{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "CNG SCOOTER Detection.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "A2X7YiYPe-W2" }, "source": [ "# CNG Scooter Detection Using YOLOv5 \n", "Reference of this work: https://github.com/ultralytics/yolov5 \n", "\n", "CNG Scooter Dataset : Omdena Repo/src/data/Computer Vision Dataset/dataset.zip\n", "\n", "Drag and drop the dataset.zip file in the Files section. Then go with the following code. " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "I-zJwUr03zgI", "outputId": "5dd78da7-41e7-485e-e4da-bbb7d6374159" }, "source": [ "!nvidia-smi" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Mon Jul 19 08:27:53 2021 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 470.42.01 Driver Version: 460.32.03 CUDA Version: 11.2 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla K80 Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 48C P8 29W / 149W | 0MiB / 11441MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "BWV-dJLIxAla" }, "source": [ "# Setup\n", "\n", "Clone repo, install dependencies and check PyTorch and GPU." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "y6OaylyrsIAu", "outputId": "d0c7728b-af3e-439b-cc07-172f2e86aa89" }, "source": [ "!git clone https://github.com/ultralytics/yolov5 # clone repo\n", "%cd yolov5\n", "%pip install -qr requirements.txt # install dependencies\n", "\n", "import torch\n", "from IPython.display import Image, clear_output # to display images\n", "\n", "clear_output()\n", "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Setup complete. Using torch 1.9.0+cu102 (Tesla K80)\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "VaoF7cSf0Ygw" }, "source": [ "# Loading Data\n", "\n", "* First drag and drop the dataset.zip in Files. (if it is not there) or Keep the dataset.zip in MyDrive\n", "* If you are loading dataset from your drive, then -> Mount Drive\n", "* Run the following cell to unzip the dataset.zip" ] }, { "cell_type": "code", "metadata": { "id": "MUXka9UKwu7P" }, "source": [ "!unzip -q ../drive/MyDrive/dataset.zip -d ../" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "SgNIgZ2b3U3v" }, "source": [ "# Training \n", "Before training we have to modify coco128.yaml file. So go to `yolov5/data` you will find the coco128.yaml file and rename it as cng_data.yaml. Then write this file as following.\n", "\n", "```\n", "train: ../dataset/images/train/ # train images (relative to 'path')\n", "val: ../dataset/images/val/ # val images (relative to 'path') \n", "test: # test images (optional)\n", "\n", "# Classes\n", "nc: 1 # number of classes\n", "names: [ 'cng scooter' ] # class names\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "LS92af9cLHnh" }, "source": [ "Now we are going to train the YOLOv5 model with CNG Scooter dataset." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DbNjbDiV0EPF", "outputId": "6a2a4ffc-b804-4a1d-968c-af3ee7d8b9d1" }, "source": [ "# Train YOLOv5s on CNG_DATASET for 3 epochs\n", "!python train.py --img 640 --batch 8 --epochs 20 --data cng_data.yaml --weights yolov5s.pt --cache" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=cng_data.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=20, batch_size=8, img_size=[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", "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", "YOLOv5 🚀 v5.0-295-g9dd33fd torch 1.9.0+cu102 CUDA:0 (Tesla K80, 11441.1875MB)\n", "\n", "\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", "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", "2021-07-19 08:30:20.498373: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0\n", "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)\n", "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt to yolov5s.pt...\n", "100% 14.1M/14.1M [00:00<00:00, 22.2MB/s]\n", "\n", "Overriding model.yaml nc=80 with nc=1\n", "\n", " from n params module arguments \n", " 0 -1 1 3520 models.common.Focus [3, 32, 3] \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 1 156928 models.common.C3 [128, 128, 3] \n", " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", " 6 -1 1 625152 models.common.C3 [256, 256, 3] \n", " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", " 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n", " 9 -1 1 1182720 models.common.C3 [512, 512, 1, False] \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 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n", "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n", " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n", "Model Summary: 283 layers, 7063542 parameters, 7063542 gradients, 16.4 GFLOPs\n", "\n", "Transferred 356/362 items from yolov5s.pt\n", "Scaled weight_decay = 0.0005\n", "Optimizer groups: 62 .bias, 62 conv.weight, 59 other\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.2 required by YOLOv5, but version 0.1.12 is currently installed\n", "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../dataset/labels/train' images and labels...929 found, 0 missing, 63 empty, 0 corrupted: 100% 929/929 [00:00<00:00, 1847.30it/s]\n", "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../dataset/labels/train.cache\n", "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.6GB): 100% 929/929 [00:12<00:00, 76.72it/s] \n", "\u001b[34m\u001b[1mval: \u001b[0mScanning '../dataset/labels/val' images and labels...201 found, 0 missing, 3 empty, 0 corrupted: 100% 201/201 [00:00<00:00, 743.78it/s]\n", "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../dataset/labels/val.cache\n", "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB): 100% 201/201 [00:01<00:00, 105.95it/s]\n", "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n", "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n", "Plotting labels... \n", "\n", "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 5.05, Best Possible Recall (BPR) = 0.9984\n", "Image sizes 640 train, 640 val\n", "Using 2 dataloader workers\n", "Logging results to runs/train/exp\n", "Starting training for 20 epochs...\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 0/19 1.79G 0.09934 0.04337 0 0.1427 4 640: 100% 117/117 [01:13<00:00, 1.59it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 3.55it/s]\n", " all 201 457 0.589 0.516 0.534 0.23\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 1/19 1.93G 0.06648 0.04526 0 0.1117 4 640: 100% 117/117 [01:09<00:00, 1.68it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 3.78it/s]\n", " all 201 457 0.605 0.521 0.516 0.197\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 2/19 1.93G 0.05936 0.03668 0 0.09604 7 640: 100% 117/117 [01:08<00:00, 1.70it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.06it/s]\n", " all 201 457 0.597 0.63 0.587 0.268\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 3/19 1.93G 0.05754 0.03177 0 0.08931 2 640: 100% 117/117 [01:08<00:00, 1.70it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.13it/s]\n", " all 201 457 0.703 0.72 0.724 0.342\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 4/19 1.93G 0.05424 0.02957 0 0.08381 2 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.16it/s]\n", " all 201 457 0.58 0.755 0.641 0.318\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 5/19 1.93G 0.05278 0.02881 0 0.08159 7 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.21it/s]\n", " all 201 457 0.873 0.72 0.789 0.377\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 6/19 1.93G 0.0493 0.02748 0 0.07678 4 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.19it/s]\n", " all 201 457 0.907 0.722 0.77 0.433\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 7/19 1.93G 0.04594 0.02638 0 0.07232 13 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.22it/s]\n", " all 201 457 0.898 0.735 0.792 0.494\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 8/19 1.93G 0.04602 0.02605 0 0.07207 1 640: 100% 117/117 [01:08<00:00, 1.72it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.19it/s]\n", " all 201 457 0.911 0.716 0.792 0.442\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 9/19 1.93G 0.04328 0.02517 0 0.06845 2 640: 100% 117/117 [01:08<00:00, 1.72it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.20it/s]\n", " all 201 457 0.922 0.726 0.795 0.482\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 10/19 1.93G 0.03929 0.02428 0 0.06357 1 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.24it/s]\n", " all 201 457 0.882 0.772 0.797 0.484\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 11/19 1.93G 0.03715 0.0228 0 0.05995 3 640: 100% 117/117 [01:08<00:00, 1.72it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.26it/s]\n", " all 201 457 0.876 0.757 0.795 0.477\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 12/19 1.93G 0.03509 0.0231 0 0.05819 5 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.28it/s]\n", " all 201 457 0.907 0.748 0.802 0.478\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 13/19 1.93G 0.0335 0.02294 0 0.05644 2 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.20it/s]\n", " all 201 457 0.949 0.733 0.811 0.512\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 14/19 1.93G 0.03148 0.0219 0 0.05338 3 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.01it/s]\n", " all 201 457 0.881 0.761 0.806 0.496\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 15/19 1.93G 0.03191 0.02236 0 0.05427 5 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.16it/s]\n", " all 201 457 0.898 0.761 0.806 0.507\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 16/19 1.93G 0.03073 0.0217 0 0.05243 1 640: 100% 117/117 [01:08<00:00, 1.72it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.27it/s]\n", " all 201 457 0.939 0.748 0.811 0.506\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 17/19 1.93G 0.0311 0.02249 0 0.05359 7 640: 100% 117/117 [01:08<00:00, 1.72it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.19it/s]\n", " all 201 457 0.938 0.759 0.823 0.517\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 18/19 1.93G 0.03013 0.02119 0 0.05132 7 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 4.15it/s]\n", " all 201 457 0.895 0.788 0.825 0.51\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 19/19 1.93G 0.02996 0.02079 0 0.05075 2 640: 100% 117/117 [01:08<00:00, 1.71it/s]\n", " Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 13/13 [00:03<00:00, 3.34it/s]\n", " all 201 457 0.937 0.746 0.815 0.509\n", "20 epochs completed in 0.403 hours.\n", "\n", "Optimizer stripped from runs/train/exp/weights/last.pt, 14.4MB\n", "Optimizer stripped from runs/train/exp/weights/best.pt, 14.4MB\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "j5R0pQAdLVSS" }, "source": [ "" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bJAkI787LVoa" }, "source": [ "# Inference\n", "\n", "We can test the trained model on video. You can give your preferred video in place of `cng2.avi`\n", "\n", "Output Destination: yolov5/runs/detect/" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "t6bZS7CrLWXx", "outputId": "09944133-7aaa-486b-d7e7-4785a6e04e65" }, "source": [ "!python detect.py --weights runs/train/exp/weights/best.pt --img 640 --conf 0.25 --source ../drive/MyDrive/cng2.avi" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['runs/train/exp/weights/best.pt'], source=../drive/MyDrive/cng2.avi, 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", "YOLOv5 🚀 v5.0-295-g9dd33fd torch 1.9.0+cu102 CUDA:0 (Tesla K80, 11441.1875MB)\n", "\n", "Fusing layers... \n", "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n", " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n", "Model Summary: 224 layers, 7053910 parameters, 0 gradients, 16.3 GFLOPs\n", "video 1/1 (1/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.027s)\n", "video 1/1 (2/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.026s)\n", "video 1/1 (3/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.025s)\n", "video 1/1 (4/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.026s)\n", "video 1/1 (5/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.026s)\n", "video 1/1 (6/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.025s)\n", "video 1/1 (7/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 6 cng scooters, Done. (0.025s)\n", "video 1/1 (8/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.025s)\n", "video 1/1 (9/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.026s)\n", "video 1/1 (10/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.025s)\n", "video 1/1 (11/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 6 cng scooters, Done. (0.025s)\n", "video 1/1 (12/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.026s)\n", "video 1/1 (13/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.025s)\n", "video 1/1 (14/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. (0.025s)\n", "video 1/1 (15/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 5 cng scooters, Done. 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(0.025s)\n", "video 1/1 (821/821) /content/yolov5/../drive/MyDrive/cng2.avi: 384x640 1 cng scooter, Done. (0.025s)\n", "Results saved to runs/detect/exp2\n", "Done. (54.638s)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "Yx4x3WLBUEC_", "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "outputId": "21dfffdd-5486-4b62-e57d-cb7370c7ddbb" }, "source": [ "torch.__version__" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'1.9.0+cu102'" ] }, "metadata": { "tags": [] }, "execution_count": 6 } ] }, { "cell_type": "markdown", "metadata": { "id": "8rbXNp_IVXY3" }, "source": [ "# Playing With Custom Model" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sy54003VVWKn", "outputId": "b19b3db2-e31f-4e82-f31e-66772f16cb9b" }, "source": [ "model = torch.hub.load('../yolov5', 'custom', path='runs/train/exp/weights/best.pt', source='local') #loading best weights file what was acheived during training." ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\u001b[31m\u001b[1mrequirements:\u001b[0m PyYAML>=5.3.1 not found and is required by YOLOv5, attempting auto-update...\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "YOLOv5 🚀 v5.0-295-g9dd33fd torch 1.9.0+cu102 CUDA:0 (Tesla K80, 11441.1875MB)\n", "\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.7/dist-packages (5.4.1)\n", "\n", "\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per /content/yolov5/requirements.txt\n", "\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n", "\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "Fusing layers... \n", "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n", " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n", "Model Summary: 224 layers, 7053910 parameters, 0 gradients, 16.3 GFLOPs\n", "Adding AutoShape... \n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "ZwE-z3mmmb2-" }, "source": [ "# checkpoint_ = torch.load('runs/train/exp/weights/best.pt')['model']" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "yJ7hRErTlgZL" }, "source": [ "## Additional Information\n", "\n", "* This work has been done with `torch.__version__ == 1.9.0+cu102`\n", "\n", "* Reference Notebook: https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb" ] }, { "cell_type": "code", "metadata": { "id": "-ppq6-n-IO39" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }