a407", "value": "100%" } }, - "3f3b925287274893baf5ed7bb0cf6635": { + "c038e52d41bf4d5b9602930c3d074087": { "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_9ce169fe4b8543c0b26d745daa230f18", + "layout": "IPY_MODEL_0aaabfac395b43afbdd6d752c502bbf6", "max": 818322941, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_d5da01aca8fb400c96e76f44c9403581", + "style": "IPY_MODEL_3786d970492b4aa38f886f2572fd958c", "value": 818322941 } }, - "c44bdca7c9784b20ba2146250ee744d6": { + "2667604641764341b0bc8c6afea438fd": { "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_98cbaa572fdd4c42975f52015672b3a5", + "layout": "IPY_MODEL_b86d0f2d7be74cebbcaa884b53123eeb", "placeholder": "​", - "style": "IPY_MODEL_a636aa81f5cc453099c9e552f0986e63", - "value": " 780M/780M [01:27<00:00, 6.98MB/s]" + "style": "IPY_MODEL_fa7b1497925a457f89286a71f073f416", + "value": " 780M/780M [00:57<00:00, 10.1MB/s]" } }, - "5b0ed23cd32c4c7d8d9467b7425684ad": { + "98b3a4806ed14102b0d75e6c571d6134": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -154,7 +154,7 @@ "width": null } }, - "1e10b4db5d644cb78bd6e005bb34038a": { + "c66a77395e42424d904699edcbb67291": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -206,7 +206,7 @@ "width": null } }, - "a58728093ecb4eafb826bee11a84c549": { + "c4bbc15bf853439399dbcf1d40a5a407": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -221,7 +221,7 @@ "description_width": "" } }, - "9ce169fe4b8543c0b26d745daa230f18": { + "0aaabfac395b43afbdd6d752c502bbf6": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -273,7 +273,7 @@ "width": null } }, - "d5da01aca8fb400c96e76f44c9403581": { + "3786d970492b4aa38f886f2572fd958c": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -289,7 +289,7 @@ "description_width": "" } }, - "98cbaa572fdd4c42975f52015672b3a5": { + "b86d0f2d7be74cebbcaa884b53123eeb": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -341,7 +341,7 @@ "width": null } }, - "a636aa81f5cc453099c9e552f0986e63": { + "fa7b1497925a457f89286a71f073f416": { "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": "bcb6db4a-fc21-4258-9b53-4a760a534656" + "outputId": "32e3bc15-6d02-4352-f0a3-912059d134a5" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", @@ -418,7 +418,7 @@ "output_type": "stream", "name": "stderr", "text": [ - "YOLOv5 🚀 v6.2-250-g467a57f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + "YOLOv5 🚀 v6.2-256-g0051615 Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" ] }, { @@ -459,7 +459,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "de684b46-7623-4836-ee44-49cdb320cbf3" + "outputId": "8e81d6e9-0360-4212-cd61-9a5a58d3f703" }, "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 🚀 v6.2-250-g467a57f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "YOLOv5 🚀 v6.2-256-g0051615 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, 162MB/s]\n", + "100% 14.1M/14.1M [00:00<00:00, 19.5MB/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, 17.2ms\n", - "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 13.3ms\n", - "Speed: 0.5ms pre-process, 15.2ms inference, 19.5ms NMS per image at shape (1, 3, 640, 640)\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 17.5ms\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", "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n" ] } @@ -515,20 +515,20 @@ "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ - "13e0e8b77bf54b25b8893f0b4164315f", - "48037f2f7fea4012b9b341f6aee75297", - "3f3b925287274893baf5ed7bb0cf6635", - "c44bdca7c9784b20ba2146250ee744d6", - "5b0ed23cd32c4c7d8d9467b7425684ad", - "1e10b4db5d644cb78bd6e005bb34038a", - "a58728093ecb4eafb826bee11a84c549", - "9ce169fe4b8543c0b26d745daa230f18", - "d5da01aca8fb400c96e76f44c9403581", - "98cbaa572fdd4c42975f52015672b3a5", - "a636aa81f5cc453099c9e552f0986e63" + "300b4d5355ef4967bd5246afeef6eef5", + "84e6829bb88845a8a4f42700b8496925", + "c038e52d41bf4d5b9602930c3d074087", + "2667604641764341b0bc8c6afea438fd", + "98b3a4806ed14102b0d75e6c571d6134", + "c66a77395e42424d904699edcbb67291", + "c4bbc15bf853439399dbcf1d40a5a407", + "0aaabfac395b43afbdd6d752c502bbf6", + "3786d970492b4aa38f886f2572fd958c", + "b86d0f2d7be74cebbcaa884b53123eeb", + "fa7b1497925a457f89286a71f073f416" ] }, - "outputId": "b1e02a1f-981f-4739-e75d-10d0204cc32d" + "outputId": "61ffec5e-90ea-44f6-c0ea-b006e6e7072f" }, "source": [ "# Download COCO val\n", @@ -546,7 +546,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "13e0e8b77bf54b25b8893f0b4164315f" + "model_id": "300b4d5355ef4967bd5246afeef6eef5" } }, "metadata": {} @@ -560,7 +560,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "9c2f755f-f383-4a9e-cd19-f73a0c763a9c" + "outputId": "aa5d5cea-14c1-4a19-bfdf-95b7164962cf" }, "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 🚀 v6.2-250-g467a57f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "YOLOv5 🚀 v6.2-256-g0051615 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' images and labels...4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2019.92it/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, 2066.57it/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, 2.25it/s]\n", + " Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:09<00:00, 2.26it/s]\n", " all 5000 36335 0.67 0.521 0.566 0.371\n", - "Speed: 0.2ms pre-process, 2.7ms inference, 2.1ms NMS per image at shape (32, 3, 640, 640)\n", + "Speed: 0.1ms pre-process, 2.7ms inference, 1.9ms 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=0.41s)\n", + "Done (t=0.82s)\n", "creating index...\n", "index created!\n", "Loading and preparing results...\n", - "DONE (t=6.19s)\n", + "DONE (t=5.49s)\n", "creating index...\n", "index created!\n", "Running per image evaluation...\n", "Evaluate annotation type *bbox*\n", - "DONE (t=75.81s).\n", + "DONE (t=74.26s).\n", "Accumulating evaluation results...\n", - "DONE (t=15.26s).\n", + "DONE (t=13.46s).\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": "7d03d4d2-9a6e-47de-88f4-c673b55c73c5" + "outputId": "f0fcdc77-5326-41e1-bacc-be5432eefa2a" }, "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 🚀 v6.2-250-g467a57f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "YOLOv5 🚀 v6.2-256-g0051615 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, 26.1MB/s]\n", - "Dataset download success ✅ (0.9s), saved to \u001b[1m/content/datasets\u001b[0m\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", "\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' images and labels...126 found, 2 missing, 0 empty, 0 corrupt: 100% 128/128 [00:00<00:00, 1989.66it/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, 2084.63it/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, 246.25it/s]\n", - "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/coco128/labels/train2017.cache' images and labels... 126 found, 2 missing, 0 empty, 0 corrupt: 100% 128/128 [00:00