Created using Colaboratory
parent
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@ -65,7 +65,7 @@
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"import utils\n",
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"display = utils.notebook_init() # checks"
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],
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"execution_count": 1,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"\n",
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"```shell\n",
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"python detect.py --source 0 # webcam\n",
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" img.jpg # image \n",
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" img.jpg # image\n",
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" vid.mp4 # video\n",
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" screen # screenshot\n",
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" path/ # directory\n",
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"!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n",
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"# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)"
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],
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"execution_count": 13,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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@ -174,7 +174,7 @@
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"torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
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"!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
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],
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"execution_count": 3,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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@ -198,7 +198,7 @@
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"# Validate YOLOv5s on COCO val\n",
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"!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
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],
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"execution_count": 4,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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@ -308,7 +308,7 @@
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"# Train YOLOv5s on COCO128 for 3 epochs\n",
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"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache"
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],
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"execution_count": 5,
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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@ -539,7 +539,7 @@
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"\n",
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"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",
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"\n",
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"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",
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"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",
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"\n",
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"<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
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]
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@ -593,7 +593,7 @@
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"# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
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"import torch\n",
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"\n",
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"model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) # yolov5n - yolov5x6 or custom\n",
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"model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True) # or yolov5n - yolov5x6 or custom\n",
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"im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
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"results = model(im) # inference\n",
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"results.print() # or .show(), .save(), .crop(), .pandas(), etc."
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@ -602,4 +602,4 @@
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"outputs": []
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}
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]
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}
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}
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