{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "t6MPjfT5NrKQ" }, "source": [ "
\n", "\n", " \n", " \n", "\n", "\n", "
\n", " \"Run\n", " \"Open\n", " \"Open\n", "
\n", "\n", "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "7mGmQbAO5pQb" }, "source": [ "# Setup\n", "\n", "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wbvMlHd_QwMG", "outputId": "0f9ee467-cea4-48e8-9050-7a76ae1b6141" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "YOLOv5 🚀 v6.2-225-gf223cb2 Python-3.7.12 torch-1.12.1+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16161MiB)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Setup complete ✅ (4 CPUs, 14.7 GB RAM, 107.3/196.6 GB disk)\n" ] } ], "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", "%cd yolov5\n", "%pip install -qr requirements.txt # install\n", "\n", "import torch\n", "import utils\n", "display = utils.notebook_init() # checks" ] }, { "cell_type": "markdown", "metadata": { "id": "4JnkELT0cIJg" }, "source": [ "# 1. Predict\n", "\n", "`segment/predict.py` runs YOLOv5 instance segmentation 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/predict`. Example inference sources are:\n", "\n", "```shell\n", "python segment/predict.py --source 0 # webcam\n", " img.jpg # image \n", " 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", "```" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zR9ZbuQCH7FX", "outputId": "60647b99-e8d4-402c-f444-331bf6746da4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1msegment/predict: \u001b[0mweights=['yolov5s-seg.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/predict-seg, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n", "YOLOv5 🚀 v6.2-225-gf223cb2 Python-3.7.12 torch-1.12.1+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16161MiB)\n", "\n", "Fusing layers... \n", "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n", "image 1/2 /home/paguerrie/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 5.6ms\n", "image 2/2 /home/paguerrie/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 5.5ms\n", "Speed: 0.4ms pre-process, 5.6ms inference, 1.1ms NMS per image at shape (1, 3, 640, 640)\n", "Results saved to \u001b[1mruns/predict-seg/exp\u001b[0m\n" ] } ], "source": [ "!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images\n", "#display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)" ] }, { "cell_type": "markdown", "metadata": { "id": "hkAzDWJ7cWTr" }, "source": [ "        \n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "0eq1SMWl6Sfn" }, "source": [ "# 2. Validate\n", "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "9b8caa3522fc4cbab31e13b5dfc7808d", "574140e4c4bc48c9a171541a02cd0211", "35e03ce5090346c9ae602891470fc555", "c942c208e72d46568b476bb0f2d75496", "65881db1db8a4e9c930fab9172d45143", "60b913d755b34d638478e30705a2dde1", "0856bea36ec148b68522ff9c9eb258d8", "76879f6f2aa54637a7a07faeea2bd684", "0ace3934ec6f4d36a1b3a9e086390926", "d6b7a2243e0c4beca714d99dceec23d6", "5966ba6e6f114d8c9d8d1d6b1bd4f4c7" ] }, "id": "WQPtK1QYVaD_", "outputId": "102dabed-bc31-42fe-9133-d9ce28a2c01e" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "89f5f0a84ca642378724f1bf05f17e0d", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0.00/6.79M [00:00

\n", "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", "

\n", "\n", "Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n", "\n", "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n", "- **Training Results** are saved to `runs/train-seg/` with incrementing run directories, i.e. `runs/train-seg/exp2`, `runs/train-seg/exp3` etc.\n", "

\n", "\n", "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", "\n", "## Train on Custom Data with Roboflow 🌟 NEW\n", "\n", "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", "\n", "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/](https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/?ref=ultralytics)\n", "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JTz7kpmHsg-5qwVz2d2IH3AaenI1tv0N?usp=sharing)\n", "
\n", "\n", "

Label images lightning fast (including with model-assisted labeling)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "i3oKtE4g-aNn" }, "outputs": [], "source": [ "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", "logger = 'TensorBoard' #@param ['TensorBoard', 'Comet', 'ClearML']\n", "\n", "if logger == 'TensorBoard':\n", " %load_ext tensorboard\n", " %tensorboard --logdir runs/train-seg\n", "elif logger == 'Comet':\n", " %pip install -q comet_ml\n", " import comet_ml; comet_ml.init()\n", "elif logger == 'ClearML':\n", " %pip install -q clearml && clearml-init" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1NcFxRcFdJ_O", "outputId": "baa6d4be-3379-4aab-844a-d5a5396c0e49" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[34m\u001b[1msegment/train: \u001b[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.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-seg, 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, mask_ratio=4, no_overlap=False\n", "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", "YOLOv5 🚀 v6.2-225-gf223cb2 Python-3.7.12 torch-1.12.1+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16161MiB)\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[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n", "\n", " from n params module arguments \n", " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n", " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 models.common.Concat [1] \n", " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 16 [-1, 4] 1 0 models.common.Concat [1] \n", " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", " 19 [-1, 14] 1 0 models.common.Concat [1] \n", " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", " 22 [-1, 10] 1 0 models.common.Concat [1] \n", " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", " 24 [17, 20, 23] 1 615133 models.yolo.Segment [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], 32, 128, [128, 256, 512]]\n", "Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\n", "\n", "Transferred 367/367 items from yolov5s-seg.pt\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias\n", "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/home/paguerrie/datasets/coco128-seg/labels/train2017.cache' im\u001b[0m\n", "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100%|██████████| 128/128 [00:00<00:00, 544.41\u001b[0m\n", "\u001b[34m\u001b[1mval: \u001b[0mScanning '/home/paguerrie/datasets/coco128-seg/labels/train2017.cache' imag\u001b[0m\n", "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100%|██████████| 128/128 [00:00<00:00, 138.66it\u001b[0m\n", "\n", "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n", "Plotting labels to runs/train-seg/exp/labels.jpg... \n", "Image sizes 640 train, 640 val\n", "Using 4 dataloader workers\n", "Logging results to \u001b[1mruns/train-seg/exp\u001b[0m\n", "Starting training for 3 epochs...\n", "\n", " Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n", " 0/2 4.67G 0.04464 0.05134 0.06548 0.01895 219 \n", " Class Images Instances Box(P R mAP50 m\n", " all 128 929 0.727 0.661 0.725 0.496 0.688 0.629 0.673 0.413\n", "\n", " Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n", " 1/2 6.36G 0.04102 0.04702 0.06873 0.01734 263 \n", " Class Images Instances Box(P R mAP50 m\n", " all 128 929 0.752 0.676 0.743 0.51 0.704 0.64 0.682 0.425\n", "\n", " Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n", " 2/2 6.36G 0.0421 0.04463 0.05951 0.01746 245 \n", " Class Images Instances Box(P R mAP50 m\n", " all 128 929 0.776 0.674 0.757 0.514 0.72 0.632 0.684 0.429\n", "\n", "3 epochs completed in 0.006 hours.\n", "Optimizer stripped from runs/train-seg/exp/weights/last.pt, 15.6MB\n", "Optimizer stripped from runs/train-seg/exp/weights/best.pt, 15.6MB\n", "\n", "Validating runs/train-seg/exp/weights/best.pt...\n", "Fusing layers... \n", "Model summary: 165 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n", " Class Images Instances Box(P R mAP50 m\n", " all 128 929 0.775 0.673 0.758 0.515 0.72 0.632 0.684 0.427\n", " person 128 254 0.829 0.745 0.833 0.545 0.776 0.697 0.764 0.406\n", " bicycle 128 6 0.614 0.333 0.539 0.331 0.614 0.333 0.531 0.308\n", " car 128 46 0.774 0.413 0.571 0.266 0.693 0.37 0.493 0.204\n", " motorcycle 128 5 0.817 0.901 0.895 0.678 0.817 0.901 0.895 0.47\n", " airplane 128 6 1 0.951 0.995 0.71 0.882 0.833 0.839 0.515\n", " bus 128 7 0.695 0.714 0.757 0.661 0.695 0.714 0.757 0.627\n", " train 128 3 1 0.935 0.995 0.566 1 0.935 0.995 0.731\n", " truck 128 12 0.741 0.417 0.463 0.283 0.741 0.417 0.4 0.27\n", " boat 128 6 0.653 0.32 0.452 0.17 0.653 0.32 0.328 0.149\n", " traffic light 128 14 0.627 0.36 0.527 0.234 0.503 0.289 0.409 0.293\n", " stop sign 128 2 0.829 1 0.995 0.747 0.829 1 0.995 0.821\n", " bench 128 9 0.822 0.667 0.76 0.414 0.685 0.556 0.678 0.228\n", " bird 128 16 0.967 1 0.995 0.675 0.906 0.938 0.909 0.516\n", " cat 128 4 0.778 0.89 0.945 0.728 0.778 0.89 0.945 0.69\n", " dog 128 9 1 0.65 0.973 0.697 1 0.65 0.939 0.615\n", " horse 128 2 0.727 1 0.995 0.672 0.727 1 0.995 0.2\n", " elephant 128 17 1 0.912 0.946 0.704 0.871 0.794 0.822 0.565\n", " bear 128 1 0.626 1 0.995 0.895 0.626 1 0.995 0.895\n", " zebra 128 4 0.865 1 0.995 0.934 0.865 1 0.995 0.822\n", " giraffe 128 9 0.975 1 0.995 0.672 0.866 0.889 0.876 0.473\n", " backpack 128 6 1 0.573 0.707 0.38 0.891 0.5 0.524 0.249\n", " umbrella 128 18 0.744 0.889 0.926 0.552 0.465 0.556 0.483 0.262\n", " handbag 128 19 0.799 0.209 0.432 0.225 0.799 0.209 0.403 0.201\n", " tie 128 7 0.968 0.857 0.857 0.53 0.968 0.857 0.857 0.519\n", " suitcase 128 4 0.821 1 0.995 0.696 0.821 1 0.995 0.665\n", " frisbee 128 5 0.777 0.8 0.761 0.613 0.777 0.8 0.761 0.558\n", " skis 128 1 0.721 1 0.995 0.497 0.721 1 0.995 0.398\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " snowboard 128 7 0.851 0.857 0.887 0.599 0.284 0.286 0.253 0.151\n", " sports ball 128 6 0.961 0.667 0.687 0.429 0.721 0.5 0.481 0.476\n", " kite 128 10 0.508 0.312 0.48 0.238 0.508 0.312 0.406 0.122\n", " baseball bat 128 4 0.331 0.5 0.526 0.249 0.331 0.5 0.376 0.102\n", " baseball glove 128 7 0.876 0.571 0.579 0.282 0.657 0.429 0.429 0.343\n", " skateboard 128 5 1 0.697 0.824 0.471 0.707 0.497 0.552 0.299\n", " tennis racket 128 7 0.524 0.714 0.646 0.426 0.524 0.714 0.646 0.452\n", " bottle 128 18 0.657 0.389 0.531 0.359 0.657 0.389 0.569 0.362\n", " wine glass 128 16 0.752 0.938 0.924 0.435 0.451 0.562 0.568 0.341\n", " cup 128 36 0.859 0.676 0.848 0.503 0.823 0.648 0.793 0.496\n", " fork 128 6 0.904 0.333 0.462 0.309 0.452 0.167 0.195 0.107\n", " knife 128 16 0.749 0.5 0.665 0.413 0.655 0.438 0.523 0.314\n", " spoon 128 22 0.787 0.409 0.577 0.275 0.787 0.409 0.528 0.236\n", " bowl 128 28 0.793 0.679 0.744 0.577 0.751 0.643 0.688 0.366\n", " banana 128 1 0.931 1 0.995 0.398 0.931 1 0.995 0.497\n", " sandwich 128 2 1 0 0.828 0.713 1 0 0.498 0.449\n", " orange 128 4 0.588 1 0.995 0.666 0.588 1 0.995 0.672\n", " broccoli 128 11 0.563 0.455 0.356 0.258 0.563 0.455 0.362 0.259\n", " carrot 128 24 0.683 0.75 0.753 0.489 0.758 0.833 0.835 0.451\n", " hot dog 128 2 0.583 1 0.995 0.995 0.583 1 0.995 0.796\n", " pizza 128 5 0.801 0.8 0.962 0.644 0.801 0.8 0.962 0.583\n", " donut 128 14 0.704 1 0.889 0.759 0.704 1 0.889 0.683\n", " cake 128 4 0.904 1 0.995 0.896 0.904 1 0.995 0.838\n", " chair 128 35 0.672 0.543 0.629 0.333 0.708 0.571 0.583 0.284\n", " couch 128 6 0.827 0.5 0.821 0.583 0.827 0.5 0.681 0.352\n", " potted plant 128 14 0.809 0.908 0.884 0.584 0.809 0.908 0.884 0.474\n", " bed 128 3 1 0.654 0.913 0.36 1 0.654 0.913 0.418\n", " dining table 128 13 0.803 0.385 0.557 0.361 0.321 0.154 0.126 0.0487\n", " toilet 128 2 0.802 1 0.995 0.921 0.802 1 0.995 0.698\n", " tv 128 2 0.59 1 0.995 0.846 0.59 1 0.995 0.846\n", " laptop 128 3 1 0 0.451 0.324 1 0 0.372 0.157\n", " mouse 128 2 1 0 0 0 1 0 0 0\n", " remote 128 8 0.831 0.5 0.625 0.495 0.831 0.5 0.629 0.436\n", " cell phone 128 8 0.867 0.375 0.482 0.26 0.578 0.25 0.302 0.127\n", " microwave 128 3 0.782 1 0.995 0.695 0.782 1 0.995 0.585\n", " oven 128 5 0.389 0.4 0.432 0.299 0.584 0.6 0.642 0.411\n", " sink 128 6 0.657 0.5 0.491 0.373 0.657 0.5 0.436 0.303\n", " refrigerator 128 5 0.729 0.8 0.778 0.547 0.729 0.8 0.778 0.496\n", " book 128 29 0.77 0.231 0.451 0.186 0.77 0.231 0.399 0.136\n", " clock 128 9 0.798 0.889 0.956 0.747 0.798 0.889 0.926 0.68\n", " vase 128 2 0.437 1 0.995 0.895 0.437 1 0.995 0.796\n", " scissors 128 1 0 0 0.0226 0.0113 0 0 0 0\n", " teddy bear 128 21 0.815 0.629 0.877 0.521 0.753 0.582 0.793 0.435\n", " toothbrush 128 5 1 0.719 0.995 0.737 1 0.719 0.995 0.606\n", "Results saved to \u001b[1mruns/train-seg/exp\u001b[0m\n" ] } ], "source": [ "# Train YOLOv5s on COCO128 for 3 epochs\n", "!python segment/train.py --img 640 --batch 16 --epochs 3 --data coco128-seg.yaml --weights yolov5s-seg.pt --cache" ] }, { "cell_type": "markdown", "metadata": { "id": "15glLzbQx5u0" }, "source": [ "# 4. Visualize" ] }, { "cell_type": "markdown", "metadata": { "id": "nWOsI5wJR1o3" }, "source": [ "## Comet Logging and Visualization 🌟 NEW\n", "[Comet](https://bit.ly/yolov5-readme-comet) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! \n", "\n", "Getting started is easy:\n", "```shell\n", "pip install comet_ml # 1. install\n", "export COMET_API_KEY= # 2. paste API key\n", "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", "```\n", "\n", "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://bit.ly/yolov5-colab-comet-docs). Get started by trying out the Comet Colab Notebook:\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", "\n", "\"yolo-ui\"" ] }, { "cell_type": "markdown", "metadata": { "id": "Lay2WsTjNJzP" }, "source": [ "## ClearML Logging and Automation 🌟 NEW\n", "\n", "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", "\n", "- `pip install clearml`\n", "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", "\n", "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", "\n", "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n", "\n", "\n", "\"ClearML" ] }, { "cell_type": "markdown", "metadata": { "id": "-WPvRbS5Swl6" }, "source": [ "## Local Logging\n", "\n", "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", "\n", "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n", "\n", "\"Local\n" ] }, { "cell_type": "markdown", "metadata": { "id": "Zelyeqbyt3GD" }, "source": [ "# Environments\n", "\n", "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", "\n", "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" ] }, { "cell_type": "markdown", "metadata": { "id": "6Qu7Iesl0p54" }, "source": [ "# Status\n", "\n", "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", "\n", "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" ] }, { "cell_type": "markdown", "metadata": { "id": "IEijrePND_2I" }, "source": [ "# Appendix\n", "\n", "Additional content below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GMusP4OAxFu6" }, "outputs": [], "source": [ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", "import torch\n", "\n", "model = torch.hub.load('ultralytics/yolov5', 'yolov5s-seg') # yolov5n - yolov5x6 or custom\n", "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", "results = model(im) # inference\n", "results.print() # or .show(), .save(), .crop(), .pandas(), etc." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "machine_shape": "hm", "name": "YOLOv5 Tutorial", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0856bea36ec148b68522ff9c9eb258d8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", 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