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  535.       "source": [

  536.         "<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"

  537.       ]

  538.     },

  539.     {

  540.       "cell_type": "markdown",

  541.       "metadata": {

  542.         "id": "t6MPjfT5NrKQ"

  543.       },

  544.       "source": [

  545.         "<a align=\"left\" href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",

  546.         "<img src=\"https://user-images.githubusercontent.com/26833433/125273437-35b3fc00-e30d-11eb-9079-46f313325424.png\"></a>\n",

  547.         "\n",

  548.         "This is the **official YOLOv5 🚀 notebook** authored by **Ultralytics**, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n",

  549.         "For more information please visit https://github.com/ultralytics/yolov5 and https://ultralytics.com. Thank you!"

  550.       ]

  551.     },

  552.     {

  553.       "cell_type": "markdown",

  554.       "metadata": {

  555.         "id": "7mGmQbAO5pQb"

  556.       },

  557.       "source": [

  558.         "# Setup\n",

  559.         "\n",

  560.         "Clone repo, install dependencies and check PyTorch and GPU."

  561.       ]

  562.     },

  563.     {

  564.       "cell_type": "code",

  565.       "metadata": {

  566.         "id": "wbvMlHd_QwMG",

  567.         "colab": {

  568.           "base_uri": "https://localhost:8080/"

  569.         },

  570.         "outputId": "ada1dd8d-e0aa-4858-e893-dc320319ca30"

  571.       },

  572.       "source": [

  573.         "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",

  574.         "%cd yolov5\n",

  575.         "%pip install -qr requirements.txt  # install dependencies\n",

  576.         "\n",

  577.         "import torch\n",

  578.         "from IPython.display import Image, clear_output  # to display images\n",

  579.         "\n",

  580.         "clear_output()\n",

  581.         "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")"

  582.       ],

  583.       "execution_count": null,

  584.       "outputs": [

  585.         {

  586.           "output_type": "stream",

  587.           "text": [

  588.             "Setup complete. Using torch 1.9.0+cu102 (Tesla V100-SXM2-16GB)\n"

  589.           ],

  590.           "name": "stdout"

  591.         }

  592.       ]

  593.     },

  594.     {

  595.       "cell_type": "markdown",

  596.       "metadata": {

  597.         "id": "4JnkELT0cIJg"

  598.       },

  599.       "source": [

  600.         "# 1. Inference\n",

  601.         "\n",

  602.         "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n",

  603.         "\n",

  604.         "```shell\n",

  605.         "python detect.py --source 0  # webcam\n",

  606.         "                          file.jpg  # image \n",

  607.         "                          file.mp4  # video\n",

  608.         "                          path/  # directory\n",

  609.         "                          path/*.jpg  # glob\n",

  610.         "                          'https://youtu.be/NUsoVlDFqZg'  # YouTube\n",

  611.         "                          'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream\n",

  612.         "```"

  613.       ]

  614.     },

  615.     {

  616.       "cell_type": "code",

  617.       "metadata": {

  618.         "id": "zR9ZbuQCH7FX",

  619.         "colab": {

  620.           "base_uri": "https://localhost:8080/"

  621.         },

  622.         "outputId": "a7a37616-a82b-4bdb-a463-6ead850b5615"

  623.       },

  624.       "source": [

  625.         "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n",

  626.         "Image(filename='runs/detect/exp/zidane.jpg', width=600)"

  627.       ],

  628.       "execution_count": null,

  629.       "outputs": [

  630.         {

  631.           "output_type": "stream",

  632.           "text": [

  633.             "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images/, imgsz=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\n",

  634.             "YOLOv5 🚀 v5.0-330-g18f6ba7 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",

  635.             "\n",

  636.             "Fusing layers... \n",

  637.             "Model Summary: 224 layers, 7266973 parameters, 0 gradients\n",

  638.             "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 1 fire hydrant, Done. (0.008s)\n",

  639.             "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.008s)\n",

  640.             "Results saved to runs/detect/exp\n",

  641.             "Done. (0.091s)\n"

  642.           ],

  643.           "name": "stdout"

  644.         }

  645.       ]

  646.     },

  647.     {

  648.       "cell_type": "markdown",

  649.       "metadata": {

  650.         "id": "hkAzDWJ7cWTr"

  651.       },

  652.       "source": [

  653.         "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;\n",

  654.         "<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\" width=\"600\">"

  655.       ]

  656.     },

  657.     {

  658.       "cell_type": "markdown",

  659.       "metadata": {

  660.         "id": "0eq1SMWl6Sfn"

  661.       },

  662.       "source": [

  663.         "# 2. Validate\n",

  664.         "Validate a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be ~1% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation."

  665.       ]

  666.     },

  667.     {

  668.       "cell_type": "markdown",

  669.       "metadata": {

  670.         "id": "eyTZYGgRjnMc"

  671.       },

  672.       "source": [

  673.         "## COCO val2017\n",

  674.         "Download [COCO val 2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L14) dataset (1GB - 5000 images), and test model accuracy."

  675.       ]

  676.     },

  677.     {

  678.       "cell_type": "code",

  679.       "metadata": {

  680.         "id": "WQPtK1QYVaD_",

  681.         "colab": {

  682.           "base_uri": "https://localhost:8080/",

  683.           "height": 66,

  684.           "referenced_widgets": [

  685.             "2e915d9016c846e095e382b6a02ee773",

  686.             "cb7fc3a5c6cc4fde8d2c83e594a7c86e",

  687.             "ac3edef4e3434f4587e6cbf8aa048770",

  688.             "853ac234cc2a4236946fc516871e10eb",

  689.             "13842ca90c0047e584b8d68d99dad2b1",

  690.             "f454999c3a924c7bad0746fb453dec36",

  691.             "f94a7ca8c1f04761bf38fdc5f99664b8",

  692.             "9da1a23b042c41618dd14b0e30aa7cbe"

  693.           ]

  694.         },

  695.         "outputId": "3606f305-aa67-43fd-d5d6-93d1f311768c"

  696.       },

  697.       "source": [

  698.         "# Download COCO val2017\n",

  699.         "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n",

  700.         "!unzip -q tmp.zip -d ../datasets && rm tmp.zip"

  701.       ],

  702.       "execution_count": null,

  703.       "outputs": [

  704.         {

  705.           "output_type": "display_data",

  706.           "data": {

  707.             "application/vnd.jupyter.widget-view+json": {

  708.               "model_id": "2e915d9016c846e095e382b6a02ee773",

  709.               "version_minor": 0,

  710.               "version_major": 2

  711.             },

  712.             "text/plain": [

  713.               "HBox(children=(FloatProgress(value=0.0, max=818322941.0), HTML(value='')))"

  714.             ]

  715.           },

  716.           "metadata": {

  717.             "tags": []

  718.           }

  719.         },

  720.         {

  721.           "output_type": "stream",

  722.           "text": [

  723.             "\n"

  724.           ],

  725.           "name": "stdout"

  726.         }

  727.       ]

  728.     },

  729.     {

  730.       "cell_type": "code",

  731.       "metadata": {

  732.         "id": "X58w8JLpMnjH",

  733.         "colab": {

  734.           "base_uri": "https://localhost:8080/"

  735.         },

  736.         "outputId": "20fbc423-f536-43ff-e70b-3acf6aeade99"

  737.       },

  738.       "source": [

  739.         "# Run YOLOv5x on COCO val2017\n",

  740.         "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half"

  741.       ],

  742.       "execution_count": null,

  743.       "outputs": [

  744.         {

  745.           "output_type": "stream",

  746.           "text": [

  747.             "\u001b[34m\u001b[1mval: \u001b[0mdata=./data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, 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\n",

  748.             "YOLOv5 🚀 v5.0-330-g18f6ba7 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",

  749.             "\n",

  750.             "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5x.pt to yolov5x.pt...\n",

  751.             "100% 168M/168M [00:05<00:00, 31.9MB/s]\n",

  752.             "\n",

  753.             "Fusing layers... \n",

  754.             "Model Summary: 476 layers, 87730285 parameters, 0 gradients\n",

  755.             "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco/val2017' images and labels...4952 found, 48 missing, 0 empty, 0 corrupted: 100% 5000/5000 [00:01<00:00, 2653.03it/s]\n",

  756.             "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../datasets/coco/val2017.cache\n",

  757.             "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 157/157 [01:18<00:00,  2.00it/s]\n",

  758.             "                 all       5000      36335      0.746      0.626       0.68       0.49\n",

  759.             "Speed: 0.1ms pre-process, 5.1ms inference, 1.5ms NMS per image at shape (32, 3, 640, 640)\n",

  760.             "\n",

  761.             "Evaluating pycocotools mAP... saving runs/val/exp/yolov5x_predictions.json...\n",

  762.             "loading annotations into memory...\n",

  763.             "Done (t=0.44s)\n",

  764.             "creating index...\n",

  765.             "index created!\n",

  766.             "Loading and preparing results...\n",

  767.             "DONE (t=4.82s)\n",

  768.             "creating index...\n",

  769.             "index created!\n",

  770.             "Running per image evaluation...\n",

  771.             "Evaluate annotation type *bbox*\n",

  772.             "DONE (t=84.52s).\n",

  773.             "Accumulating evaluation results...\n",

  774.             "DONE (t=13.82s).\n",

  775.             " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.504\n",

  776.             " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.688\n",

  777.             " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.546\n",

  778.             " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.351\n",

  779.             " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.551\n",

  780.             " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.644\n",

  781.             " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.382\n",

  782.             " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.629\n",

  783.             " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.681\n",

  784.             " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.524\n",

  785.             " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.735\n",

  786.             " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.827\n",

  787.             "Results saved to runs/val/exp\n"

  788.           ],

  789.           "name": "stdout"

  790.         }

  791.       ]

  792.     },

  793.     {

  794.       "cell_type": "markdown",

  795.       "metadata": {

  796.         "id": "rc_KbFk0juX2"

  797.       },

  798.       "source": [

  799.         "## COCO test-dev2017\n",

  800.         "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (**20,000 images, no labels**). Results are saved to a `*.json` file which should be **zipped** and submitted to the evaluation server at https://competitions.codalab.org/competitions/20794."

  801.       ]

  802.     },

  803.     {

  804.       "cell_type": "code",

  805.       "metadata": {

  806.         "id": "V0AJnSeCIHyJ"

  807.       },

  808.       "source": [

  809.         "# Download COCO test-dev2017\n",

  810.         "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip', 'tmp.zip')\n",

  811.         "!unzip -q tmp.zip -d ../ && rm tmp.zip # unzip labels\n",

  812.         "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f  # 7GB,  41k images\n",

  813.         "%mv ./test2017 ../coco/images  # move to /coco"

  814.       ],

  815.       "execution_count": null,

  816.       "outputs": []

  817.     },

  818.     {

  819.       "cell_type": "code",

  820.       "metadata": {

  821.         "id": "29GJXAP_lPrt"

  822.       },

  823.       "source": [

  824.         "# Run YOLOv5s on COCO test-dev2017 using --task test\n",

  825.         "!python val.py --weights yolov5s.pt --data coco.yaml --task test"

  826.       ],

  827.       "execution_count": null,

  828.       "outputs": []

  829.     },

  830.     {

  831.       "cell_type": "markdown",

  832.       "metadata": {

  833.         "id": "VUOiNLtMP5aG"

  834.       },

  835.       "source": [

  836.         "# 3. Train\n",

  837.         "\n",

  838.         "Download [COCO128](https://www.kaggle.com/ultralytics/coco128), a small 128-image tutorial dataset, start tensorboard and train YOLOv5s from a pretrained checkpoint for 3 epochs (note actual training is typically much longer, around **300-1000 epochs**, depending on your dataset)."

  839.       ]

  840.     },

  841.     {

  842.       "cell_type": "code",

  843.       "metadata": {

  844.         "id": "Knxi2ncxWffW",

  845.         "colab": {

  846.           "base_uri": "https://localhost:8080/",

  847.           "height": 66,

  848.           "referenced_widgets": [

  849.             "6ff8a710ded44391a624dec5c460b771",

  850.             "3c19729b51cd45d4848035da06e96ff8",

  851.             "23b2f0ae3d46438c8de375987c77f580",

  852.             "dd9498c321a9422da6faf17a0be026d4",

  853.             "d8dda4b2ce864fd682e558b9a48f602e",

  854.             "ff8151449e444a14869684212b9ab14e",

  855.             "0f84fe609bcf4aa9afdc32a8cf076909",

  856.             "8fda673769984e2b928ef820d34c85c3"

  857.           ]

  858.         },

  859.         "outputId": "4510c6b0-8d2a-436c-d3f4-c8f8470d913a"

  860.       },

  861.       "source": [

  862.         "# Download COCO128\n",

  863.         "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n",

  864.         "!unzip -q tmp.zip -d ../ && rm tmp.zip"

  865.       ],

  866.       "execution_count": null,

  867.       "outputs": [

  868.         {

  869.           "output_type": "display_data",

  870.           "data": {

  871.             "application/vnd.jupyter.widget-view+json": {

  872.               "model_id": "6ff8a710ded44391a624dec5c460b771",

  873.               "version_minor": 0,

  874.               "version_major": 2

  875.             },

  876.             "text/plain": [

  877.               "HBox(children=(FloatProgress(value=0.0, max=6984509.0), HTML(value='')))"

  878.             ]

  879.           },

  880.           "metadata": {

  881.             "tags": []

  882.           }

  883.         },

  884.         {

  885.           "output_type": "stream",

  886.           "text": [

  887.             "\n"

  888.           ],

  889.           "name": "stdout"

  890.         }

  891.       ]

  892.     },

  893.     {

  894.       "cell_type": "markdown",

  895.       "metadata": {

  896.         "id": "_pOkGLv1dMqh"

  897.       },

  898.       "source": [

  899.         "Train a YOLOv5s model on [COCO128](https://www.kaggle.com/ultralytics/coco128) with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and **COCO, COCO128, and VOC datasets are downloaded automatically** on first use.\n",

  900.         "\n",

  901.         "All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n"

  902.       ]

  903.     },

  904.     {

  905.       "cell_type": "code",

  906.       "metadata": {

  907.         "id": "bOy5KI2ncnWd"

  908.       },

  909.       "source": [

  910.         "# Tensorboard  (optional)\n",

  911.         "%load_ext tensorboard\n",

  912.         "%tensorboard --logdir runs/train"

  913.       ],

  914.       "execution_count": null,

  915.       "outputs": []

  916.     },

  917.     {

  918.       "cell_type": "code",

  919.       "metadata": {

  920.         "id": "2fLAV42oNb7M"

  921.       },

  922.       "source": [

  923.         "# Weights & Biases  (optional)\n",

  924.         "%pip install -q wandb\n",

  925.         "import wandb\n",

  926.         "wandb.login()"

  927.       ],

  928.       "execution_count": null,

  929.       "outputs": []

  930.     },

  931.     {

  932.       "cell_type": "code",

  933.       "metadata": {

  934.         "id": "1NcFxRcFdJ_O",

  935.         "colab": {

  936.           "base_uri": "https://localhost:8080/"

  937.         },

  938.         "outputId": "cd8ac17d-19a8-4e87-ab6a-31af1edac1ef"

  939.       },

  940.       "source": [

  941.         "# Train YOLOv5s on COCO128 for 3 epochs\n",

  942.         "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"

  943.       ],

  944.       "execution_count": null,

  945.       "outputs": [

  946.         {

  947.           "output_type": "stream",

  948.           "text": [

  949.             "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache_images=True, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, entity=None, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias=latest, local_rank=-1\n",

  950.             "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",

  951.             "YOLOv5 🚀 v5.0-330-g18f6ba7 torch 1.9.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160.5MB)\n",

  952.             "\n",

  953.             "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, 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",

  954.             "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)\n",

  955.             "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",

  956.             "2021-07-29 22:56:52.096481: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n",

  957.             "\n",

  958.             "WARNING: Dataset not found, nonexistent paths: ['/content/datasets/coco128/images/train2017']\n",

  959.             "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip ...\n",

  960.             "100% 6.66M/6.66M [00:00<00:00, 44.0MB/s]\n",

  961.             "Dataset autodownload success\n",

  962.             "\n",

  963.             "\n",

  964.             "                 from  n    params  module                                  arguments                     \n",

  965.             "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \n",

  966.             "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",

  967.             "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",

  968.             "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",

  969.             "  4                -1  1    156928  models.common.C3                        [128, 128, 3]                 \n",

  970.             "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",

  971.             "  6                -1  1    625152  models.common.C3                        [256, 256, 3]                 \n",

  972.             "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",

  973.             "  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        \n",

  974.             "  9                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",

  975.             " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",

  976.             " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",

  977.             " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",

  978.             " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",

  979.             " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",

  980.             " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",

  981.             " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",

  982.             " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",

  983.             " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",

  984.             " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",

  985.             " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",

  986.             " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",

  987.             " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",

  988.             " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",

  989.             " 24      [17, 20, 23]  1    229245  models.yolo.Detect                      [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",

  990.             "Model Summary: 283 layers, 7276605 parameters, 7276605 gradients, 17.1 GFLOPs\n",

  991.             "\n",

  992.             "Transferred 362/362 items from yolov5s.pt\n",

  993.             "Scaled weight_decay = 0.0005\n",

  994.             "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 59 weight, 62 weight (no decay), 62 bias\n",

  995.             "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n",

  996.             "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../datasets/coco128/labels/train2017' images and labels...128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 2021.98it/s]\n",

  997.             "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../datasets/coco128/labels/train2017.cache\n",

  998.             "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:00<00:00, 273.58it/s]\n",

  999.             "\u001b[34m\u001b[1mval: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00<00:00, 506004.63it/s]\n",

  1000.             "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 128/128 [00:01<00:00, 121.71it/s]\n",

  1001.             "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",

  1002.             "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",

  1003.             "Plotting labels... \n",

  1004.             "\n",

  1005.             "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 4.27, Best Possible Recall (BPR) = 0.9935\n",

  1006.             "Image sizes 640 train, 640 val\n",

  1007.             "Using 2 dataloader workers\n",

  1008.             "Logging results to runs/train/exp\n",

  1009.             "Starting training for 3 epochs...\n",

  1010.             "\n",

  1011.             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",

  1012.             "       0/2     3.64G    0.0441   0.06646   0.02229       290       640: 100% 8/8 [00:04<00:00,  1.93it/s]\n",

  1013.             "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00,  3.45it/s]\n",

  1014.             "                 all        128        929      0.696      0.562      0.644      0.419\n",

  1015.             "\n",

  1016.             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",

  1017.             "       1/2     5.04G   0.04573   0.06289     0.021       226       640: 100% 8/8 [00:01<00:00,  5.46it/s]\n",

  1018.             "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:01<00:00,  3.16it/s]\n",

  1019.             "                 all        128        929       0.71      0.567      0.654      0.424\n",

  1020.             "\n",

  1021.             "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",

  1022.             "       2/2     5.04G   0.04542    0.0715   0.02028       242       640: 100% 8/8 [00:01<00:00,  5.12it/s]\n",

  1023.             "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 4/4 [00:02<00:00,  1.46it/s]\n",

  1024.             "                 all        128        929      0.731      0.563      0.658      0.427\n",

  1025.             "3 epochs completed in 0.006 hours.\n",

  1026.             "\n",

  1027.             "Optimizer stripped from runs/train/exp/weights/last.pt, 14.8MB\n",

  1028.             "Optimizer stripped from runs/train/exp/weights/best.pt, 14.8MB\n"

  1029.           ],

  1030.           "name": "stdout"

  1031.         }

  1032.       ]

  1033.     },

  1034.     {

  1035.       "cell_type": "markdown",

  1036.       "metadata": {

  1037.         "id": "15glLzbQx5u0"

  1038.       },

  1039.       "source": [

  1040.         "# 4. Visualize"

  1041.       ]

  1042.     },

  1043.     {

  1044.       "cell_type": "markdown",

  1045.       "metadata": {

  1046.         "id": "DLI1JmHU7B0l"

  1047.       },

  1048.       "source": [

  1049.         "## Weights & Biases Logging 🌟 NEW\n",

  1050.         "\n",

  1051.         "[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is now integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n",

  1052.         "\n",

  1053.         "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n",

  1054.         "\n",

  1055.         "<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/125274843-a27bc600-e30e-11eb-9a44-62af0b7a50a2.png\" width=\"800\">"

  1056.       ]

  1057.     },

  1058.     {

  1059.       "cell_type": "markdown",

  1060.       "metadata": {

  1061.         "id": "-WPvRbS5Swl6"

  1062.       },

  1063.       "source": [

  1064.         "## Local Logging\n",

  1065.         "\n",

  1066.         "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and val jpgs to see mosaics, labels, predictions and augmentation effects. Note an Ultralytics **Mosaic Dataloader** is used for training (shown below), which combines 4 images into 1 mosaic during training.\n",

  1067.         "\n",

  1068.         "> <img src=\"https://user-images.githubusercontent.com/26833433/124931219-48bf8700-e002-11eb-84f0-e05d95b118dd.jpg\" width=\"700\">  \n",

  1069.         "`train_batch0.jpg` shows train batch 0 mosaics and labels\n",

  1070.         "\n",

  1071.         "> <img src=\"https://user-images.githubusercontent.com/26833433/124931217-4826f080-e002-11eb-87b9-ae0925a8c94b.jpg\" width=\"700\">  \n",

  1072.         "`test_batch0_labels.jpg` shows val batch 0 labels\n",

  1073.         "\n",

  1074.         "> <img src=\"https://user-images.githubusercontent.com/26833433/124931209-46f5c380-e002-11eb-9bd5-7a3de2be9851.jpg\" width=\"700\">  \n",

  1075.         "`test_batch0_pred.jpg` shows val batch 0 _predictions_\n",

  1076.         "\n",

  1077.         "Training results are automatically logged to [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) as `results.csv`, which is plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:\n",

  1078.         "\n",

  1079.         "```python\n",

  1080.         "from utils.plots import plot_results \n",

  1081.         "plot_results('path/to/results.csv')  # plot 'results.csv' as 'results.png'\n",

  1082.         "```\n",

  1083.         "\n",

  1084.         "<img align=\"left\" width=\"800\" alt=\"COCO128 Training Results\" src=\"https://user-images.githubusercontent.com/26833433/126906780-8c5e2990-6116-4de6-b78a-367244a33ccf.png\">"

  1085.       ]

  1086.     },

  1087.     {

  1088.       "cell_type": "markdown",

  1089.       "metadata": {

  1090.         "id": "Zelyeqbyt3GD"

  1091.       },

  1092.       "source": [

  1093.         "# Environments\n",

  1094.         "\n",

  1095.         "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",

  1096.         "\n",

  1097.         "- **Google Colab and Kaggle** notebooks with free GPU: <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",

  1098.         "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n",

  1099.         "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n",

  1100.         "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\n"

  1101.       ]

  1102.     },

  1103.     {

  1104.       "cell_type": "markdown",

  1105.       "metadata": {

  1106.         "id": "6Qu7Iesl0p54"

  1107.       },

  1108.       "source": [

  1109.         "# Status\n",

  1110.         "\n",

  1111.         "![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)\n",

  1112.         "\n",

  1113.         "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n"

  1114.       ]

  1115.     },

  1116.     {

  1117.       "cell_type": "markdown",

  1118.       "metadata": {

  1119.         "id": "IEijrePND_2I"

  1120.       },

  1121.       "source": [

  1122.         "# Appendix\n",

  1123.         "\n",

  1124.         "Optional extras below. Unit tests validate repo functionality and should be run on any PRs submitted.\n"

  1125.       ]

  1126.     },

  1127.     {

  1128.       "cell_type": "code",

  1129.       "metadata": {

  1130.         "id": "mcKoSIK2WSzj"

  1131.       },

  1132.       "source": [

  1133.         "# Reproduce\n",

  1134.         "for x in 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x':\n",

  1135.         "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45  # speed\n",

  1136.         "  !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65  # mAP"

  1137.       ],

  1138.       "execution_count": null,

  1139.       "outputs": []

  1140.     },

  1141.     {

  1142.       "cell_type": "code",

  1143.       "metadata": {

  1144.         "id": "GMusP4OAxFu6"

  1145.       },

  1146.       "source": [

  1147.         "# PyTorch Hub\n",

  1148.         "import torch\n",

  1149.         "\n",

  1150.         "# Model\n",

  1151.         "model = torch.hub.load('ultralytics/yolov5', 'yolov5s')\n",

  1152.         "\n",

  1153.         "# Images\n",

  1154.         "dir = 'https://ultralytics.com/images/'\n",

  1155.         "imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')]  # batch of images\n",

  1156.         "\n",

  1157.         "# Inference\n",

  1158.         "results = model(imgs)\n",

  1159.         "results.print()  # or .show(), .save()"

  1160.       ],

  1161.       "execution_count": null,

  1162.       "outputs": []

  1163.     },

  1164.     {

  1165.       "cell_type": "code",

  1166.       "metadata": {

  1167.         "id": "FGH0ZjkGjejy"

  1168.       },

  1169.       "source": [

  1170.         "# Unit tests\n",

  1171.         "%%shell\n",

  1172.         "export PYTHONPATH=\"$PWD\"  # to run *.py. files in subdirectories\n",

  1173.         "\n",

  1174.         "rm -rf runs  # remove runs/\n",

  1175.         "for m in yolov5s; do  # models\n",

  1176.         "  python train.py --weights $m.pt --epochs 3 --img 320 --device 0  # train pretrained\n",

  1177.         "  python train.py --weights '' --cfg $m.yaml --epochs 3 --img 320 --device 0  # train scratch\n",

  1178.         "  for d in 0 cpu; do  # devices\n",

  1179.         "    python detect.py --weights $m.pt --device $d  # detect official\n",

  1180.         "    python detect.py --weights runs/train/exp/weights/best.pt --device $d  # detect custom\n",

  1181.         "    python val.py --weights $m.pt --device $d # val official\n",

  1182.         "    python val.py --weights runs/train/exp/weights/best.pt --device $d # val custom\n",

  1183.         "  done\n",

  1184.         "  python hubconf.py  # hub\n",

  1185.         "  python models/yolo.py --cfg $m.yaml  # inspect\n",

  1186.         "  python export.py --weights $m.pt --img 640 --batch 1  # export\n",

  1187.         "done"

  1188.       ],

  1189.       "execution_count": null,

  1190.       "outputs": []

  1191.     },

  1192.     {

  1193.       "cell_type": "code",

  1194.       "metadata": {

  1195.         "id": "gogI-kwi3Tye"

  1196.       },

  1197.       "source": [

  1198.         "# Profile\n",

  1199.         "from utils.torch_utils import profile\n",

  1200.         "\n",

  1201.         "m1 = lambda x: x * torch.sigmoid(x)\n",

  1202.         "m2 = torch.nn.SiLU()\n",

  1203.         "results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)"

  1204.       ],

  1205.       "execution_count": null,

  1206.       "outputs": []

  1207.     },

  1208.     {

  1209.       "cell_type": "code",

  1210.       "metadata": {

  1211.         "id": "RVRSOhEvUdb5"

  1212.       },

  1213.       "source": [

  1214.         "# Evolve\n",

  1215.         "!python train.py --img 640 --batch 64 --epochs 100 --data coco128.yaml --weights yolov5s.pt --cache --noautoanchor --evolve\n",

  1216.         "!d=runs/train/evolve && cp evolve.* $d && zip -r evolve.zip $d && gsutil mv evolve.zip gs://bucket  # upload results (optional)"

  1217.       ],

  1218.       "execution_count": null,

  1219.       "outputs": []

  1220.     },

  1221.     {

  1222.       "cell_type": "code",

  1223.       "metadata": {

  1224.         "id": "BSgFCAcMbk1R"

  1225.       },

  1226.       "source": [

  1227.         "# VOC\n",

  1228.         "for b, m in zip([64, 48, 32, 16], ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']):  # zip(batch_size, model)\n",

  1229.         "  !python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --cache --img 512 --nosave --hyp hyp.finetune.yaml --project VOC --name {m}"

  1230.       ],

  1231.       "execution_count": null,

  1232.       "outputs": []

  1233.     }

  1234.   ]

  1235. }