327 lines
1.0 MiB
Plaintext
327 lines
1.0 MiB
Plaintext
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "ultralytics/YOLOv3",
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"version": "0.3.2",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"colab_type": "text",
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"id": "HvhYZrIZCEyo"
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},
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"source": [
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"<img src=\"https://storage.googleapis.com/ultralytics/logo/logoname1000.png\" width=\"150\">\n",
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"\n",
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"This notebook contains software developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information please visit https://github.com/ultralytics/yolov3 and https://www.ultralytics.com.\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "e5ylFIvlCEym",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"outputId": "79e2ed76-f1d0-45b3-b4a7-4ed561623cf9"
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},
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"source": [
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"import time\n",
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"import glob\n",
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"import torch\n",
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"import os\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"\n",
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"from IPython.display import Image \n",
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"from IPython.display import clear_output\n",
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"print('PyTorch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
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],
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"execution_count": 39,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"PyTorch 1.1.0 _CudaDeviceProperties(name='Tesla K80', major=3, minor=7, total_memory=11441MB, multi_processor_count=13)\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "7mGmQbAO5pQb",
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"colab_type": "text"
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},
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"source": [
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"Clone repository and download COCO 2014 dataset (20GB):"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "tIFv0p1TCEyj",
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"colab": {}
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},
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"source": [
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"!git clone https://github.com/ultralytics/yolov3 # clone\n",
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"!bash yolov3/data/get_coco_dataset_gdrive.sh # copy COCO2014 dataset (20GB)\n",
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"%cd yolov3"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "N3qM6T0W53gh",
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"colab_type": "text"
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},
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"source": [
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"Run `detect.py` to perform inference on images in `data/samples` folder:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "zR9ZbuQCH7FX",
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"colab_type": "code",
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"outputId": "49268b66-125d-425e-dbd0-17b108914c51",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 477
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}
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},
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"source": [
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"!python3 detect.py\n",
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"Image(filename='output/zidane.jpg', width=600)"
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],
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"execution_count": 0,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Namespace(cfg='cfg/yolov3-spp.cfg', conf_thres=0.5, data='data/coco.data', fourcc='mp4v', images='data/samples', img_size=416, nms_thres=0.5, output='output', weights='weights/yolov3-spp.weights')\n",
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"Using CUDA with Apex device0 _CudaDeviceProperties(name='Tesla K80', total_memory=11441MB)\n",
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"\n",
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"image 1/2 data/samples/bus.jpg: 416x320 3 persons, 1 buss, 1 handbags, Done. (0.119s)\n",
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"image 2/2 data/samples/zidane.jpg: 256x416 2 persons, 1 ties, Done. (0.085s)\n",
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"Results saved to /content/yolov3/output\n"
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],
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"name": "stdout"
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},
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{
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"output_type": "execute_result",
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"data": {
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"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYF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\naOL/AEXa21n/
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
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},
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"metadata": {
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"tags": [],
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"image/jpeg": {
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"width": 600
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}
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},
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"execution_count": 26
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ijTFlKcp6JVy",
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"colab_type": "text"
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},
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"source": [
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"Run `train.py` to train YOLOv3-SPP starting from a darknet53 backbone:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Mupsoa0lzSPo",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"!python3 train.py --data data/coco_64img.data --img-size 320 --epochs 3 --nosave"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "0eq1SMWl6Sfn",
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"colab_type": "text"
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},
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"source": [
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"Run `test.py` to evaluate the performance of a trained darknet or PyTorch model:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "0v0RFtO-WG9o",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"!python3 test.py --data data/coco.data --save-json --img-size 416 # 0.565 mAP"
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],
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"execution_count": 0,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "VUOiNLtMP5aG",
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"colab_type": "text"
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},
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"source": [
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"Reproduce tutorial training runs and overlays results:"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab_type": "code",
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"id": "LA9qqd_NCEyB",
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"outputId": "7d4c3c52-35e3-4fc0-8209-5d2a2159b442",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 417
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}
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},
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"source": [
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"!python3 train.py --nosave --batch-size 4 --accumulate 1 --data data/coco_16img.data && mv results.txt results0_16img.txt\n",
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"!python3 train.py --nosave --batch-size 4 --accumulate 1 --data data/coco_64img.data && mv results.txt results0_64img.txt\n",
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"!python3 -c \"from utils import utils; utils.plot_results()\" # plot training results\n",
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"Image(filename='results.png', width=800)"
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],
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"execution_count": 0,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAEGgAAAg0CAYAAAD7SfeBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxRmKUUREhGzdu
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"tags": [],
|
||
|
|
"image/png": {
|
||
|
|
"width": 800
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"execution_count": 38
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"id": "14mT7T7Q6erR",
|
||
|
|
"colab_type": "text"
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Extras below\n",
|
||
|
|
"\n",
|
||
|
|
"---\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"id": "9bVTcveIOzDd",
|
||
|
|
"colab_type": "code",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"%cd yolov3"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"colab_type": "code",
|
||
|
|
"id": "odMr0JFnCEyb",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"!ls"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"colab_type": "code",
|
||
|
|
"id": "uB3v5hj_CEyI",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Unit Tests\n",
|
||
|
|
"!python3 detect.py # detect 2 persons, 1 tie\n",
|
||
|
|
"!python3 test.py --data data/coco_32img.data # test mAP = 0.8\n",
|
||
|
|
"!python3 train.py --data data/coco_32img.data --epochs 3 --nosave # train 3 epochs"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"colab_type": "code",
|
||
|
|
"id": "6D0si0TNCEx5",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Evolve Hyperparameters\n",
|
||
|
|
"!python3 train.py --data data/coco.data --img-size 320 --epochs 1 --evolve"
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"metadata": {
|
||
|
|
"colab_type": "code",
|
||
|
|
"id": "JGI1Q-4UCExz",
|
||
|
|
"colab": {}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Plot Training Results\n",
|
||
|
|
"!python3 -c \"from utils import utils; utils.plot_results()\""
|
||
|
|
],
|
||
|
|
"execution_count": 0,
|
||
|
|
"outputs": []
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|