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"grid_template_areas": null, - "object_position": null, - "object_fit": null, - "grid_auto_columns": null, - "margin": null, - "display": null, - "left": null + "_view_name": "StyleView", + "description_width": "" } } } @@ -368,7 +369,7 @@ "colab_type": "text" }, "source": [ - "\"Open" + "\"Open" ] }, { @@ -402,7 +403,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "3809e5a9-dd41-4577-fe62-5531abf7cca2" + "outputId": "ebf225bd-e109-4dbd-8561-3b15514ca47c" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone\n", @@ -413,14 +414,20 @@ "import utils\n", "display = utils.notebook_init() # checks" ], - "execution_count": null, + "execution_count": 1, "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v6.1-174-gc4cb7c6 torch 1.11.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n" + ] + }, { "output_type": "stream", "name": "stdout", "text": [ - "YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", - "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 42.2/166.8 GB disk)\n" + "Setup complete ✅ (8 CPUs, 51.0 GB RAM, 38.2/166.8 GB disk)\n" ] } ] @@ -453,26 +460,29 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "8f7e6588-215d-4ebd-93af-88b871e770a7" + "outputId": "2f43338d-f533-4277-ef9f-b37b565e2702" }, "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": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, 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\n", - "YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", + "\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\n", + "YOLOv5 🚀 v6.1-174-gc4cb7c6 torch 1.11.0+cu113 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt to yolov5s.pt...\n", + "100% 14.1M/14.1M [00:00<00:00, 220MB/s]\n", "\n", "Fusing layers... \n", - "Model Summary: 213 layers, 7225885 parameters, 0 gradients\n", - "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.007s)\n", - "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.007s)\n", - "Speed: 0.5ms pre-process, 6.9ms inference, 1.3ms NMS per image at shape (1, 3, 640, 640)\n", + "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.012s)\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.013s)\n", + "Speed: 0.5ms pre-process, 12.5ms inference, 17.3ms NMS per image at shape (1, 3, 640, 640)\n", "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n" ] } @@ -514,41 +524,41 @@ "id": "WQPtK1QYVaD_", "colab": { "base_uri": "https://localhost:8080/", - "height": 48, + "height": 49, "referenced_widgets": [ - "eb95db7cae194218b3fcefb439b6352f", - "769ecde6f2e64bacb596ce972f8d3d2d", - "384a001876054c93b0af45cd1e960bfe", - "dded0aeae74440f7ba2ffa0beb8dd612", - "5296d28be75740b2892ae421bbec3657", - "9f09facb2a6c4a7096810d327c8b551c", - "25621cff5d16448cb7260e839fd0f543", - "0ce7164fc0c74bb9a2b5c7037375a727", - "c4c4593c10904cb5b8a5724d60c7e181", - "473371611126476c88d5d42ec7031ed6", - "65efdfd0d26c46e79c8c5ff3b77126cc" + "d90eeb56398f458086e3b2b41dbd9fec", + "d91d8347f17349a4987cea29eac0a49c", + "8f4ffda703ac4348ab7edf1d12a188e1", + "8c2d91f564de45f8a403386eeeccac27", + "5dd95d3eda8b49f7910620edcdcbdcdc", + "520e5b7e80eb450188261cffbc574d25", + "3cef138c5f7743858bb0f87b65dd3c76", + "c3782c6dda80400ba7f8c5345624bf87", + "11415bab172a4904b73e29ff60f6fce1", + "eac18040908042dbae67a47d23e95b47", + "e0fc1d6eb478469c9098aa9518d7b358" ] }, - "outputId": "bcf9a448-1f9b-4a41-ad49-12f181faf05a" + "outputId": "26f3c005-cc13-4b7c-8523-844b56a0b0e3" }, "source": [ "# Download COCO val\n", "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n", "!unzip -q tmp.zip -d ../datasets && rm tmp.zip" ], - "execution_count": null, + "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "eb95db7cae194218b3fcefb439b6352f", - "version_minor": 0, - "version_major": 2 - }, "text/plain": [ " 0%| | 0.00/780M [00:00