Sneakers_YOLOv4_ResNet101V2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sneakers YOLOv4-Darknet.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jufYMny2kbo",
        "outputId": "df8ac8fb-9e88-4d6e-9e1d-88e7f25a4bbc"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDTvGt2zt7cm"
      },
      "source": [
        "# Configuring cuDNN on Colab for YOLOv4\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-bguKWgtxSx",
        "outputId": "126d19f0-0da0-43fc-fbd6-4f5865bb5c86"
      },
      "source": [
        "# CUDA: Let's check that Nvidia CUDA drivers are already pre-installed and which version is it.\n",
        "!/usr/local/cuda/bin/nvcc --version\n",
        "# We need to install the correct cuDNN according to this output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
            "Built on Mon_Oct_12_20:09:46_PDT_2020\n",
            "Cuda compilation tools, release 11.1, V11.1.105\n",
            "Build cuda_11.1.TC455_06.29190527_0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJYM7-_Had0Q",
        "outputId": "0dec9138-a187-4717-cb56-0bd796f57d55"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fri Dec 10 08:57:33 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 495.44       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 P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    26W / 250W |      0MiB / 16280MiB |      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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_qFPIBgvlkn",
        "outputId": "36965cc0-0a1c-48a9-e1b2-b94560c21fd8"
      },
      "source": [
        "# This cell ensures you have the correct architecture for your respective GPU\n",
        "# If you command is not found, look through these GPUs, find the respective\n",
        "# GPU and add them to the archTypes dictionary\n",
        "\n",
        "# Tesla V100\n",
        "# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]\n",
        "\n",
        "# Tesla K80 \n",
        "# ARCH= -gencode arch=compute_37,code=sm_37\n",
        "\n",
        "# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores\n",
        "# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75]\n",
        "\n",
        "# Jetson XAVIER\n",
        "# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72]\n",
        "\n",
        "# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4\n",
        "# ARCH= -gencode arch=compute_61,code=sm_61\n",
        "\n",
        "# GP100/Tesla P100 - DGX-1\n",
        "# ARCH= -gencode arch=compute_60,code=sm_60\n",
        "\n",
        "# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment:\n",
        "# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53]\n",
        "\n",
        "# For Jetson Tx2 or Drive-PX2 uncomment:\n",
        "# ARCH= -gencode arch=compute_62,code=[sm_62,compute_62]\n",
        "import os\n",
        "os.environ['GPU_TYPE'] = str(os.popen('nvidia-smi --query-gpu=name --format=csv,noheader').read())\n",
        "\n",
        "def getGPUArch(argument):\n",
        "  try:\n",
        "    argument = argument.strip()\n",
        "    # All Colab GPUs\n",
        "    archTypes = {\n",
        "        \"Tesla V100-SXM2-16GB\": \"-gencode arch=compute_70,code=[sm_70,compute_70]\",\n",
        "        \"Tesla K80\": \"-gencode arch=compute_37,code=sm_37\",\n",
        "        \"Tesla T4\": \"-gencode arch=compute_75,code=[sm_75,compute_75]\",\n",
        "        \"Tesla P40\": \"-gencode arch=compute_61,code=sm_61\",\n",
        "        \"Tesla P4\": \"-gencode arch=compute_61,code=sm_61\",\n",
        "        \"Tesla P100-PCIE-16GB\": \"-gencode arch=compute_60,code=sm_60\"\n",
        "\n",
        "      }\n",
        "    return archTypes[argument]\n",
        "  except KeyError:\n",
        "    return \"GPU must be added to GPU Commands\"\n",
        "os.environ['ARCH_VALUE'] = getGPUArch(os.environ['GPU_TYPE'])\n",
        "\n",
        "print(\"GPU Type: \" + os.environ['GPU_TYPE'])\n",
        "print(\"ARCH Value: \" + os.environ['ARCH_VALUE'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU Type: Tesla P100-PCIE-16GB\n",
            "\n",
            "ARCH Value: -gencode arch=compute_60,code=sm_60\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16pvdFMa1FEe"
      },
      "source": [
        "# Step 2: Installing Darknet for YOLOv4 on Colab\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K9uY-38P93oz",
        "outputId": "28888b86-fc65-48d2-bf45-fa8884d93435"
      },
      "source": [
        "%cd /content\n",
        "%rm -rf darknet"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQEktcfj9y9O",
        "outputId": "f05f6c78-1385-4779-94a8-8ee5331e731c"
      },
      "source": [
        "#we clone the fork of darknet maintained by roboflow\n",
        "#small changes have been made to configure darknet for training\n",
        "!git clone https://github.com/roboflow-ai/darknet.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'darknet'...\n",
            "remote: Enumerating objects: 13289, done.\u001b[K\n",
            "remote: Total 13289 (delta 0), reused 0 (delta 0), pack-reused 13289\u001b[K\n",
            "Receiving objects: 100% (13289/13289), 12.16 MiB | 23.81 MiB/s, done.\n",
            "Resolving deltas: 100% (9047/9047), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9O6dTiq5ga0I",
        "outputId": "2a003d89-06ab-44ac-c9fe-1141bd8d655d"
      },
      "source": [
        "%cd /content/darknet/\n",
        "%rm Makefile"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyTAyEhOgd__",
        "outputId": "dc1954e1-ce6f-433b-ef3c-f37d854e8310"
      },
      "source": [
        "#colab occasionally shifts dependencies around, at the time of authorship, this Makefile works for building Darknet on Colab\n",
        "\n",
        "%%writefile Makefile\n",
        "GPU=1\n",
        "CUDNN=1\n",
        "CUDNN_HALF=0\n",
        "OPENCV=1\n",
        "AVX=0\n",
        "OPENMP=0\n",
        "LIBSO=1\n",
        "ZED_CAMERA=0\n",
        "ZED_CAMERA_v2_8=0\n",
        "\n",
        "# set GPU=1 and CUDNN=1 to speedup on GPU\n",
        "# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher\n",
        "# set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0)\n",
        "# set ZED_CAMERA=1 to enable ZED SDK 3.0 and above\n",
        "# set ZED_CAMERA_v2_8=1 to enable ZED SDK 2.X\n",
        "\n",
        "USE_CPP=0\n",
        "DEBUG=0\n",
        "\n",
        "ARCH= -gencode arch=compute_35,code=sm_35 \\\n",
        "      -gencode arch=compute_50,code=[sm_50,compute_50] \\\n",
        "      -gencode arch=compute_52,code=[sm_52,compute_52] \\\n",
        "\t    -gencode arch=compute_61,code=[sm_61,compute_61] \\\n",
        "      -gencode arch=compute_37,code=sm_37\n",
        "\n",
        "ARCH= -gencode arch=compute_60,code=sm_60\n",
        "\n",
        "OS := $(shell uname)\n",
        "\n",
        "VPATH=./src/\n",
        "EXEC=darknet\n",
        "OBJDIR=./obj/\n",
        "\n",
        "ifeq ($(LIBSO), 1)\n",
        "LIBNAMESO=libdarknet.so\n",
        "APPNAMESO=uselib\n",
        "endif\n",
        "\n",
        "ifeq ($(USE_CPP), 1)\n",
        "CC=g++\n",
        "else\n",
        "CC=gcc\n",
        "endif\n",
        "\n",
        "CPP=g++ -std=c++11\n",
        "NVCC=nvcc\n",
        "OPTS=-Ofast\n",
        "LDFLAGS= -lm -pthread\n",
        "COMMON= -Iinclude/ -I3rdparty/stb/include\n",
        "CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC\n",
        "\n",
        "ifeq ($(DEBUG), 1)\n",
        "#OPTS= -O0 -g\n",
        "#OPTS= -Og -g\n",
        "COMMON+= -DDEBUG\n",
        "CFLAGS+= -DDEBUG\n",
        "else\n",
        "ifeq ($(AVX), 1)\n",
        "CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a\n",
        "endif\n",
        "endif\n",
        "\n",
        "CFLAGS+=$(OPTS)\n",
        "\n",
        "ifneq (,$(findstring MSYS_NT,$(OS)))\n",
        "LDFLAGS+=-lws2_32\n",
        "endif\n",
        "\n",
        "ifeq ($(OPENCV), 1)\n",
        "COMMON+= -DOPENCV\n",
        "CFLAGS+= -DOPENCV\n",
        "LDFLAGS+= `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv`\n",
        "COMMON+= `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv`\n",
        "endif\n",
        "\n",
        "ifeq ($(OPENMP), 1)\n",
        "CFLAGS+= -fopenmp\n",
        "LDFLAGS+= -lgomp\n",
        "endif\n",
        "\n",
        "ifeq ($(GPU), 1)\n",
        "COMMON+= -DGPU -I/usr/local/cuda/include/\n",
        "CFLAGS+= -DGPU\n",
        "ifeq ($(OS),Darwin) #MAC\n",
        "LDFLAGS+= -L/usr/local/cuda/lib -lcuda -lcudart -lcublas -lcurand\n",
        "else\n",
        "LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand\n",
        "endif\n",
        "endif\n",
        "\n",
        "ifeq ($(CUDNN), 1)\n",
        "COMMON+= -DCUDNN\n",
        "ifeq ($(OS),Darwin) #MAC\n",
        "CFLAGS+= -DCUDNN -I/usr/local/cuda/include\n",
        "LDFLAGS+= -L/usr/local/cuda/lib -lcudnn\n",
        "else\n",
        "CFLAGS+= -DCUDNN -I/usr/local/cudnn/include\n",
        "LDFLAGS+= -L/usr/local/cudnn/lib64 -lcudnn\n",
        "endif\n",
        "endif\n",
        "\n",
        "ifeq ($(CUDNN_HALF), 1)\n",
        "COMMON+= -DCUDNN_HALF\n",
        "CFLAGS+= -DCUDNN_HALF\n",
        "ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70]\n",
        "endif\n",
        "\n",
        "ifeq ($(ZED_CAMERA), 1)\n",
        "CFLAGS+= -DZED_STEREO -I/usr/local/zed/include\n",
        "ifeq ($(ZED_CAMERA_v2_8), 1)\n",
        "LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed\n",
        "#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0\n",
        "else\n",
        "LDFLAGS+= -L/usr/local/zed/lib -lsl_zed\n",
        "#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0\n",
        "endif\n",
        "endif\n",
        "\n",
        "OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o\n",
        "ifeq ($(GPU), 1)\n",
        "LDFLAGS+= -lstdc++\n",
        "OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o\n",
        "endif\n",
        "\n",
        "OBJS = $(addprefix $(OBJDIR), $(OBJ))\n",
        "DEPS = $(wildcard src/*.h) Makefile include/darknet.h\n",
        "\n",
        "all: $(OBJDIR) backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO)\n",
        "\n",
        "ifeq ($(LIBSO), 1)\n",
        "CFLAGS+= -fPIC\n",
        "\n",
        "$(LIBNAMESO): $(OBJDIR) $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp\n",
        "\t$(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS)\n",
        "\n",
        "$(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp\n",
        "\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO)\n",
        "endif\n",
        "\n",
        "$(EXEC): $(OBJS)\n",
        "\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS)\n",
        "\n",
        "$(OBJDIR)%.o: %.c $(DEPS)\n",
        "\t$(CC) $(COMMON) $(CFLAGS) -c $< -o $@\n",
        "\n",
        "$(OBJDIR)%.o: %.cpp $(DEPS)\n",
        "\t$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -c $< -o $@\n",
        "\n",
        "$(OBJDIR)%.o: %.cu $(DEPS)\n",
        "\t$(NVCC) $(ARCH) $(COMMON) --compiler-options \"$(CFLAGS)\" -c $< -o $@\n",
        "\n",
        "$(OBJDIR):\n",
        "\tmkdir -p $(OBJDIR)\n",
        "backup:\n",
        "\tmkdir -p backup\n",
        "results:\n",
        "\tmkdir -p results\n",
        "setchmod:\n",
        "\tchmod +x *.sh\n",
        "\n",
        ".PHONY: clean\n",
        "\n",
        "clean:\n",
        "\trm -rf $(OBJS) $(EXEC) $(LIBNAMESO) $(APPNAMESO)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing Makefile\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyMBDkaL-Aep"
      },
      "source": [
        "#install environment from the Makefile\n",
        "#note if you are on Colab Pro this works on a P100 GPU\n",
        "#if you are on Colab free, you may need to change the Makefile for the K80 GPU\n",
        "#this goes for any GPU, you need to change the Makefile to inform darknet which GPU you are running on.\n",
        "#note the Makefile above should work for you, if you need to tweak, try the below\n",
        "%cd /content/darknet\n",
        "!sed -i 's/OPENCV=0/OPENCV=1/g' Makefile\n",
        "!sed -i 's/GPU=0/GPU=1/g' Makefile\n",
        "!sed -i 's/CUDNN=0/CUDNN=1/g' Makefile\n",
        "!sed -i \"s/ARCH= -gencode arch=compute_60,code=sm_60/ARCH= ${ARCH_VALUE}/g\" Makefile\n",
        "!make"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGPDEjfAALrQ",
        "outputId": "8ec76a1f-b6bb-4aed-c4da-ca834f1589d3"
      },
      "source": [
        "#download the newly released yolov4 ConvNet weights\n",
        "%cd /content/darknet\n",
        "!wget https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n",
            "--2021-12-10 08:59:24--  https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.conv.137\n",
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            "Saving to: ‘yolov4.conv.137’\n",
            "\n",
            "yolov4.conv.137     100%[===================>] 162.16M  57.2MB/s    in 2.8s    \n",
            "\n",
            "2021-12-10 08:59:27 (57.2 MB/s) - ‘yolov4.conv.137’ saved [170038676/170038676]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kWOiKj37l4wW"
      },
      "source": [
        "# Set up Custom Dataset for YOLOv4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KiQvussOcXdJ"
      },
      "source": [
        "#follow the link below to get your download code from from Roboflow\n",
        "!pip install -q roboflow\n",
        "# from roboflow import Roboflow\n",
        "# rf = Roboflow(model_format=\"darknet\", notebook=\"roboflow-yolov4\")\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"hRTCFzw2Ax720OnbMBHC\")\n",
        "project = rf.workspace().project(\"sneakers-k7svc\")\n",
        "dataset = project.version(2).download(\"darknet\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "1ok3Snnk6cnm",
        "outputId": "1d2001be-b57e-4245-d135-616659683870"
      },
      "source": [
        "dataset.location"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/darknet/Sneakers-2'"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueSd5oLs8Dan"
      },
      "source": [
        "dataset.location = '/content/drive/MyDrive/darknet/Sneakers-2'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KiCILEbs1NII",
        "outputId": "0ee4565b-21a3-4712-c3cb-eb501d5da380"
      },
      "source": [
        "#Set up training file directories for custom dataset\n",
        "%cd /content/darknet/\n",
        "%cp {dataset.location}/train/_darknet.labels data/obj.names\n",
        "%mkdir data/obj\n",
        "#copy image and labels\n",
        "%cp {dataset.location}/train/*.jpg data/obj/\n",
        "%cp {dataset.location}/valid/*.jpg data/obj/\n",
        "\n",
        "%cp {dataset.location}/train/*.txt data/obj/\n",
        "%cp {dataset.location}/valid/*.txt data/obj/\n",
        "\n",
        "with open('data/obj.data', 'w') as out:\n",
        "  out.write('classes = 1\\n')\n",
        "  out.write('train = data/train.txt\\n')\n",
        "  out.write('valid = data/valid.txt\\n')\n",
        "  out.write('names = data/obj.names\\n')\n",
        "  out.write('backup = backup/')\n",
        "\n",
        "#write train file (just the image list)\n",
        "import os\n",
        "\n",
        "with open('data/train.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir(dataset.location + '/train') if f.endswith('jpg')]:\n",
        "    out.write('data/obj/' + img + '\\n')\n",
        "\n",
        "#write the valid file (just the image list)\n",
        "import os\n",
        "\n",
        "with open('data/valid.txt', 'w') as out:\n",
        "  for img in [f for f in os.listdir(dataset.location + '/valid') if f.endswith('jpg')]:\n",
        "    out.write('data/obj/' + img + '\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/darknet\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HtRqO3QvjkP"
      },
      "source": [
        "# Write Custom Training Config for YOLOv4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U_WJcqHhpeVr",
        "outputId": "7d2dfc48-e9ea-43f7-a3ca-c4fa0cd2632e"
      },
      "source": [
        "#we build config dynamically based on number of classes\n",
        "#we build iteratively from base config files. This is the same file shape as cfg/yolo-obj.cfg\n",
        "def file_len(fname):\n",
        "  with open(fname) as f:\n",
        "    for i, l in enumerate(f):\n",
        "      pass\n",
        "  return i + 1\n",
        "\n",
        "num_classes = file_len(dataset.location + '/train/_darknet.labels')\n",
        "print(\"writing config for a custom YOLOv4 detector detecting number of classes: \" + str(num_classes))\n",
        "\n",
        "#Instructions from the darknet repo\n",
        "#change line max_batches to (classes*2000 but not less than number of training images, and not less than 6000), f.e. max_batches=6000 if you train for 3 classes\n",
        "#change line steps to 80% and 90% of max_batches, f.e. steps=4800,5400\n",
        "if os.path.exists('./cfg/custom-yolov4-detector.cfg'): os.remove('./cfg/custom-yolov4-detector.cfg')\n",
        "\n",
        "\n",
        "with open('./cfg/custom-yolov4-detector.cfg', 'a') as f:\n",
        "  f.write('[net]' + '\\n')\n",
        "  f.write('batch=64' + '\\n')\n",
        "  #####smaller subdivisions help the GPU run faster. 12 is optimal, but you might need to change to 24,36,64####\n",
        "  f.write('subdivisions=24' + '\\n')\n",
        "  f.write('width=416' + '\\n')\n",
        "  f.write('height=416' + '\\n')\n",
        "  f.write('channels=3' + '\\n')\n",
        "  f.write('momentum=0.949' + '\\n')\n",
        "  f.write('decay=0.0005' + '\\n')\n",
        "  f.write('angle=0' + '\\n')\n",
        "  f.write('saturation = 0' + '\\n')\n",
        "  f.write('exposure = 0' + '\\n')\n",
        "  f.write('hue = 0' + '\\n')\n",
        "  f.write('\\n')\n",
        "  f.write('learning_rate=0.0005' + '\\n')\n",
        "  f.write('burn_in=1000' + '\\n')\n",
        "  ######you can adjust up and down to change training time#####\n",
        "  ##Darknet does iterations with batches, not epochs####\n",
        "  # max_batches = num_classes*2000\n",
        "  max_batches = 5500\n",
        "  f.write('max_batches=' + str(max_batches) + '\\n')\n",
        "  f.write('policy=steps' + '\\n')\n",
        "  steps1 = .8 * max_batches\n",
        "  steps2 = .9 * max_batches\n",
        "  f.write('steps='+str(steps1)+','+str(steps2) + '\\n')\n",
        "\n",
        "#Instructions from the darknet repo\n",
        "#change line classes=80 to your number of objects in each of 3 [yolo]-layers:\n",
        "#change [filters=255] to filters=(classes + 5)x3 in the 3 [convolutional] before each [yolo] layer, keep in mind that it only has to be the last [convolutional] before each of the [yolo] layers.\n",
        "\n",
        "  with open('cfg/yolov4-custom2.cfg', 'r') as f2:\n",
        "    content = f2.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)    \n",
        "    num_filters = (num_classes + 5) * 3\n",
        "    f.write('filters='+str(num_filters) + '\\n')\n",
        "    f.write('activation=linear')\n",
        "    f.write('\\n')\n",
        "    f.write('\\n')\n",
        "    f.write('[yolo]' + '\\n')\n",
        "    f.write('mask = 0,1,2' + '\\n')\n",
        "    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n",
        "    f.write('classes=' + str(num_classes) + '\\n')\n",
        "\n",
        "  with open('cfg/yolov4-custom3.cfg', 'r') as f3:\n",
        "    content = f3.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)    \n",
        "    num_filters = (num_classes + 5) * 3\n",
        "    f.write('filters='+str(num_filters) + '\\n')\n",
        "    f.write('activation=linear')\n",
        "    f.write('\\n')\n",
        "    f.write('\\n')\n",
        "    f.write('[yolo]' + '\\n')\n",
        "    f.write('mask = 3,4,5' + '\\n')\n",
        "    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n",
        "    f.write('classes=' + str(num_classes) + '\\n')\n",
        "\n",
        "  with open('cfg/yolov4-custom4.cfg', 'r') as f4:\n",
        "    content = f4.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)    \n",
        "    num_filters = (num_classes + 5) * 3\n",
        "    f.write('filters='+str(num_filters) + '\\n')\n",
        "    f.write('activation=linear')\n",
        "    f.write('\\n')\n",
        "    f.write('\\n')\n",
        "    f.write('[yolo]' + '\\n')\n",
        "    f.write('mask = 6,7,8' + '\\n')\n",
        "    f.write('anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401' + '\\n')\n",
        "    f.write('classes=' + str(num_classes) + '\\n')\n",
        "    \n",
        "  with open('cfg/yolov4-custom5.cfg', 'r') as f5:\n",
        "    content = f5.readlines()\n",
        "    for line in content:\n",
        "      f.write(line)\n",
        "\n",
        "print(\"file is written!\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "writing config for a custom YOLOv4 detector detecting number of classes: 1\n",
            "file is written!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2LAciMh4Cut"
      },
      "source": [
        "#here is the file that was just written. \n",
        "#you may consider adjusting certain things\n",
        "\n",
        "#like the number of subdivisions 64 runs faster but Colab GPU may not be big enough\n",
        "#if Colab GPU memory is too small, you will need to adjust subdivisions to 16\n",
        "%cat cfg/custom-yolov4-detector.cfg"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vWrG9EGamSpH"
      },
      "source": [
        "# Train Custom YOLOv4 Detector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6miYFbvExqMd"
      },
      "source": [
        "!./darknet detector train data/obj.data cfg/custom-yolov4-detector.cfg backup/custom-yolov4-detector_final.weights -dont_show -map\n",
        "#If you get CUDA out of memory adjust subdivisions above!\n",
        "#adjust max batches down for shorter training above"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBnwpBV5ZXxQ"
      },
      "source": [
        "# Infer Custom Objects with Saved YOLOv4 Weights"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzoJQQw8Zdco"
      },
      "source": [
        "#define utility function\n",
        "def imShow(path):\n",
        "  import cv2\n",
        "  import matplotlib.pyplot as plt\n",
        "  %matplotlib inline\n",
        "\n",
        "  image = cv2.imread(path)\n",
        "  height, width = image.shape[:2]\n",
        "  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)\n",
        "\n",
        "  fig = plt.gcf()\n",
        "  fig.set_size_inches(18, 10)\n",
        "  plt.axis(\"off\")\n",
        "  #plt.rcParams['figure.figsize'] = [10, 5]\n",
        "  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3dJB6NZv4kh"
      },
      "source": [
        "#check if weigths have saved yet\n",
        "#backup houses the last weights for our detector\n",
        "#(file yolo-obj_last.weights will be saved to the build\\darknet\\x64\\backup\\ for each 100 iterations)\n",
        "#(file yolo-obj_xxxx.weights will be saved to the build\\darknet\\x64\\backup\\ for each 1000 iterations)\n",
        "#After training is complete - get result yolo-obj_final.weights from path build\\darknet\\x64\\bac\n",
        "!ls backup\n",
        "#if it is empty you haven't trained for long enough yet, you need to train for at least 100 iterations"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-_E3O5Mf4Mf"
      },
      "source": [
        "#coco.names is hardcoded somewhere in the detector\n",
        "%cp data/obj.names data/coco.names"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os"
      ],
      "metadata": {
        "id": "617Hf4HRhbzy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KgFcWVdpHix2"
      },
      "source": [
        "# задаем пути к папкам с моделями\n",
        "\n",
        "#path_to_all_models = '/content/drive/MyDrive/ebay_sneakers/OG_photos'\n",
        "path_to_two_models ='/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra'\n",
        "\n",
        "# models_list =  os.listdir(path_to_all_models)\n",
        "two_path = [x[0] for x in os.walk(path_to_two_models)]\n",
        "# all_path = [x[0] for x in os.walk(path_to_all_models)]\n",
        "\n",
        "# unknown_class = [os.path.join(all_path[0], i) for i in os.listdir(all_path[0])]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "two_path"
      ],
      "metadata": {
        "id": "rTIFlDdkay4_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8538d12b-578f-4cc4-910c-b38d227cdd08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra',\n",
              " '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Adidas_Ultra_Boost_4.0',\n",
              " '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CskIhZXsHZnW"
      },
      "source": [
        "# unknown_class = [os.path.join(all_path[0], i) for i in os.listdir(all_path[0])]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9wl0EYkHZEN"
      },
      "source": [
        "# Абсолютный путь для каждой фотки в папках\n",
        "\n",
        "two_abs_paths = [os.path.join(path, i) for path in two_path[1:] for i in os.listdir(path)]\n",
        "# # for path in all_path:\n",
        "#     for i in os.listdir(path):\n",
        "#         os.path.abspath(i)\n",
        "\n",
        "with open('/content/drive/MyDrive/ebay_sneakers/OG_photos/project_two_path_to_images.txt', 'w') as w_f:\n",
        "    for path in two_abs_paths:\n",
        "        w_f.write(f'{path}\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1ikZhIgwCui"
      },
      "source": [
        "#Model Ebay photos testing\n",
        "#/test has images that we can test our detector on\n",
        "#test_images = [f for f in os.listdir('test') if f.endswith('.jpg')]\n",
        "#import random\n",
        "\n",
        "images = \"/content/drive/MyDrive/ebay_sneakers/OG_photos/project_two_path_to_images.txt\"\n",
        "\n",
        "#test out our detector!\n",
        "!./darknet detector test data/obj.data /content/drive/MyDrive/sneaker_weights/custom-yolov4-detector.cfg /content/drive/MyDrive/sneaker_weights/custom-yolov4-detector_best-2.weights -thresh 0.6 -dont_show -ext_output < {images} > /content/drive/MyDrive/ebay_sneakers/OG_photos/project_two_result.txt\n",
        "# imShow('/content/darknet/predictions.jpg')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jp0ERKDV3SZW"
      },
      "source": [
        "**For Test**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iW2n5HEh3aa_"
      },
      "source": [
        "# path_to_all_models = '/content/drive/MyDrive/ebay_sneakers/OG_photos'\n",
        "path_to_example_models = '/content/drive/MyDrive/ebay_sneakers/OG_photos/exmaple'\n",
        "# models_list =  os.listdir(path_to_all_models)\n",
        "# all_pathes = [os.listdir(os.path.abspath(i)) for i in models_list]\n",
        "example_path = [x[0] for x in os.walk(path_to_example_models)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2lea-7Q3abA"
      },
      "source": [
        "example_class = [os.path.join(example_path[0], i) for i in os.listdir(example_path[0])]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kV2RCa4b4GVB"
      },
      "source": [
        "with open('/content/drive/MyDrive/ebay_sneakers/path_to_example_images.txt', 'w') as w_f:\n",
        "    for path in unknown_class:\n",
        "        w_f.write(f'{path}\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5pYAphE73abA",
        "outputId": "29e8da87-33e2-41b8-b05c-34494240b04c"
      },
      "source": [
        "# predict for tests\n",
        "# img_path = \"/content/drive/MyDrive/тесты/dima.jpeg\"\n",
        "images = \"/content/drive/MyDrive/ebay_sneakers/example/ex_res.txt\"\n",
        "\n",
        "#test out our detector!\n",
        "!./darknet detector test data/obj.data /content/drive/MyDrive/sneaker_weights/custom-yolov4-detector.cfg /content/drive/MyDrive/sneaker_weights/custom-yolov4-detector_best-2.weights -thresh 0.6 -dont_show -ext_output < {images} > /content/drive/MyDrive/ebay_sneakers/example/example_result.txt\n",
        "# imShow('/content/darknet/predictions.jpg')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: ./darknet: No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtSmJZk-3U2g"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsOixB2q3Uac"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vZQtYu1dA-s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "115735d7-8202-4720-9fc1-65c7d131f3b5"
      },
      "source": [
        "from PIL import Image\n",
        "import itertools\n",
        "import random\n",
        "import os\n",
        "\n",
        "\n",
        "path_cropped_images = '/content/drive/MyDrive/ebay_sneakers/project/'\n",
        "res = []\n",
        "result_dict = {}\n",
        "line_id_img = []\n",
        "\n",
        "\n",
        "def crop_image(input_image, coords, cropped_image):\n",
        "    x, y, w, h = coords\n",
        "    croped_shoe = Image.open(input_image)\n",
        "    croped_shoe = croped_shoe.crop((x, y, x + w, y + h)).resize((224,224)).save(cropped_image)\n",
        "\n",
        "\n",
        "with open('/content/drive/MyDrive/ebay_sneakers/OG_photos/project_two_result.txt') as f:\n",
        "    lines = f.read().split('\\n')\n",
        "    lines = list(filter(None, lines))\n",
        "    \n",
        "    for line, text in enumerate(lines):\n",
        "        if text.startswith('Enter'):\n",
        "            line_id_img.append(line + 1)\n",
        "        \n",
        "    line_id_img_dif = list(itertools.starmap(lambda x, y: y - x - 1, zip(line_id_img, line_id_img[1:])))\n",
        "    line_id_img = line_id_img[:-1]\n",
        "\n",
        "    for i, j in zip(line_id_img, line_id_img_dif):\n",
        "        res.append(lines[i:i + j])\n",
        "\n",
        "    for k in range(len(line_id_img)):\n",
        "        images = lines[line_id_img[k] - 1].split(':')[1].strip()\n",
        "        coords = [i.split('(')[1].strip().replace(')', '') for i in res[k]]\n",
        "        out = [l.split() for l in coords]\n",
        "        int_coords = [tuple(int(x) for x in out[i] if x.isnumeric()) for i in range(len(out))]\n",
        "        result_dict.update({images: int_coords})\n",
        "\n",
        "print(result_dict)\n",
        "\n",
        "\n",
        "for k, v in result_dict.items():\n",
        "    sneaker_name = k.split('/')[-2]\n",
        "    sneaker_path = os.path.join(path_cropped_images, sneaker_name)\n",
        "    if not os.path.exists(sneaker_path):\n",
        "        os.mkdir(sneaker_path)\n",
        "    for i in v:\n",
        "        try:\n",
        "          crop_image(k, i, sneaker_path + '/' + str(random.randint(1111, 9999)) + k.split('/')[-1])\n",
        "        except Exception as e:\n",
        "            continue"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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(2).jpg': [(6, 646, 496), (17, 121, 975, 1044)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/40 (3).jpg': [(30, 91, 252, 146)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/8 (1).jpg': [(95, 98, 1044, 607)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/45 (3).jpg': [(145, 21, 692, 593), (456, 183, 593, 586)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/1 (6).jpg': [(28, 292, 847, 598)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/48 (5).jpg': [(1, 62, 774, 415)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/52 (4).jpg': [(9, 125, 1084, 590)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/54 (5).jpg': [(26, 22, 758, 353)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/13 (6).jpg': [(22, 955, 1881, 1776)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/15 (1).jpg': [(23, 39, 668, 379)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/38 (5).jpg': [(46, 154, 317, 196)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/24 (2).jpg': [(54, 129, 1053, 551)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/32 (4).jpg': [(12, 36, 996, 542)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/53 (3).jpg': [(26, 222, 125)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/23 (2).jpg': [(219, 602, 318), (30, 55, 515, 261)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/46 (3).jpg': [(78, 385, 1075, 641)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/29 (4).jpg': [(36, 333, 1526, 795)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/30 (1).jpg': [(23, 50, 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'/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/19 (5).jpg': [(438, 529, 366), (465, 132, 615, 715)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/13 (5).jpg': [(188, 894, 606)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/22 (5).jpg': [(116, 264, 3614, 2085)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/67 (2).jpg': [(41, 26, 230, 115)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/56 (2).jpg': [(31, 159, 1043, 547)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/66 (1).jpg': [(114, 361, 1031, 580)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/53 (2).jpg': [(6, 56, 857, 508)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/63 (3).jpg': [(56, 263, 905, 518)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/62 (4).jpg': [(143, 710, 3597, 2059), (423, 3699, 1799)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/61 (4).jpg': [(3, 96, 1188, 567)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/55 (2).jpg': [(4, 52, 602, 424)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/21 (4).jpg': [(66, 56, 902, 508)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/54 (3).jpg': [(487, 616, 694)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/38 (2).jpg': [(72, 184, 337, 150), (77, 63, 302, 142)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/29 (2).jpg': [(75, 92, 565, 284)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/40 (2).jpg': [(661, 1302, 2046, 1666)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/19 (2).jpg': [(120, 1041, 601)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/14 (2).jpg': [(257, 1608, 958)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/13 (3).jpg': [(222, 416, 225, 130), (227, 484, 385, 247)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/37 (2).jpg': [(131, 111, 523, 377)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/43 (2).jpg': [(195, 299, 539, 272), (580, 104, 528, 517)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/58 (2).jpg': [(150, 24, 980, 698)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/61 (2).jpg': [], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/42 (2).jpg': [(14, 6, 276, 164)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/15.jpg': [(963, 2486, 1584)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/39 (1).jpg': [(97, 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'/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/62 (2).jpg': [], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/73 (3).jpg': [(20, 76, 1156, 595)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/9 (4).jpg': [(124, 252, 383), (135, 219, 743, 554), (190, 40, 432, 271)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/7 (3).jpg': [(11, 222, 608, 408)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/4 (3).jpg': [(27, 119, 1056, 705)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/17 (2).jpg': [(28, 391, 326)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/25 (4).jpg': [(59, 146, 292, 154), (65, 27, 290, 155)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/68 (1).jpg': [(391, 44, 797, 830)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/75 (3).jpg': [], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/47 (1).jpg': [(166, 79, 155, 215), (266, 271, 237, 140)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/1 (4).jpg': [(304, 502, 428)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/61 (3).jpg': [(17, 89, 273, 160)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/64 (3).jpg': [(34, 123, 1087, 648)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/28 (3).jpg': [(25, 106, 1055, 646)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/71 (2).jpg': [(397, 328, 1207, 623)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/19 (3).jpg': [(16, 53, 860, 438)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/11 (2).jpg': [(37, 32, 571, 585)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/86 (2).jpg': [(39, 78, 781, 430)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/39 (2).jpg': [(15, 22, 444, 265)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/48 (3).jpg': [(158, 61, 669, 642)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/53 (1).jpg': [(441, 1266, 963)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/10 (3).jpg': [(101, 204, 145), (77, 96, 132, 117)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/41 (2).jpg': [(4, 31, 463, 250)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/67 (1).jpg': [(11, 147, 284, 159), (47, 19, 270, 204)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/79 (2).jpg': [(33, 204, 551, 301)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/82 (1).jpg': [(4, 153, 997, 523)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/42 (3).jpg': [(75, 332, 2830, 1885)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/52 (3).jpg': [(168, 307, 720, 392)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/27 (4).jpg': [(18, 42, 152, 94), (145, 47, 82, 86)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/63 (2).jpg': [(15, 175, 639, 423)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/71 (3).jpg': [(18, 105, 1182, 819)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/62 (3).jpg': [(69, 65, 690, 388)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/22 (3).jpg': [(103, 95, 463, 421)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/46 (2).jpg': [(200, 177, 257, 159), (242, 271, 322, 184)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/51 (2).jpg': [(198, 418, 1191, 601)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/38 (3).jpg': [(10, 67, 770, 448)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/70 (1).jpg': [(481, 3166, 2056)], '/content/drive/MyDrive/ebay_sneakers/OG_photos/Max_n_Ultra/Nike_Air_Max_1/55 (3).jpg': [(18, 242, 953, 713)]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import os\n",
        "# path_to_final_models = \"/content/drive/MyDrive/ebay_sneakers/black_final_crop\"           #пути к кропнутым фоткам\n",
        "# final_models_list =  os.listdir(path_to_final_models)                                # пути ко всем папкам\n",
        "# final_name_jpg = [i for i in [x for x in os.walk(path_to_final_models)]][1:]\n",
        "# final_path = [os.path.join(name[0], i) for name in final_name_jpg[1:] for i in name[2]]\n",
        "# #final_path = [os.path.join(path_to_final_models, i) for i in final_models_list]         #абсолютный путь для каждой кропнутой фотки"
      ],
      "metadata": {
        "id": "_46RgdYFUGca"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-lHFgkLcxIs2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbrLNEdTSHPX"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense, Dropout, Flatten, BatchNormalization\n",
        "import random"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "EmmRj3w1tk56"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oUOVLeZFSHPX"
      },
      "source": [
        "path_to_train = '/content/drive/MyDrive/ebay_sneakers/project/Train'\n",
        "path_to_valid = '/content/drive/MyDrive/ebay_sneakers/project/Valid'\n",
        "\n",
        "# path_to_test = '/content/drive/MyDrive/ebay_sneakers/cropped_images/for_test' # для предсказаний тренировочных\n",
        "# new_path_to_learning = '/content/drive/MyDrive/ebay_sneakers/cropped_images/new_learning' # путь до лернинга"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iya7jUHTitQ8"
      },
      "source": [
        "import os\n",
        "path_to_models = \"/content/drive/MyDrive/ebay_sneakers/project\"           #пути к кропнутым фоткам\n",
        "# path_to_new_models = \"/content/drive/MyDrive/ebay_sneakers/cropped_images/for_test\"\n",
        "models_list =  os.listdir(path_to_models)                                # пути ко всем папкам\n",
        "name_jpg = [i for i in [x for x in os.walk(path_to_models)]]\n",
        "abs_path = [os.path.join(name[0], i) for name in name_jpg for i in name[2]]\n",
        "# abs_path = [os.path.join(path_to_models, i) for i in name_jpg]         #абсолютный путь для каждой кропнутой фотки"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yq3rGcWOSHPY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24920574-d73e-454a-f2d0-0ec662331a84"
      },
      "source": [
        "abs_folders_path_to_models = [os.path.join(path_to_models, i) for i in os.listdir(path_to_models) if i != 'Valid' and i != 'Train' \n",
        "                              and i != 'checkpoint' and i != 'extra']\n",
        "abs_folders_path_to_models"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/MyDrive/ebay_sneakers/project/Nike_Air_Force_1_Low',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Nike_Air_Max_90',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Nike_Air_Huarache',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Reebok_Instapump_Fury',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Nike_Air_Max_95',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Nike_Blazer',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Air_Jordan_11',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Air_Jordan_4',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Adidas_NMD_R1',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Unknown',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Adidas_Yeezy_Boost_350',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Adidas_Yeezy_Boost_700',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Nike_Dunk_low',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Air_Jordan_12',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Air_Jordan_6',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Air_Jordan_13',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Air_Jordan_1_Retro_High',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Nike_Air_VaporMax',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Adidas_Ultra_Boost_4.0',\n",
              " '/content/drive/MyDrive/ebay_sneakers/project/Nike_Air_Max_1']"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Запускать Цикл Только ОДИН РАЗ"
      ],
      "metadata": {
        "id": "G2GDBPCmIj0v"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVcQNn1ESHPY"
      },
      "source": [
        "for folder in abs_folders_path_to_models:\n",
        "  k = len(os.listdir(folder)) * 20 // 100 \n",
        "  indicies = random.sample(range(len(os.listdir(folder))), k)\n",
        "  valid_photos = [os.path.join(folder, os.listdir(folder)[i]) for i in indicies]\n",
        "  for vl in valid_photos: \n",
        "    path_to_valid_model = os.path.join(path_to_valid, vl.split('/')[-2])\n",
        "    if not os.path.exists(path_to_valid_model):\n",
        "      os.mkdir(path_to_valid_model) \n",
        "    os.rename(vl, os.path.join(path_to_valid_model, vl.split('/')[-1]))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.applications.inception_v3 import InceptionV3, preprocess_input\n",
        "from tensorflow.keras.applications import InceptionResNetV2\n",
        "from tensorflow.keras.applications import NASNetLarge, ResNet101V2\n",
        "# from tensorflow.keras.applications.na\n",
        "from tensorflow.keras.preprocessing import image, image_dataset_from_directory\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
        "import numpy as np\n",
        "from operator import itemgetter "
      ],
      "metadata": {
        "id": "pyijU7Ncoyqz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "236dfbc9-4a0b-4736-aac4-d4ded9aa0de0",
        "outputId": "72cf3818-36c4-44e6-cd32-15061a46d521"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2969 images belonging to 20 classes.\n",
            "Found 729 images belonging to 20 classes.\n"
          ]
        }
      ],
      "source": [
        "train_data_gen = image.ImageDataGenerator(rescale=1/255., \n",
        "                                        shear_range=0.2,\n",
        "                                        zoom_range=0.2,\n",
        "                                        horizontal_flip=True,\n",
        "                                        vertical_flip=True,\n",
        "                                        rotation_range=90,\n",
        "                                        validation_split=0.2).flow_from_directory('/content/drive/MyDrive/ebay_sneakers/project/Train')\n",
        "val_data_gen = image.ImageDataGenerator(rescale=1/255.).flow_from_directory('/content/drive/MyDrive/ebay_sneakers/project/Valid')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3e1415fd-c522-40ac-b820-d84ab6b288a5"
      },
      "outputs": [],
      "source": [
        "inception = ResNet101V2(weights='imagenet', include_top=False)\n",
        "x = inception.output\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "x = Dropout(.3)(x)\n",
        "# x = Dense(512, activation='relu')(x)\n",
        "# x = Dropout(.3)(x)\n",
        "preds = Dense(20, activation='softmax')(x)\n",
        "model = Model(inputs=inception.input, outputs=preds)\n",
        "\n",
        "\n",
        "for layer in inception.layers:\n",
        "    layer.trainable = False\n",
        "    \n",
        "# for layer in inception.layers[:-3]:\n",
        "#     layer.trainable = True\n",
        "\n",
        "\n",
        "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), \n",
        "              loss='categorical_crossentropy', \n",
        "              metrics=['accuracy',keras.metrics.TopKCategoricalAccuracy(k=4, name=\"top_4_accuracy\")])"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ks5zz24Hue3P"
      },
      "source": [
        "checkpoint = keras.callbacks.ModelCheckpoint(filepath = '/content/drive/MyDrive/ebay_sneakers/project/checkpoint/', monitor='val_top_4_accuracy', verbose=1, save_best_only=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_path = '/content/drive/MyDrive/ebay_sneakers/project/Train/'"
      ],
      "metadata": {
        "id": "WdQiT-IA5MLC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "num_skipped = 0\n",
        "for folder_name in (os.listdir(my_path)):\n",
        "    folder_path = os.path.join(my_path, folder_name)\n",
        "    print(folder_name)\n",
        "    for fname in os.listdir(folder_path):\n",
        "        fpath = os.path.join(folder_path, fname)\n",
        "        try:\n",
        "            fobj = open(fpath, \"rb\")\n",
        "            is_jfif = tf.compat.as_bytes(\"JFIF\") in fobj.peek(10)\n",
        "        finally:\n",
        "            fobj.close()\n",
        "\n",
        "        if not is_jfif:\n",
        "            num_skipped += 1\n",
        "            # Delete corrupted image\n",
        "            os.remove(fpath)\n",
        "\n",
        "print(\"Deleted %d images\" % num_skipped)"
      ],
      "metadata": {
        "id": "uc7Hogaw4A1w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eg_IM8T0ue3Q"
      },
      "source": [
        "history = model.fit(\n",
        "      train_data_gen,\n",
        "      # steps_per_epoch=34, \n",
        "      callbacks = checkpoint,\n",
        "      epochs=10,\n",
        "      validation_data=val_data_gen,\n",
        "      # validation_steps_per_epoch=14, \n",
        "      )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyVEHqzHSQBI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "37d8e3db-0494-49a2-dd6e-9c438afade73"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "epochs=10\n",
        "train_acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "train_loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "No_Of_Epochs = range(epochs)\n",
        "\n",
        "plt.plot(No_Of_Epochs, train_acc, marker = 'o', color = 'blue', markersize = 12, \n",
        "                 linewidth = 2, label = 'Training Accuracy')\n",
        "plt.plot(No_Of_Epochs, val_acc, marker = '.', color = 'red', markersize = 12, \n",
        "                 linewidth = 2, label = 'Validation Accuracy')\n",
        "\n",
        "plt.title('Training Accuracy and Testing Accuracy w.r.t Number of Epochs')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(No_Of_Epochs, train_loss, marker = 'o', color = 'blue', markersize = 12, \n",
        "                 linewidth = 2, label = 'Training Loss')\n",
        "plt.plot(No_Of_Epochs, val_acc, marker = '.', color = 'red', markersize = 12, \n",
        "                 linewidth = 2, label = 'Validation Loss')\n",
        "\n",
        "plt.title('Training Loss and Testing Loss w.r.t Number of Epochs')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+YeHChdSqVYu5c+dyxx138Morr5T6zDfffMO8efPYtWsXHTt25Prrry/VTvuLL74gLy+PZs2a0a9fPz755BNycnK49tprWbBgAW3atGH06NFh53Pz5s3cdtttfP755zRq1IiBAwcya9YsWrZsyaZNm1i5ciUAO3bsAODBBx9k7dq11K1b9/B7VaVal9D79nU3DYWqSklPh6FDoU+fqsmXMabyRowYQWpqKgA7d+5kxIgRdOnShVtuuYW8vLygnxkyZAh169alSZMmNG3alB9//LFUmr59+9KiRQtSUlLo0aMH69at45tvvqFt27aH23RHEtCXLl1K//79ycrKolatWowZM4YFCxbQtm1b1qxZw4033sg777zDEUe4cdi7devGmDFjeP7558usSoqVah3QRdwdoMOGFTejCpSR4eY/95zdVGRMKKqlp4yMyJaRmVl6GRWRmZl5+Pmf//xnBgwYwMqVK3njjTfKbH9dt27dw89TU1M5ePBghdJEQ6NGjVixYgX9+/fnn//8J1dffTUAb731FjfccAPLli2jT58+MVt/MNU6oIO7nf+FF+DDD+HCC0sH9mefhenT7bZ/YyqqOlyr2rlzJ82bNwdgypQpUV9+x44dWbNmDevWrQPgxRdfDPuzffv25aOPPmLbtm0cOnSI6dOnc9ppp7Ft2zaKioq48MILuf/++1m2bBlFRUVs3LiRAQMG8NBDD7Fz5052794d9e0pS7UP6OBK3n37unq73bvh0CG45BI3b8uW+ObNmEQ3fnz4LcRida1qwoQJ3H777fTs2TMmJdr09HSeeuopBg0aRO/evalfvz4NGjQImvaDDz6gRYsWh6d169bx4IMPMmDAALp3707v3r0ZNmwYmzZton///vTo0YNLLrmEBx54gEOHDnHJJZfQtWtXevbsyU033UTDhg2jvj1liduYojk5OVqZAS6mTnVtYs85x104NcYE9/XXX3PssceWOV/V3TT0+uvlt3ZJT3fVm4naZ9Lu3bupV68eqsoNN9xA+/btueWWW+KdrXIF++5E5HNVDdqWMyFK6MGceaZ7nD8ffv01rlkxJqGFulaVkpIc16qefvppevTowXHHHcfOnTu59tpr452lqEvYgH7MMdCtmytRfPJJvHNjTGIr61pVZiYMH+4KTol+reqWW25h+fLlrFq1imnTppER6dXgBFCt26GHMnAgfPklvP++62vCGFNx/teqTGJK2BI6wFlnucf33otvPowxpjpI6IB+yilQty4sWwYFBfHOjTHGxFdCB/T0dDj1VPd87tz45sUYY+ItoQM6uHp0sGoXY6qrAQMG8O6775Z474knnuD6668v8zP9+/fH16x58ODBQftEueeee3jkkUfKXfesWbNYtWrV4dd33XUXc6NQ+quu3ewmVUC3EVWMiYJp0yA72zVzyc52ryth9OjRzJgxo8R7M2bMCLs/lTlz5lT45pzAgH7vvfdypq/NcxIKGdBF5FkR2SoiK0Ok6yMiB0VkePSyF1rXrnDUUbB5M/h9b8aYipg2DcaNg/XrXQlp/Xr3uhJBffjw4bz11luHB7NYt24dmzdv5pRTTuH6668nJyeH4447jrvvvjvo57Ozs9m2bRsAEydOpEOHDpx88smHu9gF18a8T58+dO/enQsvvJC9e/eyaNEiZs+ezR/+8Ad69OjB999/z9ixY3n55ZcBd0doz5496dq1K1deeSW/eje0ZGdnc/fdd9OrVy+6du3KN998E/a2xr2bXVUtdwJOBXoBK8tJkwp8CMwBhodapqrSu3dvjZZLL3VdBD32WNQWaUzSWLVqVfGL4P1zVX4KYciQITpr1ixVVX3ggQd0/Pjxqqq6fft2VVU9ePCgnnbaabpixQpVVT3ttNN06dKlqqraunVrLSgo0NzcXO3SpYvu2bNHd+7cqe3atdOHH35YVVW3bdt2eF133nmnTpo0SVVVL7/8cn3ppZcOz/O93rdvn7Zo0UK//fZbVVW99NJL9fHHHz+8Pt/nn3zySb3qqqtKbc+8efN0yJAhJd7btGmTtmzZUrdu3aqFhYU6YMAAfe211zQ3N1fPPPPMw+l+/vlnVVU95phjdP/+/SXeC1Tiu/MAuVpGXA1ZQlfVBcBPIZLdCLwCbK34X0vFWT26MdWbf7WLf3XLzJkz6dWrFz179iQvL69E9UighQsXcv7555ORkcERRxzB0KFDD89buXIlp5xyCl27dmXatGlldr/r8+2339KmTRs6dOgAwOWXX86CBQsOz7/gggsA6N279+EOvUKpDt3sVroOXUSaA+cD/wgj7TgRyRWR3IIotjP0VYl99BGU0eumMQZCl7Vbtw7+udaty/9cCMOGDeODDz5g2bJl7N27l969e7N27VoeeeQRPvjgA7788kuGDBlSZre5oYwdO5a///3vfPXVV9x9990VXo6PrwveaHS/W5Xd7EbjougTwG2qWhQqoapOVtUcVc3JysqKwqqdo4+G7t2tGwBjKm3ixNIdpGdkuPcroV69egwYMIArr7zycOn8l19+ITMzkwYNGvDjjz/y9ttvl7uMU089lVmzZrFv3z527drFG2+8cXjerl27OOaYYygsLGSaX31//fr12bVrV6lldezYkXXr1vHdd98BMHXqVE477bRKbWN16GY3GuX8HGCGuB57mgCDReSgqs6KwrLDNnAgrFjhql3OOKMq12xMEhkzxj3eeSds2ACtWrlg7nu/EkaPHs35559/uOqle/fu9OzZk06dOtGyZUv69etX7ud79erFRRddRPfu3WnatCl9/IYou++++zj++OPJysri+OOPPxzER40axTXXXMOkSZMOXwwFSEtL49///jcjRozg4MGD9OnTh+uuuy6i7fF1s+vz0ksvHe5mV1UZMmQIw4YNY8WKFVxxxRUUFbkyr383uzt37kRVo9bNbljd54pINvCmqnYJkW6Kl+7l8tJB5bvPDTR3rusKoGdPd+eoMcYJ1X2uqb4i7T43ZAldRKYD/YEmIpIP3A3UBlDVf1Y2w9Fy8smu8/0vvoCtW6Fp03jnyBhjqlbIgK6qYY+mqqpjK5WbSkhLc90AvPeeK61ffHG8cmKMMfGR8HeK+rPmi8YEF07VqqleKvKdJW1At+PXGCctLY3t27dbUE8gqsr27dtJS0uL6HMJPcBFoC5dXBPGLVsgL8+9Nqama9GiBfn5+UTz3g8Te2lpaSVa0YQjqQK6iCulP/ecK6VbQDcGateuTZs2beKdDVMFkqrKBawe3RhTcyVdQLduAIwxNVXSBfSjjoIePVww//jjeOfGGGOqTtIFdLBqF2NMzWQB3RhjkkRSBvR+/dwA0itWwI8/xjs3xhhTNZIyoKelga8nzCiMB2uMMQkhKQM6WLWLMabmqREB3e54NsbUBEkb0Dt3hmbN4IcfYOXKeOfGGGNiL2kDuogb8AKs2sUYUzMkbUAHq0c3xtQsSR3Qfd0ALFjgBpA2xphkltQBvWlTN8aodQNgjKkJkjqgg1W7GGNqDgvoxhiTJJI+oPu6AfjyS9eE0RhjklXIgC4iz4rIVhEJ2ppbRMaIyJci8pWILBKR7tHPZsXVrQv9+7vn1g2AMSaZhVNCnwIMKmf+WuA0Ve0K3AdMjkK+osqqXYwxNUHIMUVVdYGIZJczf5Hfy8+AyEY1rQKB3QCIxDc/xhgTC9GuQ78KeLusmSIyTkRyRSS3KkcgP/ZYaN7cdaX71VdVtlpjjKlSUQvoIjIAF9BvKyuNqk5W1RxVzcnKyorWqsPIm1W7GGOSX1QCuoh0A54Bhqnq9mgsM9qsXxdjTLKrdEAXkVbAq8Clqrq68lmKDesGwBiT7MJptjgd+BToKCL5InKViFwnItd5Se4CGgNPichyEcmNYX4rLCsLevWCX3+FhQvjnRtjjIm+cFq5jA4x/2rg6qjlKIYGDoRly1y1i69O3RhjkkXS3ynqzy6MGmOSWY0K6CedBBkZrunili3xzo0xxkRXjQro/t0AvP9+XLNijDFRV6MCOli1izEmedXYgD53LhQVxTcvxhgTTTUuoHfqBC1aWDcAxpjkU+MCunUDYIxJVjUuoIMFdGNMcqqRAf2MM1xJfeFC2Ls33rkxxpjoqJEBvUkT6wbAGJN8amRAB6t2McYkHwvoFtCNMUmixgb0E0+EzExYuRI2b453bowxpvJqbEC3bgCMMcmmxgZ0sGoXY0xysYCOK6FbNwDGmERXowN6x47QsiUUFMCXX8Y7N8YYUzk1OqBbNwDGmGRSowM6WEA3xiSPGh/QrRsAY0yyqPEBvXFjyMmBAwdgwYJ458YYYyquxgd0sGoXY0xyCBnQReRZEdkqIivLmC8iMklEvhORL0WkV/SzGVtnneUeLaAbYxJZOCX0KcCgcuafA7T3pnHAPyqfrarl6wYgLw82bYp3bowxpmJCBnRVXQD8VE6SYcBz6nwGNBSRY6KVwapQpw4MGOCeWzcAxphEFY069ObARr/X+d57pYjIOBHJFZHcgoKCKKw6eqwe3RiT6Kr0oqiqTlbVHFXNycrKqspVh2TdABhjEl00AvomoKXf6xbeewmlQwdo1Qq2bYPly+OdG2OMiVw0Avps4DKvtcsJwE5V3RKF5VYp/24ArB7dGJOIwmm2OB34FOgoIvkicpWIXCci13lJ5gBrgO+Ap4Hfxiy3MWb16MaYRFYrVAJVHR1ivgI3RC1HceTrBuDjj2HPHteU0RhjEoXdKernyCOhTx/rBsAYk5gsoAewahdjTKKygB7AAroxJlFZQA9wwglQrx6sWgX5+fHOjTEmKqZNg+xsSElxj9OmxTtHMWEBPUDt2nD66e65NV80JglMmwbjxsH69aDqHseNi09Qj/EfS8hWLjXRWWfB7Nmu2uWKK+KdG2NMhe3bB+PHlx69Zu9eF9TfeQdq1XIlOd/k/7q8eZG+njMH/vQnlyco/mMBGDMmKpsrrtVh1cvJydHc3Ny4rDuU1avdANKNG8PWre7P1BhTzR065OpKlywpnr76yr1fnbVuDevWhZ1cRD5X1Zxg86yEHkT79m4fr18PX3wBvXvHO0fGmBJUXRBcurQ4eH/+eemSeEqKKx0XFpZeRpMm8Nhjbl5hIRw8WPw8Fq9Xrw6+LRs2RG23WEAPwtcNwNNPu2oXC+jGxNm2bSWD95Il7r1AbdpA375u6tMHevWCWbNc1YZ/sM/IgCeeiFpVR1iys10pMVCrVlFbhQX0MvgC+vvvw+23xzs31dy0aXDnna6k0aoVTJxYtT8Uk1z27HGnxv7Be+3a0umaNCkZvPv0gWC9uPqOxXgfoxMnBv9jmTgxeutQ1bhMvXv31ups+3bVlBTV2rVVd++Od27K8Pzzqq1bq4q4x+efj08eMjJU3UmwmzIy4pMXU/2EOkYLC1WXL1edPFn16qtVu3VzPzz/48l3TJ1yiur48aozZqiuWaNaVBSPLaqcKPxmgVwtI67aRdFynHACLF4Mb70FgwfHOzcBfE2xAv/tJ08uu+RRVOSusO/b5z7ne/R/Hunjp5+6vhICpafD+ee7Rv3167spnOeZmRW/Cm1nCtVLsGM0LQ2uusoNE7ZkCSxbVtzqwyc1Fbp2LVn67tzZtRgx5V4UtYBejrvugvvug5tvdtVt1UpZ9XF160KPHsGD76+/Vnk2KyQzM/I/gs8/h3/8o+Q2hvqDS2ZV9ed24ADs2FE87dxZ/HzCBPcYSrt2JYN3z57uuzNBWSuXCho40AX0atUNwM6d8PLLwYM5uIC2eHHZn09Pdz+W9PSSzyv6OGYM/PBD6fVkZbkWBLt2uWn37vCe79lTPP34Y+X21d69cM01kJvrgoZvys52JcRkFVgyLqu9syrs3192QC7vPd/7gaXrcN17b3G9d+PGldtec5iV0MtRWOiOtV27XEGnZcvQn4lZRt59F6ZOdXc87d9fdtqmTeH114MH37Q014QnmipS9VOeQ4dcMA/3D8D3fPr08NeRkgItWpQM8r6pbVto2DDyfMfb/v1QUOBunDjnHPc8UFqaq8rwD9LBqssiUauW218NGrhH/2nmTPfdBIqw3bUpyUroFeTrBuD1111rlyuvrMKVq7pqhKlTXbDy/4H27+8ayz//fMkSUkaGKxWfcBXHUJAAABaXSURBVELV5TPaLQhSU+GII9wUiUWLgp+1NGkCt94K339fPG3c6PK6YQPMm1f6M0ceWTrI+543axa6jj8a1R1FRfDzz+4sZevW0lPg+7/8EnqZ+/e7pn/+6tQpHYh9U7AgHfheRkbZhYQBA2LfqsOUYCX0EJ56Cm64AS66CGbMqIIVbtjgAsLUqfD118Xvd+oEl13mAoOv3apdBCwWyZnCgQMu+PsH+e+/hzVr3GN51Qhpaa6ts3+Q96/KeeWVsvNx/vnhBeetW90feCR3ONaq5c7OmjaFb74JfhbnO3vzD8hpaeGvoyLsGI06uyhaCd995wrDRx7pfmepqTFYyS+/uEAwdSrMn+9K5+DqoUePhksvdXc3Rbu6JNlEI3ioumsC/gHefwpWleEj4krvwQKxSPH3Gq5GjYqDdLDpqKOKnzdsWHx8RLsazFQrFtArQdUVxnx3GecE3Y0VcPCgq8eZOtXdyeYrFdatC8OGuSB+9tmu3sdUH7t2lQ70vtfr15dfqq5bt+yAHDhlZVXuwq2VjJOW1aFXgq8bgMmTXWuXSgV0VVi+3AXxF14o2Yrj1FNdEB8+PDEvytUU9etD9+5uClRY6KpdNm8uPa9lSxfwq+osa8wYC+A1kPUjGIZKj2K0aRP89a+uhUGvXvD44y6Yd+jg2kWuXQsffQRXX23BPJHVru2+58A21BkZ8MADVmVmYi6sErqIDAL+BqQCz6jqgwHzWwH/ARp6af6oqnOinNe4Of10VzW6aJFrKVevXhgf2r0bXn0VnnsOPvywuP60cWMYNcpd4OzTx37kyaa69BtiaqSQdegikgqsBs4C8oGlwGhVXeWXZjLwhar+Q0Q6A3NUNbu85SZKHbrPiSfCZ5/Bm2/CkCFlJDp0CD74wAXx114rvihVpw6cd54L4oMGJfdNLcaYmKpsHXpf4DtVXeMtbAYwDFjll0YBX8PhBkCQSsTENnCgC+jvvQdDdgRccLr2Wti+3dWLb9lS/KGTT3b14iNGuBYLxhgTQ+EE9ObARr/X+cDxAWnuAd4TkRuBTODMYAsSkXHAOIBWUewDuCoMHAj33VtEwxefhsm/L27nu3493HFHccLf/MYF8Usucc1jjDGmikSrlctoYIqqPioiJwJTRaSLqhb5J1LVycBkcFUuEa8l2k2x9u93neQXFBQ/+j/3ezypoIBCtpP6Y1HwZdWr54rvJ5xg9eLGmLgIJ6BvAvx7MWnhvefvKmAQgKp+KiJpQBNgazQyCYTucEjV9U1RRkAOGqx37w579YK72qve81L27HEV7cYYEyfhBPSlQHsRaYML5KOAiwPSbADOAKaIyLFAGlDOLXUVcOedwUfuvuIKN6r3tm2RDwZbq5a7gaNJk+CPAe/96+XGnH1TB7KJ7TBSxhhTESEDuqoeFJHfAe/iCqnPqmqeiNyLGzljNjAeeFpEbsEVYsdqtG9BLWsg1cLC4ht06tcPKzAffmzQIKLqkTMHwx03TeQZxpGBdThkjKleEufW/7IGdDjmGHdPfpMm7tbqGGvXDo5fM41/H30ndX+0dsbGmKpVXrPFxLlTdOLE4HfgPfwwNG9eJcEcXGuX6Yzhkd+tc12crltnwdwYUy0kTkAfM8Z1qNK6tasmad26ynuPU3WrBTc8XUqKGy1t5Eg3PGKcTnaMMQZIpCqXOCssdDd6zp5d+tpsSoobEOi889xNotZBojEmVpKjyiWOVMsO5uBqXvbscWMHXHaZldSNMfFhAT0MS5bAG28ED+b+9u1z6QJH+TLGmKpgAT0Mjz4a/uDm+/a59MYYU9UsoIfhrbdctUo4iopcemOMqWoW0MMQbum8oumNMSYaLKCHIT09tumNMSYaLKCHYcgQ1zQxHCkp5QyAYYwxMWQBPQzjx4df6haBG2+MbX6MMSYYC+hh6NvX3TQUTlA/dMh1DPnzz7HPlzHG+LOAHgYRdwfosGHuVv/A6peUFNetzKBBrq+wBQugX7/gfYkZY0ysWEAPU+3absjQDz+ECy8sDuyZmTB8OMyfD2+/DYsXQ5cu8PXXbvCiZcvinXNjTE1hfbnEwI4dLuh/+KEL+C+9BOecE+9cGWOSgfXlUsUaNnSl9UsucX28nHcePPNMvHNljEl2FtBjpE4dV+9+553uQuk118Cf/2wddxljYscCegyJwP33w7/+Bamp7vnll8OBA/HOmTEmGVlArwLjxrmudzMzYepUGDwYdu6Md66MMcnGAnoVGTwYPvoIjjoKPvgATj4ZNm6Md66MMcnEAnoV6t0bPvsMOnWClSvhxBNhxYp458oYkywsoFex7GxYtAhOPRU2bYJTToH33493rowxySCsgC4ig0TkWxH5TkT+WEaakSKySkTyROSF6GYzuTRqBO+9B6NGwa5drjpmypR458oYk+hCBnQRSQWeBM4BOgOjRaRzQJr2wO1AP1U9Dvh9DPKaVOrWhWnTYMIEOHgQrrgC7r3XmjUaYyounBJ6X+A7VV2jqgeAGcCwgDTXAE+q6s8Aqro1utlMTikp8NBD8Pe/u+d33w1XXw2FhfHOmTEmEYUT0JsD/u0x8r33/HUAOojIJyLymYgMCrYgERknIrkikltQUFCxHCehG26A115zvTk++6y7s3TXrnjnyhiTaKJ1UbQW0B7oD4wGnhaRhoGJVHWyquaoak5WVlaUVp0chg51HXxlZcG777qLpps3xztXxphEEk5A3wS09HvdwnvPXz4wW1ULVXUtsBoX4E0E+vaFTz+F9u1h+XLXW2NeXrxzZYxJFOEE9KVAexFpIyJ1gFHA7IA0s3Clc0SkCa4KZk0U81ljtGvnmjWedJK78ahfP5g3L965MsYkgpABXVUPAr8D3gW+Bmaqap6I3CsiQ71k7wLbRWQVMA/4g6puj1Wmk12TJjB3ruuCd+dOOPts1yLGGGPKY/2hV2NFRXDrrfD44+71X/4Cf/yj6/TLGFMzWX/oCSolBR57DJ54wgXxO+6A6693zRoXL4YRI0qOnDRyJCxZYm3ZjamprISeIF59FcaMgf373bilO3e650VFxWlSUlzTx/POc32x164dv/waY2LDSuhJ4IILXL16nTqwZQvs3VsymIN7vWcPvP46XHaZldSNqWksoCeQWrXcQBmh7NsHb7wBS5fGPk/GmOrDAnoCefRR+PXX8NLu2+fSG2NqDgvoCeStt0pXs5SlqMilN8bUHBbQE8i+fbFNb4xJbBbQE0h6emTpU1NtmDtjahIL6AlkyBDXNDFchYXQpg2MHu3arRtjkpsF9AQyfnz4pfS0NDjrLPd8xgzX0ddJJ8HMmW5ADWNM8rGAnkD69nU3DYUK6unp8F//5brhXbsWbrvNDXv36adw0UXQti08/DD8/HPV5NsYUzUsoCcQEXcH6LBhxbf8+0tJgYwMN/+551z6li3hwQddXfpTT0GHDu75hAlu3u9+B6tXx2d7jDHRZQE9wdSuDS+8AB9+6Hpj9O/LZfhwN0jG9Omlb/vPzHT9wHz9tWvOeNZZ7q7SJ5+ETp1cyf+DD+zuUmMSmfXlUoOtXAl/+xtMnVp8w1LXrvD738PFF7t6eGNM9WJ9uZigunSBp592VTD33QdHHw1ffQVXXQWtWrlBq3/4Id65NMaEywK6ISsL/vQnWL/e1b337AkFBXDvvdC6NYwd64bEC6Rq3fgaU51YlYspRRUWLnT9sM+aVRyY+/d31THnnuu6FrjsMpg927rxNaYqlVflYgHdlGvNGvh//w/+939h1y73Xtu20LChu8BaXvcC6emuxc0LL9goS8ZEi9Whmwpr29YNgZef7x7btHFBftmy0H3FWDe+xlQtC+gmLEcc4apb/u//3B2n4bJufI2pOhbQTURSU4NfIC2LdeNrTNUJK6CLyCAR+VZEvhORP5aT7kIRUREJWr9jkkOk3fLu3euqaKzVizGxFTKgi0gq8CRwDtAZGC0inYOkqw/cDFi/fkku0m58VaF3b2jRAsaNcy1j9uyJTd7iyZpxmngLp4TeF/hOVdeo6gFgBjAsSLr7gIeA/VHMn6mGIunGV8RdWG3eHDZvdjcyDRsGjRvD4MGuf5n162Ob36pQWOjurj39dHj1VXdWouoeX3nFvX/xxS6dMbESzs+yOeA/TEK+995hItILaKmq5daWisg4EckVkdyCgoKIM2uqh0i68U1Pd33LbNwIX3zh7kg94QQ4cADefhtuuAGys6FbN7jjDli0CA4dimn2o061uE3+3r2lhwksKnJnJK+/7tJZSd3ESqUviopICvAYMD5UWlWdrKo5qpqTlZVV2VWbOImkG9+hQ6FPH1dS79HD3ZH66aeuS4EpU1yHYvXruy4HHngA+vWDo45ygW/mTNixI3R+4l3VsWSJa565d2/56awZp4k5VS13Ak4E3vV7fTtwu9/rBsA2YJ037Qc2AznlLbd3795qEteBA6qjRqlmZqqmpKi6sOmmlBTVjAw3/8CB0Mv69VfVuXNVf/971XbtSi6rVi3VAQNUH31U9dtvy85HRkbwfGRmhp+PihoxovS6y5pSUlRHjoxdXoqKVD/7THX4cLdPRNzjiBGqixe7+SaxAblaRlwNeaeoiNQCVgNnAJuApcDFqppXRvr5wK2qWu5toHanaOJTdaXNRx6BOXNcCTQ93dWx33qrK5lXZJmrV8Obb7pp4cKSVTDt27uuB84915Xmx44truooS0XvWC0qgp9+gq1by58WLYrsLKBuXfjkE2jXzt1xGy2FhdYdQ01Q6Vv/RWQw8ASQCjyrqhNF5F7cP8XsgLTzsYBuomTHDjfy0ptvuj+Nn34qnpeR4br9DafOPTPT9ffeuXPoAO2btm0rXR8ebUce6S4at2tXemrWLPyLz6ruomus/txM9WF9uZikcOgQfPZZcel95crIPp+SEnmAbtQImjYte8rKgkGDXIk4XKmpcOyxrguF8oJv3bquq4XAQN+2rXvfv7/6xYvhjDPCaw6amekGSOnbN/w8m+rDArpJShkZkd/klJnpgnB5Qdo3NWkSXtXEyJGuaWI4fxYpKe5C8IsvulL1Dz+4wP7996Wn8hqCibimoL4gv2QJ5OWFV/XjnweTeCygm6SUkhJZ3bVIbKpQIikdZ2TAvHnhlY537Qoe7NescW33K9O8MzMTdu+u+OdN/JQX0GtVdWaMiZb09NBNBf1lZMQmH75mnK+/Hro7YV8zznDUrw/du7spUGEhbNhQHOR/+9vI8rxnj7vJq1s3N3JVZmZknw9F1Z01lHfB3Orwo89K6CZhVbSqIxZ8LUzeeMMFr8AWJmlpLpjHqoVJZmZkf27+RFy1TffuLsD7puzs8C/K+rPWNrFlVS4mKcWqqqOiYtGMM1yR/LmJuMHAu3WDFSvcQCUHD5ZOV79+cTrf1LWr60q5LNbaJvYsoJuk5Ase4VR1JHvwqMyf24ED8M038OWXLsB/+aWbyhogvE2bkkG+e3fX8iY11VrbVAUL6CZpxbuqo7qIxZ/b1q3Fwd035eW5P4BAGRmuLr6gANats9Y2sWQB3SS1eFZ1VCdV8edWWOju5PUP8itWwKZNFVuetbaJnAV0Y2qIeP25bd/uOlgbMCCyz6WkJF7vmhURzVY/FtCNMVUi0tY2tWvDt9+6evlkFe1WP+UFdBtT1BgTNZEMfgIu2LVt6y6MPvqo6zc/mVR1X/kW0I0xURPJ4Cd168LAga5Uv3Spq3po1cr1ojlpkhvhKtFVdV/5FtCNMVETyeAn558P77zjWtO89JJr8ZKe7rojvvlmNwZt//5umMKtW6sk+1H36KPh9ze0b59LXxlWh26MiarKtLbZvdt97sUX3RCFviaSKSnugutFF8EFF7gxaau7oiKoVy+yDuTCafVjdejGmCpTu7Zr5/7hh3DhhSWHBRw+HObPd+PMBrsAWK8ejB4Ns2a5Uvl//uMGE09Jcf3ZjxsHRx8N55zjhjAMNURhVQ1PuH27265Jk+Caa9y4uQ0aRN4baKTpA1kJ3RhT7f30kwvyL77oAruvqWOdOnD22a7kPnSo667AJxZ9yuzfD6tWuSaa/tOWLdHZzsqW0C2gG2MSSkEBvPqqC+7z5xeXstPSXGl+5EjX2uaaayrep0xREaxdWzpwr14dvL+czEx3p2zXriWn3/42+h3IWUA3xiSlH36Al192QfDjj4vfr1vXdTgWzk1LGRnw8MOuRO8L3Hl5wfujSUmBDh1KBu3yeqaMRQdyFtCNMUkvP784uH/2WeWXd8wxJYN2165u6ED/of9CiUUfOxbQjTE1SqTDE6akwJVXFgfurl2j15Im2n3s2IhFxpgaJZJBu32efjr6+YDiVj9V0ceOBXRjTNKJdHjCcO9urSgRVzc+c2Zs1xNWO3QRGSQi34rIdyLyxyDz/1tEVonIlyLygYi0jn5WjTEmPJH0KZOS4tIng5CbLCKpwJPAOUBnYLSIdA5I9gWQo6rdgJeBv0Y7o8YYE65I+pRJS3Ppk0E4/2F9ge9UdY2qHgBmAMP8E6jqPFX1neB8BrSIbjaNMSZ8kfQpM3Ro8gyCEk5Abw74d2qZ771XlquAt4PNEJFxIpIrIrkFBQXh59IYYyIg4lqNDBtWfMu/v5QU1xJm2DCXLlnGmo1qXy4icgmQAzwcbL6qTlbVHFXNycrKiuaqjTGmhMr0KZOowmnlsglo6fe6hfdeCSJyJnAncJqq/hqd7BljTMVVVeuS6iKcEvpSoL2ItBGROsAoYLZ/AhHpCfwLGKqqCdpzsTHGJLaQAV1VDwK/A94FvgZmqmqeiNwrIkO9ZA8D9YCXRGS5iMwuY3HGGGNiJG63/otIAbC+gh9vAmyLYnYSne2Pkmx/FLN9UVIy7I/Wqhr0ImTcAnpliEhuWX0Z1ES2P0qy/VHM9kVJyb4/bMQiY4xJEhbQjTEmSSRqQJ8c7wxUM7Y/SrL9Ucz2RUlJvT8Ssg7dGGNMaYlaQjfGGBPAAroxxiSJhAvoofpmr0lEpKWIzPP6os8TkZvjnad4E5FUEflCRN6Md17iTUQaisjLIvKNiHwtIifGO0/xIiK3eL+RlSIyXUQiGBk0cSRUQA+zb/aa5CAwXlU7AycAN9Tw/QFwM+6OZgN/A95R1U5Ad2rofhGR5sBNuDEbugCpuC5Mkk5CBXTC6Ju9JlHVLaq6zHu+C/eDLa9r46QmIi2AIcAz8c5LvIlIA+BU4H8BVPWAqu6Ib67iqhaQLiK1gAxgc5zzExOJFtAj7Zu9xhCRbKAnsDi+OYmrJ4AJQFGohDVAG6AA+LdXBfWMiGTGO1PxoKqbgEeADcAWYKeqvhffXMVGogV0E4SI1ANeAX6vqr/EOz/xICLnAltV9fN456WaqAX0Av6hqj2BPUCNvOYkIo1wZ/JtgGZApjd2Q9JJtIAeVt/sNYmI1MYF82mq+mq88xNH/YChIrIOVxV3uog8H98sxVU+kK+qvjO2l3EBviY6E1irqgWqWgi8CpwU5zzFRKIF9JB9s9ckIiK4OtKvVfWxeOcnnlT1dlVtoarZuOPiQ1VNylJYOFT1B2CjiHT03joDWBXHLMXTBuAEEcnwfjNnkKQXiMMZsajaUNWDIuLrmz0VeFZV8+KcrXjqB1wKfCUiy7337lDVOXHMk6k+bgSmeYWfNcAVcc5PXKjqYhF5GViGaxn2BUnaBYDd+m+MMUki0apcjDHGlMECujHGJAkL6MYYkyQsoBtjTJKwgG6MMUnCAroxxiQJC+jGGJMk/j9eLRD4FB9+9AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.preprocessing import image, image_dataset_from_directory\n",
        "from operator import itemgetter"
      ],
      "metadata": {
        "id": "fKIqHQPRNO_U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzoUchXcONsI"
      },
      "source": [
        "# from tensorflow.keras.applications.resnet50 import ResNet50\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "import numpy as np\n",
        "\n",
        "img_path = '/content/drive/MyDrive/ebay_sneakers/cropped_images/for_test/new_test/nt_2/6334adidas_sl80_3.jpg'\n",
        "img = image.load_img(img_path, target_size=(224, 224))\n",
        "# img = img.rescale(1/255.)\n",
        "x = image.img_to_array(img).astype('float32')/255\n",
        "x = np.expand_dims(x, axis=0)\n",
        "\n",
        "preds = model.predict(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NN0IkJuqONsM"
      },
      "source": [
        "def get_labels(preds, n=4): \n",
        "    preds = list(reversed(np.argsort(preds, axis=1)[:,-4:][0]))\n",
        "    print(preds)\n",
        "    labels = list(train_data_gen.class_indices.keys())\n",
        "    selector = itemgetter(*preds)\n",
        "    return selector(labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_gen.class_indices"
      ],
      "metadata": {
        "id": "KuXQ65AZAFhc",
        "outputId": "c265cdd0-a77c-4d5a-e0d4-8a220d6a8523",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Adidas_NMD_R1': 0,\n",
              " 'Adidas_Ultra_Boost_4.0': 1,\n",
              " 'Adidas_Yeezy_Boost_350': 2,\n",
              " 'Adidas_Yeezy_Boost_700': 3,\n",
              " 'Air_Jordan_11': 4,\n",
              " 'Air_Jordan_12': 5,\n",
              " 'Air_Jordan_13': 6,\n",
              " 'Air_Jordan_1_Retro_High': 7,\n",
              " 'Air_Jordan_4': 8,\n",
              " 'Air_Jordan_6': 9,\n",
              " 'Nike_Air_Force_1_Low': 10,\n",
              " 'Nike_Air_Huarache': 11,\n",
              " 'Nike_Air_Max_1': 12,\n",
              " 'Nike_Air_Max_90': 13,\n",
              " 'Nike_Air_Max_95': 14,\n",
              " 'Nike_Air_VaporMax': 15,\n",
              " 'Nike_Blazer': 16,\n",
              " 'Nike_Dunk_low': 17,\n",
              " 'Reebok_Instapump_Fury': 18,\n",
              " 'Unknown': 19}"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_labels(preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YIWnqBOk5ABf",
        "outputId": "7caa7061-aec1-4c8f-bcd3-24c2b8c4a81e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 16, 12, 0]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Adidas_Ultra_Boost_4.0', 'Nike_Blazer', 'Nike_Air_Max_1', 'Adidas_NMD_R1')"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('sneaker_class_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkpIDKF58zFu",
        "outputId": "a36c5071-2493-4af4-84a8-52a6d1a3358a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  layer_config = serialize_layer_fn(layer)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "my_model = load_model('sneaker_class_model.h5')"
      ],
      "metadata": {
        "id": "VUjgnGUM83TR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_weights('/content/drive/MyDrive/ebay_sneakers/please')"
      ],
      "metadata": {
        "id": "jG48hCF8Bd5z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-WlDmSAONsM",
        "outputId": "04df295d-15bc-461c-9921-5aac484a5a38"
      },
      "source": [
        "train_generator.class_indices"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Adidas_Originals_Sl80': 0,\n",
              " 'Adidas_Stan_Smith': 1,\n",
              " 'New_Balance_237': 2,\n",
              " 'New_Balance_997': 3,\n",
              " 'Nike_Air_Force_1_Low': 4,\n",
              " 'Nike_Air_Huarache': 5,\n",
              " 'Nike_Air_Max_90': 6,\n",
              " 'Nike_Air_Max_95': 7,\n",
              " 'Nike_Blazer': 8,\n",
              " 'PUMA_Suede': 9,\n",
              " 'Reebok_Instapump_Fury': 10}"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "E_WYBUjXwn_g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1lnUwiXk5bIl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "SLu4zzRGqXqM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}