car-detection-bayes/examples.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ultralytics/YOLOv3",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HvhYZrIZCEyo"
      },
      "source": [
        "<img src=\"https://storage.googleapis.com/ultralytics/logo/logoname1000.png\" width=\"150\">\n",
        "\n",
        "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",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "e5ylFIvlCEym",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "79e2ed76-f1d0-45b3-b4a7-4ed561623cf9"
      },
      "source": [
        "import time\n",
        "import glob\n",
        "import torch\n",
        "import os\n",
        "\n",
        "from IPython.display import Image, clear_output\n",
        "print('PyTorch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PyTorch 1.1.0 _CudaDeviceProperties(name='Tesla K80', major=3, minor=7, total_memory=11441MB, multi_processor_count=13)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mGmQbAO5pQb",
        "colab_type": "text"
      },
      "source": [
        "Clone repository and download COCO 2014 dataset (20GB):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tIFv0p1TCEyj",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov3  # clone\n",
        "!bash yolov3/data/get_coco_dataset_gdrive.sh  # copy COCO2014 dataset (20GB)\n",
        "%cd yolov3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3qM6T0W53gh",
        "colab_type": "text"
      },
      "source": [
        "Run `detect.py` to perform inference on images in `data/samples` folder:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zR9ZbuQCH7FX",
        "colab_type": "code",
        "outputId": "49268b66-125d-425e-dbd0-17b108914c51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        }
      },
      "source": [
        "!python3 detect.py\n",
        "Image(filename='output/zidane.jpg', width=600)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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",
            "Using CUDA with Apex device0 _CudaDeviceProperties(name='Tesla K80', total_memory=11441MB)\n",
            "\n",
            "image 1/2 data/samples/bus.jpg: 416x320 3 persons, 1 buss, 1 handbags, Done. (0.119s)\n",
            "image 2/2 data/samples/zidane.jpg: 256x416 2 persons, 1 ties, Done. (0.085s)\n",
            "Results saved to /content/yolov3/output\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/jpeg": {
              "width": 600
            }
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijTFlKcp6JVy",
        "colab_type": "text"
      },
      "source": [
        "Run `train.py` to train YOLOv3-SPP starting from a darknet53 backbone:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mupsoa0lzSPo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!python3 train.py --data data/coco_64img.data --img-size 320 --epochs 3 --nosave"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0eq1SMWl6Sfn",
        "colab_type": "text"
      },
      "source": [
        "Run `test.py` to evaluate the performance of a trained darknet or PyTorch model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0v0RFtO-WG9o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!python3 test.py --data data/coco.data --save-json --img-size 416  # 0.565 mAP"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VUOiNLtMP5aG",
        "colab_type": "text"
      },
      "source": [
        "Reproduce tutorial training runs and overlays results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LA9qqd_NCEyB",
        "outputId": "82481ea3-00f4-4a01-9412-ca8d81eaa8b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "!python3 train.py --nosave --batch-size 4 --accumulate 1 --data data/coco_16img.data && mv results.txt results0_16img.txt\n",
        "!python3 train.py --nosave --batch-size 4 --accumulate 1 --data data/coco_64img.data && mv results.txt results0_64img.txt\n",
        "!python3 -c \"from utils import utils; utils.plot_results()\"  # plot training results\n",
        "Image(filename='results.png', width=800)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAACvAAAAV4CAYAAAB8IQgEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAewgAAHsIBbtB1PgAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl8TNf/P/BXdhEhe6RBQgStpEgs\niS1BfRSxVVBU7VtV6UZREksRWtRSFE0+XVTRRVW1PiqLxBprqFpjSyUiRMi+3N8f+eV+701muZNM\nJgmv5+Mxj8c9M+ece2YmnDP3vs85RoIgCCAiIiIiIiIiIiIiIiIiIiIiIiIiIiKDMK7qBhARERER\nERERERERERERERERERERET1PGMBLRERERERERERERERERERERERERERkQAzgJSIiIiIiIiIiIiIi\nIiIiIiIiIiIiMiAG8BIRERERERERERERERERERERERERERkQA3iJiIiIiIiIiIiIiIiIiIiIiIiI\niIgMiAG8REREREREREREREREREREREREREREBsQAXiIiIiIiIiIiIiIiIiIiIiIiIiIiIgNiAC8R\nEREREREREREREREREREREREREZEBMYCXiIiIiIiIiIiIiIiIiIiIiIiIiIjIgBjAS0RERERERERE\nREREREREREREREREZEAM4CUiIiIiIiIiIiIiIiIiIiIiIiIiIjIgBvASERERERERERERERERERER\nEREREREZEAN4iYiIiIiIiIiIiIiIiIiIiIiIiIiIDIgBvERERERERERERERERERERERERERERAbE\nAF4iIiIiIiIiIiIiIiIiIiIiIiIiIiIDYgAvERERERERERERERERERERERERERGRATGAl4iIiIiI\niIiIiIiIiIiIiIiIiIiIyIAYwEtERERERERERERERERERERERERERGRADOAlIiIiIiIiIiIiIiIi\nIiIiIiIiIiIyIAbwEhERERERERERERERERERERERERERGRADeImIiIiIiIiIiIiIiIiIiIiIiIiI\niAyIAbxEREREREREREREREREREREREREREQGxABeIiIiIiIiIiIiIiIiIiIiIiIiIiIiA2IALxER\nERERERERERERERERERERERERkQExgJeIiIiIiIiIiIiIiIiIiIiIiIiIiMiAGMBLRERERERERERE\nRERERERERERERERkQAzgJSIiIiIiIiIiIiIiIiIiIiIiIiIiMiAG8BIRERERERERERERERERERER\nERERERkQA3iJiIiIiIiIiIiIiIiIiIiIiIiIiIgMiAG8REREREREREREREREREREREREREREBsQA\nXiIiIiIiIiIiIiIiIiIiIiIiIiIiIgNiAC8REREREVWKqKgoGBkZiQ99k9YdFRWl9/rHjBkj1j9m\nzBi91/+8CgwMFD/X0NDQKmlDaGio2IbAwMAqaQMRERERERERERERERERPd8YwEv0HHvy5AkOHTqE\n7du3Y+3atfjkk0+wbt06fPPNNzh69CgyMzOruolERERERERERKQHKSkpCAkJgb+/P+zt7WFqaqpy\nQktERIT4vLu7u17bcPPmTdkkrJs3b+q1fiIiopqusicr11TlGZ88fvwYn376KQIDA+Hk5AQzMzOV\ndVT2BPSahGM1IiIiZd5++22xvwwODq7q5hBRDWda1Q0gIsPKzs7Gl19+iV27duH48eMoKChQm9fY\n2BitW7dGcHAwhg0bhiZNmmitX3pxY/To0YiIiNBHsxWLiopCt27dxHR4eLjOK+ZFRERg7NixYjoy\nMlKnldmWLl2KefPmAQDeeecdfP755wCACRMmYNu2bWK+AwcOoGfPnorrTUxMhLe3txhY3aFDB8TF\nxaGwsBA+Pj64ePEiAMDOzg5///03nJ2dFdcNFP9tvPzyy7h27RoAoH79+rh48SLs7Ox0qoeIiKrG\n66+/jh9++AEAULt2baSnp8PMzExruYyMDNjZ2aGwsFB87uDBg+jRo4ei8/r7++PYsWMAgAYNGuDO\nnTvlaH3lWrNmDdLT0wEAAwcOROvWrau4RepNmjQJW7ZsAQCsWrUK7777rvhaYGAgoqOjAQBubm7l\nuolS1WO150F6ejrWrFkjpmfOnAkbG5sqbBEREaWmpiI+Ph7379/HgwcPkJ+fD1tbWzg7O8PX1xcN\nGzas6iZWutjYWAwcOBBpaWlV3RQiIqJnEscb1culS5fQu3dv3Lp1q6qbQkREREREpBEDeImeI1u3\nbsWCBQtw7949RfmLiopw+vRpnD59Gh9//DGGDx+OkJAQeHp6VnJLa7a9e/eKx/369ROPP/vsM/zx\nxx9ISkoCAEycOBEXLlxAnTp1tNYpCALGjx8vBu9aWFggPDwcJiYmMDExQUREBPz8/FBYWIiHDx9i\nypQp+Pnnn3Vq97x588TgXQDYtGkTg3eJiGqQgIAAMYA3KysLJ0+eRMeOHbWWi42NlQXvAkBMTIyi\nAN7MzEzEx8fL2lAdrVmzRrxh4+7uXm0DeAVBwG+//SampeMIqjnS09OxcOFCMT1mzBgG8BIRVYEn\nT55g3bp1+PHHH3HmzBkIgqA2r6urK4YPH44xY8agZcuWBmylYWRkZGDw4MGy4N06derA0dERxsbF\nG7S5urpWVfOIiIhqLI43qqeioiIEBwfLgnctLS3h7OwMExMTAMWT0J9lUVFR4grO7u7uOi90Q0RE\npMTNmzfRuHHjSqk7JCQEoaGhlVL38+rTTz/Fhx9+KKYTEhLg5eVVhS0iohIM4CV6DuTn52PatGni\nam4lzM3N4e/vDz8/Pzg5OcHW1hbp6elITk5GQkICIiMjkZOTA6D4gsd3332HnJwc7N69uyreRo1w\n//59nDhxAgBQt25dWSBTvXr18OWXX6Jv374AgFu3buHDDz/Exo0btda7ceNGREZGiunQ0FC8+OKL\nYrpt27aYPXs2li5dCgD45ZdfsH37dowYMUJRu48ePSquFAwAI0eOxIABAxSVJSKi6qF08Gx0dLSi\nAN6SFV21PafKkSNHZKv5l16xPjAwUOPNK5I7deqUONGqRYsWaNq0aRW3qHJwC1AiIqpsGzZsQGho\nKB48eKAof1JSEj799FN89tlnGDlyJJYuXfpMrZL3zTff4P79+wCKg1d27NiBfv36PfdbRBMREVUE\nxxvV1/79+/H3338DKN6J6Msvv8SYMWNgavr83BaPiooSJxcHBAQwgJeIiIiIqBp7fn6pED2nBEHA\nsGHDZKux2tjY4P3338eMGTNgbW2ttmxWVhZ+++03fPLJJzh//rwhmlvj7du3D0VFRQCAXr16ldm6\nvE+fPnjzzTfx9ddfAwA2b96MoUOHolu3bmrrvHnzJmbPni2m27ZtK5sZVSIkJAS//vorLly4AACY\nPn06evToAWdnZ41tzsnJwdixY8V2169fH2vXrlXwbomIqDp56aWX4OTkJAZnREdHY86cOVrLSYN1\nrayskJmZiePHjyM3NxcWFhaKywLVdwXemkLdKv5ERESkTH5+PiZPnozw8HDZ81ZWVggMDISvry8c\nHR1haWmJ5ORk3L59GwcOHMDNmzcBFF9D+fbbb2Fvb481a9ZUwTuoHIcOHRKPR40ahf79+2vMP2bM\nGAZ5EBERqcHxRtXQZXwiHfv07NkTEyZM0JifE9D/j7u7Oz8LIiJSzMzMDB4eHlrz3b9/H0+ePBHT\nSspU952C169fj/Xr11d1M4joGcEAXqJn3KeffioL3m3WrBn++OMPRVsZ1K5dG0OHDsWQIUPwww8/\nYNq0aZXZ1GeCNPAmKChIZZ41a9bgwIEDSE5OhiAIGD9+PBISEmBlZVUmb8nrT58+BVC8anJ4eLi4\nzZOUubk5IiIi4Ofnh4KCAjx8+BBTpkyRff+qLFiwAJcvXxbTmzdvrvYDYiIiUq1r167iSvlxcXEo\nKCjQuLpIZmYmT
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 800
            }
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14mT7T7Q6erR",
        "colab_type": "text"
      },
      "source": [
        "Extras below\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42_zEpW6W_N1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git pull"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "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": []
    }
  ]
}