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\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", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
We hope that the resources in this notebook will help you get the most out of YOLOv5. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", "\n", "" ] @@ -401,7 +54,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "f9f016ad-3dcf-4bd2-e1c3-d5b79efc6f32" + "outputId": "e8225db4-e61d-4640-8b1f-8bfce3331cea" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", @@ -412,20 +65,20 @@ "import utils\n", "display = utils.notebook_init() # checks" ], - "execution_count": null, + "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ - "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ - "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/166.8 GB disk)\n" ] } ] @@ -459,29 +112,29 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "b4db5c49-f501-4505-cf0d-a1d35236c485" + "outputId": "284ef04b-1596-412f-88f6-948828dd2b49" }, "source": [ "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n", "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)" ], - "execution_count": null, + "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n", - "YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", "\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", + "100% 14.1M/14.1M [00:00<00:00, 24.5MB/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, 17.0ms\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", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 41.5ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 60.0ms\n", + "Speed: 0.5ms pre-process, 50.8ms inference, 37.7ms NMS per image at shape (1, 3, 640, 640)\n", "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n" ] } @@ -512,44 +165,23 @@ "metadata": { "id": "WQPtK1QYVaD_", "colab": { - "base_uri": "https://localhost:8080/", - "height": 49, - "referenced_widgets": [ - "1f7df330663048998adcf8a45bc8f69b", - "e896e6096dd244c59d7955e2035cd729", - "a6ff238c29984b24bf6d0bd175c19430", - "3c085ba3f3fd4c3c8a6bb41b41ce1479", - "16b0c8aa6e0f427e8a54d3791abb7504", - "c7b2dd0f78384cad8e400b282996cdf5", - "6a27e43b0e434edd82ee63f0a91036ca", - "cce0e6c0c4ec442cb47e65c674e02e92", - "c5b9f38e2f0d4f9aa97fe87265263743", - "df554fb955c7454696beac5a82889386", - "74e9112a87a242f4831b7d68c7da6333" - ] + "base_uri": "https://localhost:8080/" }, - "outputId": "c7d0a0d2-abfb-44c3-d60d-f99d0e7aabad" + "outputId": "cf7d52f0-281c-4c96-a488-79f5908f8426" }, "source": [ "# Download COCO val\n", "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n", "!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip" ], - "execution_count": null, + "execution_count": 3, "outputs": [ { - "output_type": "display_data", - "data": { - "text/plain": [ - " 0%| | 0.00/780M [00:00

\n", + "

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

\n", "\n", @@ -645,14 +277,14 @@ "cell_type": "code", "source": [ "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", - "logger = 'ClearML' #@param ['ClearML', 'Comet', 'TensorBoard']\n", + "logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n", "\n", - "if logger == 'ClearML':\n", - " %pip install -q clearml\n", - " import clearml; clearml.browser_login()\n", - "elif logger == 'Comet':\n", + "if logger == 'Comet':\n", " %pip install -q comet_ml\n", " import comet_ml; comet_ml.init()\n", + "elif logger == 'ClearML':\n", + " %pip install -q clearml\n", + " import clearml; clearml.browser_login()\n", "elif logger == 'TensorBoard':\n", " %load_ext tensorboard\n", " %tensorboard --logdir runs/train" @@ -670,21 +302,24 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "721b9028-767f-4a05-c964-692c245f7398" + "outputId": "bbeeea2b-04fc-4185-aa64-258690495b5a" }, "source": [ "# Train YOLOv5s on COCO128 for 3 epochs\n", "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache" ], - "execution_count": null, + "execution_count": 5, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ + "2023-04-09 14:11:38.063605: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-04-09 14:11:39.026661: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", "\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 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\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", @@ -693,8 +328,8 @@ "\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, 261MB/s]\n", - "Dataset download success ✅ (0.3s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "100% 6.66M/6.66M [00:00<00:00, 75.6MB/s]\n", + "Dataset download success ✅ (0.6s), 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", @@ -728,11 +363,11 @@ "\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, 1911.57it/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, 1709.36it/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, 229.69it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 264.35it/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