Flower_Detection.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Flower Detection.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flvKPM6CK1yu"
      },
      "source": [
        "# Building a Model for flower detection app"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcgeMraEGPGb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "be595bff-582e-4ae0-f5f9-8bdb1892cf41"
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.4.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__Hdi-yRK91G"
      },
      "source": [
        "## Download the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41guE6RqGS0N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "820dc26d-3056-48cb-9414-1401d116e2ea"
      },
      "source": [
        "dataset_url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'\n",
        "zip_file = tf.keras.utils.get_file(origin=dataset_url, fname='flower_dataset.tgz', extract=True, cache_subdir='/content')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\n",
            "228818944/228813984 [==============================] - 3s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVO3ILj8LH6E"
      },
      "source": [
        "## Creating Data Generators for Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sQTdWfx8Q3B"
      },
      "source": [
        "base_dir = '/content/flower_photos' # Base directory"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zco2GLns9mpj"
      },
      "source": [
        "IMAGE_SIZE = 224 \n",
        "BATCH_SIZE = 64 "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSNwFrr09zm9",
        "outputId": "8ee2f364-b940-46b4-8feb-d2c96d3e7754"
      },
      "source": [
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale = 1/255.,\n",
        "    validation_split=0.2\n",
        ") \n",
        "\n",
        "# Train \n",
        "train_generator = datagen.flow_from_directory(\n",
        "    base_dir,\n",
        "    target_size = (IMAGE_SIZE, IMAGE_SIZE),\n",
        "    batch_size = BATCH_SIZE,\n",
        "    subset = 'training'\n",
        ")\n",
        "\n",
        "# Validation\n",
        "val_generator = datagen.flow_from_directory(\n",
        "    base_dir,\n",
        "    target_size = (IMAGE_SIZE, IMAGE_SIZE),\n",
        "    batch_size = BATCH_SIZE,\n",
        "    subset = 'validation'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2939 images belonging to 5 classes.\n",
            "Found 731 images belonging to 5 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlQEhJ2FLboy"
      },
      "source": [
        "## Creating our labels file for classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZSPGYfr-t9e",
        "outputId": "3e281fe8-94a9-44fe-fcc2-c780ccc378b2"
      },
      "source": [
        "labels = '\\n'.join(sorted(train_generator.class_indices.keys()))\n",
        "print(labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "daisy\n",
            "dandelion\n",
            "roses\n",
            "sunflowers\n",
            "tulips\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0KEHUYj_jYK"
      },
      "source": [
        "with open('labels.txt', 'w') as f:\n",
        "    f.write(labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9d2tXro4LhWl"
      },
      "source": [
        "## Building a Model\n",
        "\n",
        "Here, we will use transfer learning to make our predictions accurate. We are gonna use MobileNetV2 model as our base model and build our own model on top of it."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VB5n8cYW_2tq"
      },
      "source": [
        "IMG_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)\n",
        "\n",
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Be-z9uleBHP-"
      },
      "source": [
        "base_model.trainable = False # We dont want to train mobilenet layers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mdXVojgBB0Gr"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "                             base_model,\n",
        "                             tf.keras.layers.Conv2D(64, 3, activation='relu'),\n",
        "                             tf.keras.layers.MaxPool2D((2,2)),\n",
        "                             tf.keras.layers.BatchNormalization(),\n",
        "                             tf.keras.layers.Flatten(),\n",
        "                             tf.keras.layers.Dense(64, activation='relu'),\n",
        "                             tf.keras.layers.Dropout(0.2),\n",
        "                             tf.keras.layers.Dense(5, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlw_TxgbDT32",
        "outputId": "9191fc38-9ed2-4a02-e032-461684338204"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_10\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_224 (Functi (None, 7, 7, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 5, 5, 64)          737344    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 2, 2, 64)          0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 2, 2, 64)          256       \n",
            "_________________________________________________________________\n",
            "flatten_8 (Flatten)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_28 (Dense)             (None, 64)                16448     \n",
            "_________________________________________________________________\n",
            "dropout_18 (Dropout)         (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_29 (Dense)             (None, 5)                 325       \n",
            "=================================================================\n",
            "Total params: 3,012,357\n",
            "Trainable params: 754,245\n",
            "Non-trainable params: 2,258,112\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i006fE_rMJFV"
      },
      "source": [
        "## Compile and Fit the model\n",
        "We will use Adam as our optimizer, also we have 5 different classes so we are gonna use categorical cross entropy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mNmf0_loDVeE"
      },
      "source": [
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(lr=0.001),\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyNjPCzDEvD6",
        "outputId": "1fce4e69-285d-4dbe-96c8-5c2e777bddc6"
      },
      "source": [
        "# Fitting the model\n",
        "EPOCHS = 5 \n",
        "\n",
        "history = model.fit(train_generator,\n",
        "                    epochs=EPOCHS,\n",
        "                    validation_data=val_generator)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "46/46 [==============================] - 20s 361ms/step - loss: 0.7804 - accuracy: 0.7073 - val_loss: 0.4775 - val_accuracy: 0.8523\n",
            "Epoch 2/5\n",
            "46/46 [==============================] - 15s 334ms/step - loss: 0.2121 - accuracy: 0.9289 - val_loss: 0.4167 - val_accuracy: 0.8700\n",
            "Epoch 3/5\n",
            "46/46 [==============================] - 15s 332ms/step - loss: 0.0872 - accuracy: 0.9773 - val_loss: 0.4617 - val_accuracy: 0.8440\n",
            "Epoch 4/5\n",
            "46/46 [==============================] - 15s 328ms/step - loss: 0.0404 - accuracy: 0.9912 - val_loss: 0.4840 - val_accuracy: 0.8427\n",
            "Epoch 5/5\n",
            "46/46 [==============================] - 15s 328ms/step - loss: 0.0263 - accuracy: 0.9954 - val_loss: 0.4941 - val_accuracy: 0.8659\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oaone3_vMg6R"
      },
      "source": [
        "## Saving the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDKhnqhHFYO3",
        "outputId": "c14eff9c-3246-4faa-8c5c-5a5c88045816"
      },
      "source": [
        "# Saving the model\n",
        "tf.saved_model.save(model, '/content/models')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/models/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_g5pdC8MkKh"
      },
      "source": [
        "## Convert the saved model to tflite format so that we can use it in our app"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiQ11IWFJNqL"
      },
      "source": [
        "# Convert the model to tflite\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model('/content/models')\n",
        "\n",
        "tflite_model = converter.convert()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGh9TvORJ4On"
      },
      "source": [
        "with open('flower_model.tflite', 'wb') as f:\n",
        "    f.write(tflite_model)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEt25hzwMvHp"
      },
      "source": [
        "## Finally Download the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "I4HZYsOhKB_6",
        "outputId": "ec7e4d25-b88d-4150-838e-4a3ff97206e5"
      },
      "source": [
        "from google.colab import files\n",
        "files.download('flower_model.tflite')\n",
        "files.download('labels.txt')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "      for await (const message of channel.messages) {\n",
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              "        channel.send({})\n",
              "        if (message.buffers) {\n",
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              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
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              "  "
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              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
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              "        // Send a message to notify the kernel that we're ready.\n",
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              "download(\"download_7b6e1c21-f8d2-4534-abcc-7b424554f205\", \"labels.txt\", 39)"
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}