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Glenn Jocher 2020-07-11 15:25:52 -07:00
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tutorial.ipynb
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@ -74,7 +74,7 @@
"clear_output()\n",
"print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
],
"execution_count": 1,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -212,7 +212,7 @@
"gdrive_download('1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43','coco2017val.zip') # val2017 dataset\n",
"!mv ./coco ../ # move folder alongside /yolov5"
],
"execution_count": 10,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -238,7 +238,7 @@
"# Run YOLOv5x on COCO val2017\n",
"!python test.py --weights yolov5x.pt --data coco.yaml --img 672"
],
"execution_count": 15,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -352,7 +352,7 @@
"gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip') # coco128 dataset\n",
"!mv ./coco128 ../ # move folder alongside /yolov5"
],
"execution_count": 16,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -405,7 +405,7 @@
"# Train YOLOv5s on coco128 for 3 epochs\n",
"!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --cfg yolov5s.yaml --weights yolov5s.pt --nosave --cache"
],
"execution_count": 24,
"execution_count": null,
"outputs": [
{
"output_type": "stream",
@ -622,7 +622,7 @@
"colab_type": "text"
},
"source": [
"Training losses and performance metrics are saved to Tensorboard and also to a `runs/exp0/results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.utils import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 300 epochs, starting from scratch (orange), and from pretrained `yolov5s.pt` (blue)."
"Training losses and performance metrics are saved to Tensorboard and also to a `runs/exp0/results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.utils import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 300 epochs, starting from scratch (blue), and from pretrained `yolov5s.pt` (orange)."
]
},
{
@ -639,7 +639,7 @@
"source": [
"from utils.utils import plot_results; plot_results() # plot results.txt files as results.png"
],
"execution_count": 29,
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
@ -701,7 +701,7 @@
"!rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n",
"%cd yolov5"
],
"execution_count": 9,
"execution_count": null,
"outputs": []
},
{