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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "dWyPGNkCGhIX"
},
"source": [
"# Part I : Create Your Own Dataset and Train it with ConvNets\n",
"\n",
"In this part of the notebook, you will set up your own dataset for image classification. Please specify \n",
"under `queries` the image categories you are interested in. Under `limit` specify the number of images \n",
"you want to download for each image category. \n",
"\n",
"You do not need to understand the class `simple_image_download`, just execute the cell after you have specified \n",
"the download folder.\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "8rckz3ZuGhIc",
"outputId": "6f615f06-759a-4eea-839e-658155df8d36"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 2 image links\n",
"Saved 2 images\n",
"Found 2 image links\n",
"Saved 2 images\n",
"Found 2 image links\n",
"Saved 2 images\n",
"Found 2 image links\n",
"Saved 2 images\n",
"Found 2 image links\n",
"Saved 2 images\n",
"Found 2 image links\n",
"Saved 2 images\n",
"Found 2 image links\n",
"Saved 2 images\n",
"Found 2 image links\n",
"ERROR - Could not save https://upload.wikimedia.org/wikipedia/commons/5/59/Marion_Cotillard_at_2019_Cannes.jpg - cannot identify image file <_io.BytesIO object at 0x7f1a0b4d6d70>\n",
"Saved 1 images\n"
}
],
"from selenium import webdriver\n",
"from selenium.webdriver.firefox.options import Options\n",
"from Image_crawling import Image_crawling\n",
"queries = [\"brad pitt\",\"johnny depp\", \"leonardo dicaprio\", \"robert de niro\", \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
"#queries = [\"Bart Simpson\",\"Homer Simpson\"]\n",
"download_folder = \"./brandnew_images/train/\"\n",
"waittime = 0.1 # Time to wait between actions, depends on the number of pictures you want to crawl. More pictures means you need to wait longer for them to load. \n",
"# Set options\n",
"options = webdriver.FirefoxOptions()\n",
"options.add_argument('--headless')\n",
"# Create Driver\n",
"driver = webdriver.Firefox(options=options, executable_path=\"/usr/bin/geckodriver\")\n",
"# create instance of crawler\n",
"image_crawling = Image_crawling(driver, waittime=waittime)\n",
"for query in queries:\n",
" # Craws image urls:\n",
" image_urls = image_crawling.fetch_image_urls(query, limit)\n",
" \n",
" # download images\n",
" image_crawling.download_image(download_folder + query)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "CRHl9UX6GhIs"
},
"source": [
"Please check carefully the downloaded images, there may be a lot of garbage! You definitely need to \n",
"clean the data.\n",
"\n",
"In the following, you will apply data augmentation to your data set."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "3SX21FtcGhIu"
},
"outputs": [],
"source": [
"# General imports\n",
"import tensorflow as tf\n",
"tf.compat.v1.enable_eager_execution(\n",
" config=None, device_policy=None, execution_mode=None\n",
")\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Shortcuts to keras if (however from tensorflow)\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
"from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
"from tensorflow.keras.callbacks import TensorBoard \n",
"\n",
"# Shortcut for displaying images\n",
"def plot_img(img):\n",
" plt.imshow(img, cmap='gray')\n",
" plt.axis(\"off\")\n",
" plt.show()\n",
" \n",
"# The target image size can be fixed here (quadratic)\n",
"# the ImageDataGenerator() automatically scales the images accordingly (aspect ratio is changed)\n",
"image_size = 150"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "rN_Mp1rmGhI1",
"outputId": "6417b1f9-e7d4-4d56-a213-191f9d17524a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 480 images belonging to 8 classes.\n"
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# These are the class names; this defines the ordering of the classes\n",
"class_names = [\"brad pitt\", \"johnny depp\", \"leonardo dicaprio\", \"robert de niro\",\n",
" \"angelina jolie\", \"sandra bullock\", \"catherine deneuve\", \"marion cotillard\"]\n",
"\n",
"\n",
"# Class ImageDataGenerator() returns an iterator holding one batch of images\n",
"# the constructor takes arguments defining the different image transformations\n",
"# for augmentation purposes (rotation, x-/y-shift, intensity scaling - here 1./255 \n",
"# to scale range to [0, 1], shear, zoom, flip, ... )\n",
"train_datagen = ImageDataGenerator(\n",
" rotation_range=10,\n",
" width_shift_range=0.2,\n",
" height_shift_range=0.2,\n",
" rescale=1./255,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True,\n",
" fill_mode='nearest')\n",
"\n",
"\n",
"dir_iter = train_datagen.flow_from_directory('./train/', \n",
" target_size=(image_size, image_size),\n",
" classes=class_names,\n",
" batch_size=25, class_mode='sparse', shuffle=False)\n",
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"dir_iter[0][1]"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "V2fYccc8GhJF"
},
"source": [
"Before you continue, you need to split the downloaded images into a `train` folder and into a `validation` folder."
]
},
{
"cell_type": "raw",
"metadata": {
"colab_type": "raw",
"id": "VamXG4FoGhJH"
},
"source": [
"./\n",
"├── train\n",
"│ ├── brad pitt\n",
"│ └── johnny deep\n",
"| ├── leonardo di caprio\n",
"| └── ...\n",
"│ \n",
"└── validation\n",
" ├── brad pitt\n",
" ├── johnny deep\n",
" ├── leonardo di caprio\n",
" └── ..."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9322su6vGhJJ"
},
"source": [
"If you want to use the example of this jupyter notebook, you can use the images provided in the ./train and ./validation folders."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "xPqJWgeAGhJL"
},
"source": [
"## Define a ConvNet Model"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "UuJV4JBKGhJO"
},
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"source": [
"batch_size = 20\n",
"num_train_images = 480\n",
"num_valid_images = 80\n",
"num_classes = 8\n",
"\n",
"model_scratch = Sequential()\n",
"model_scratch.add(Conv2D(32, (3, 3), input_shape=(image_size, image_size, 3)))\n",
"model_scratch.add(Activation('relu'))\n",
"model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model_scratch.add(Conv2D(32, (3, 3)))\n",
"model_scratch.add(Activation('relu'))\n",
"model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"model_scratch.add(Conv2D(64, (3, 3)))\n",
"model_scratch.add(Activation('relu'))\n",
"model_scratch.add(MaxPooling2D(pool_size=(2, 2)))\n",
"\n",
"# this converts our 3D feature maps to 1D feature vectors\n",
"model_scratch.add(Flatten()) \n",
"model_scratch.add(Dense(64))\n",
"model_scratch.add(Activation('relu'))\n",
"model_scratch.add(Dropout(0.5))\n",
"model_scratch.add(Dense(num_classes))\n",
"model_scratch.add(Activation('softmax'))\n",
"\n",
"model_scratch.compile(loss='categorical_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "JFdkIokMGhJT",
"outputId": "63e7d032-4083-4fe0-d970-c10bf0c39a94"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 480 images belonging to 8 classes.\n",
"Found 80 images belonging to 8 classes.\n"
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}
],
"source": [
"# This is the augmentation configuration we will use for training\n",
"train_datagen = ImageDataGenerator(\n",
" rescale=1./255,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True)\n",
"\n",
"# This is the augmentation configuration we will use for validation:\n",
"# only rescaling\n",
"validation_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
"# This is a generator that will read pictures found in\n",
"# subfolers of './train', and indefinitely generate\n",
"# batches of augmented image data\n",
"train_generator = train_datagen.flow_from_directory(\n",
" './train', # this is the target directory\n",
" target_size=(image_size, image_size), # all images will be resized to 150x150\n",
" classes=class_names,\n",
" batch_size=batch_size) \n",
"\n",
"# This is a similar generator, for validation data\n",
"validation_generator = validation_datagen.flow_from_directory(\n",
" './validation',\n",
" target_size = (image_size, image_size),\n",
" classes = class_names,\n",
" batch_size = batch_size)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "cytHiQUTGhJb"
},
"outputs": [],
"source": [
"logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
"tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "C7dCbyXPGhJg",
"outputId": "98b4085e-ed6d-43e2-831f-aec32161583f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2/24 [=>............................] - ETA: 34s - loss: 2.1133 - accuracy: 0.0500 "
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/PIL/Image.py:952: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
" \"Palette images with Transparency expressed in bytes should be \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"24/24 [==============================] - 33s 1s/step - loss: 2.0931 - accuracy: 0.1250 - val_loss: 2.0772 - val_accuracy: 0.1500\n",
"18/24 [=====================>........] - ETA: 6s - loss: 2.0769 - accuracy: 0.1333"
]
}
],
"source": [
"history = model_scratch.fit(\n",
" train_generator,\n",
" steps_per_epoch = num_train_images // batch_size,\n",
" epochs = 20,\n",
" validation_data = validation_generator,\n",
" validation_steps = num_valid_images // batch_size,\n",
" callbacks = [tensorboard_callback])"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "wt_ONw5PGhJm",
"outputId": "e75d8a73-da49-4dbe-ffcf-7cb316be39a2"
},
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"source": [
"plt.plot(history.history['accuracy'])\n",
"plt.plot(history.history['val_accuracy'])\n",
"plt.title('model accuracy')\n",
"plt.ylabel('accuracy')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='lower right')\n",
"plt.show()\n",
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])\n",
"plt.title('model loss')\n",
"plt.ylabel('loss')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='upper right')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tensorboard"
]
},
{
"cell_type": "code",
"# Load the TensorBoard notebook extension\n",
"os.makedirs(logdir, exist_ok=True)\n",
"%tensorboard --logdir logs"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Y8oAT4oUGhJs"
},
"source": [
"# Part II : Transfer Learning\n",
"\n",
"\n",
"Having to train an image-classification model using very little data is a common situation,\n",
"which you’ll likely encounter in practice if you ever do computer vision in a\n",
"professional context. A “few” samples can mean anywhere from a few hundred to a\n",
"few tens of thousands of images. As a practical example, we’ll focus on classifying\n",
"560 images belongig to 8 actors. We’ll use 480 pictures for training, and 80 for validation.\n",
"\n",
"## 2.1 Feature Extraction with a Pretrained Model\n",
"Feature extraction consists of using the representations learned by a previously\n",
"trained model to extract interesting features from new samples. These features are\n",
"then run through a new classifier, which is trained from scratch.\n",
"As you saw previously, ConvNets used for image classification comprise two parts:\n",
"they start with a series of pooling and convolution layers, and they end with a densely\n",
"connected classifier. The first part is called the _convolutional base_ of the model. In the\n",
"case of convnets, feature extraction consists of taking the convolutional base of a previously\n",
"trained network, running the new data through it, and training a new classifier\n",
"on top of the output.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
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"source": [
"from IPython.display import Image\n",
"Image(\"./Images/feature_extraction.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why only reuse the convolutional base? Could we reuse the densely connected\n",
"classifier as well? In general, doing so should be avoided. The reason is that the representations\n",
"learned by the convolutional base are likely to be more generic and, therefore,\n",
"more reusable: the feature maps of a ConvNet are presence maps of generic\n",
"concepts over a picture, which are likely to be useful regardless of the computer vision\n",
"problem at hand. But the representations learned by the classifier will necessarily be\n",
"specific to the set of classes on which the model was trained—they will only contain\n",
"information about the presence probability of this or that class in the entire picture.\n",
"Additionally, representations found in densely connected layers no longer contain any information about where objects are located in the input image; these layers get rid of\n",
"the notion of space, whereas the object location is still described by convolutional feature\n",
"maps. For problems where object location matters, densely connected features\n",
"are largely useless.\n",
"\n",
"\n",
"Note that the level of generality (and therefore reusability) of the representations\n",
"extracted by specific convolution layers depends on the depth of the layer in the\n",
"model. Layers that come earlier in the model extract local, highly generic feature\n",
"maps (such as visual edges, colors, and textures), whereas layers that are higher up\n",
"extract more-abstract concepts (such as “cat ear” or “dog eye”). So if your new dataset\n",
"differs a lot from the dataset on which the original model was trained, you may be better\n",
"off using only the first few layers of the model to do feature extraction, rather than\n",
"using the entire convolutional base.\n",
"\n",
"\n",
"\n",
"In this case, because the ImageNet class set does not contain images of actors, we’ll \n",
"choose not to use the densely connected layers, in order to cover\n",
"the more general case where the class set of the new problem doesn’t overlap the\n",
"class set of the original model. Let’s put this into practice by using the convolutional\n",
"base of the VGG16 network, trained on ImageNet, to extract interesting features\n",
"from actors, and then train a classifier for the 8 actors on top of\n",
"these features.\n",
"\n",
"The VGG16 model, among others, comes prepackaged with Keras. You can import\n",
"it from the `keras.applications` module. Many other image-classification models (all\n",
"pretrained on the ImageNet dataset) are available as part of `keras.applications`:\n",
"\n",
"\n",
"- Xception\n",
"- ResNet\n",
"- MobileNet\n",
"- EfficientNet\n",
"- DenseNet\n",
"- etc.\n",
"\n",
"Let's instantiate the VGG16 model."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4Luec7pbGhJv",
"scrolled": true
},
"outputs": [],
"source": [
"# General imports\n",
"import sys\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import confusion_matrix\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"\n",
"# Shortcuts to keras if (however from tensorflow)\n",
"from tensorflow.keras import applications\n",
"from tensorflow.keras import optimizers\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPool2D\n",
"from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
"from tensorflow.keras.callbacks import TensorBoard \n",
"\n",
"# Shortcut for displaying images\n",
"def plot_img(img):\n",
" plt.imshow(img, cmap='gray')\n",
" plt.axis(\"off\")\n",
" plt.show()\n",
" \n",
"# The target image size can be fixed here (quadratic)\n",
"# The ImageDataGenerator() automatically scales the images accordingly (aspect ratio is changed)\n",
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "eRes_n9BGhJ0"
},
"conv_base = keras.applications.vgg16.VGG16(weights=\"imagenet\",\n",
" include_top=False,\n",
" input_shape=(image_size, image_size, 3))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "vEIWLeqSGhJ5"
},
"source": [
"You pass three arguments to the constructor:\n",
"\n",
"- `weights` specifies the weight checkpoint from which to initialize the model.\n",
"\n",
"- `include_top` refers to including (or not) the densely connected classifier on\n",
"top of the network. By default, this densely connected classifier corresponds to\n",
"the 1'000 classes from ImageNet. Because we intend to use our own densely\n",
"connected classifier (with 8 classes of actors), we don’t need to\n",
"include it.\n",
"- `input_shape` is the shape of the image tensors that we’ll feed to the network.\n",
"This argument is purely optional: if we don’t pass it, the network will be able to\n",
"process inputs of any size. Here we pass it so that we can visualize (in the following\n",
"summary) how the size of the feature maps shrinks with each new convolution\n",
"and pooling layer."
]
},
{
"cell_type": "markdown",
"Here’s the detail of the architecture of the VGG16 convolutional base. It’s similar to\n",
"the simple convnets you’re already familiar with:"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "DBSrhVORGhKH"
},
"source": [
"\n",
"The final feature map (output volume) has shape $(5, 5, 512)$. That's the feature on top of which we will stick a densely connected classifier.\n",
"\n",
"At this point, there are two ways how we could proceed:\n",
"\n",
"- __Approach 1__: Run the convolutional base over our dataset, record its output to a NumPy array\n",
"on disk, and then use this data as input to a standalone, densely connected classifier\n",
"similar to those you saw in Block 4 of this course. This solution is fast and\n",
"cheap to run, because it only requires running the convolutional base once for\n",
"every input image, and the convolutional base is by far the most expensive part\n",
"of the pipeline. But for the same reason, this technique won’t allow us to use\n",
"data augmentation.\n",
"\n",
"- __Approach 2__: Extend the model we have (`conv_base`) by adding `Dense` layers on top, and run\n",
"the whole thing from end to end on the input data. This will allow us to use\n",
"data augmentation, because every input image goes through the convolutional\n",
"base every time it’s seen by the model. But for the same reason, this technique is\n",
"far more expensive than the first.\n",
"We’ll cover both techniques. Let’s walk through the code required to set up the first\n",
"one: recording the output of `conv_base` on our data and using these outputs as inputs\n",
"to a new model."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mlpIDmSCGhKI"
},
"source": [
"### 1. Approach : Fast feature extraction without data augmentation\n",
"We’ll start by extracting features as NumPy arrays by calling the `predict()` method of\n",
"the `conv_base` model on our training, and validation datasets.\n",
"Let’s iterate over our datasets to extract the VGG16 features."
]
},
{
"cell_type": "code",
"from tensorflow.keras.utils import image_dataset_from_directory\n",
"train_dataset = image_dataset_from_directory(\n",
" './train',\n",
" batch_size=32,\n",
" label_mode=\"categorical\")\n",
"validation_dataset = image_dataset_from_directory(\n",
" './validation',\n",
" batch_size=32,\n",
" label_mode=\"categorical\")"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"def get_features_and_labels(dataset):\n",
" all_features = []\n",
" all_labels = []\n",
" for images, labels in dataset:\n",
" preprocessed_images = keras.applications.vgg16.preprocess_input(images)\n",
" features = conv_base.predict(preprocessed_images)\n",
" all_features.append(features)\n",
" all_labels.append(labels)\n",
" return np.concatenate(all_features), np.concatenate(all_labels)\n",
"train_features, train_labels = get_features_and_labels(train_dataset)\n",
"val_features, val_labels = get_features_and_labels(validation_dataset)"
]
},
"Importantly, `predict()` only expects images, not labels, but our current dataset yields\n",
"batches that contain both images and their labels. Moreover, the VGG16 model expects\n",
"inputs that are preprocessed with the function `keras.applications.vgg16.preprocess_input`, which scales pixel values to an appropriate range.\n",
"The extracted features are currently of shape `(samples, 5, 5, 512)`:"
]
},
{
"cell_type": "code",
]
},
{
"cell_type": "markdown",
"And the labels are now referring to the order of the folders"
]
},
{
"cell_type": "code",
]
},
{
"cell_type": "code",
"print(val_features.shape)\n",
"print(val_labels.shape)"
]
},
{
"cell_type": "code",
"# Note the use of the Flatten\n",
"# layer before passing the\n",
"# features to a Dense layer\n",
"x = layers.Flatten()(inputs)\n",
"x = layers.Dense(256)(x)\n",
"x = layers.Dropout(0.7)(x)\n",
"outputs = layers.Dense(8, activation=\"softmax\")(x)\n",
"model = keras.Model(inputs, outputs)"
]
},
{
"cell_type": "code",
"model.summary()"
]
},
{
"cell_type": "code",
"source": [
"model.compile(loss=\"categorical_crossentropy\",\n",
" optimizer=\"rmsprop\",\n",
" metrics=[\"accuracy\"])\n",
"\n",
"logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
"\n",
"\n",
"callbacks = [\n",
" keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
" tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
"]\n",
"\n",
"history = model.fit(\n",
"train_features, train_labels,\n",
"epochs=30,\n",
"validation_data=(val_features, val_labels),\n",
"callbacks=callbacks\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that we’ll also use a `ModelCheckpoint` callback to save the model after each\n",
"epoch. We’ll configure it with the path specifying where to save the file, as well as the\n",
"arguments `save_best_only=True` and `monitor=\"val_loss\"`: they tell the callback to\n",
"only save a new file (overwriting any previous one) when the current value of the\n",
"`val_loss` metric is lower than at any previous time during training. This guarantees\n",
"that your saved file will always contain the state of the model corresponding to its bestperforming\n",
"training epoch, in terms of its performance on the validation data. As a\n",
"result, we won’t have to retrain a new model for a lower number of epochs if we start\n",
"overfitting: we can just reload our saved file."
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let’s look at the loss and accuracy curves during training:"
]
},
{
"cell_type": "code",
"source": [
"plt.plot(history.history['accuracy'])\n",
"plt.plot(history.history['val_accuracy'])\n",
"plt.title('model accuracy')\n",
"plt.ylabel('accuracy')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='lower right')\n",
"plt.show()\n",
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])\n",
"plt.title('model loss')\n",
"plt.ylabel('loss')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='upper right')\n",
"plt.show()"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"We reach a validation accuracy of about 32% — much worse than we achieved in the\n",
"previous section with the small model trained from scratch. \n",
"\n",
"The learning curves indicate that we’re overfitting almost from the start—\n",
"despite using dropout with a fairly large rate. That’s because this technique doesn’t\n",
"use data augmentation, which is essential for preventing overfitting with small image\n",
"datasets."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Tensorboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the TensorBoard notebook extension\n",
"%load_ext tensorboard\n",
"\n",
"%tensorboard --logdir logs"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "DJT-DgHvGhKu"
},
"source": [
"### 2. Approach : Feature Extraction with Data Augmentation\n",
"\n",
"\n",
"Now let’s review the second technique we mentioned for doing feature extraction,\n",
"which is much slower and more expensive, but which allows us to use data augmentation\n",
"during training: creating a model that chains the `conv_base` with a new dense\n",
"classifier, and training it end to end on the inputs.\n",
"In order to do this, we will first freeze the convolutional base. Freezing a layer or set of\n",
"layers means preventing their weights from being updated during training. If we don’t\n",
"do this, the representations that were previously learned by the convolutional base will\n",
"be modified during training. Because the Dense layers on top are randomly initialized,\n",
"very large weight updates would be propagated through the network, effectively\n",
"destroying the representations previously learned.\n",
"\n",
"In Keras, we freeze a layer or model by setting its trainable attribute to `False`. "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "50DF9pH1GhKw"
},
"source": [
"#### Instantiating and freezing the VGG16 convolutional base"
]
},
{
"cell_type": "code",
"conv_base = keras.applications.vgg16.VGG16(weights=\"imagenet\", include_top=False)\n",
"conv_base.trainable = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setting trainable to `False` empties the list of trainable weights of the layer or model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Printing the list of trainable weights before and after freezing"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"conv_base.trainable = True"
]
},
{
"cell_type": "code",
"source": [
"print(\"This is the number of trainable weights before freezing the conv base:\", len(conv_base.trainable_weights))"
]
},
{
"cell_type": "code",
"metadata": {},
"outputs": [],
"source": [
"conv_base.trainable = False"
]
},
{
"cell_type": "code",
"source": [
"print(\"This is the number of trainable weights after freezing the conv base:\", len(conv_base.trainable_weights))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can create a new model that chains together\n",
"\n",
"1. A data augmentation stage\n",
"2. Our frozen convolutional base \n",
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"3. A dense classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Adding a data augmentation stage and a classifier to the convolutional base"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data_augmentation = keras.Sequential(\n",
"[\n",
"layers.RandomFlip(\"horizontal\"),\n",
"layers.RandomRotation(0.1),\n",
"layers.RandomZoom(0.2),\n",
"]\n",
")"
]
},
{
"cell_type": "code",
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"metadata": {},
"outputs": [],
"source": [
"inputs = keras.Input(shape=(180, 180, 3))\n",
"# Apply data augmentation\n",
"x = data_augmentation(inputs)\n",
"# Apply input value scaling\n",
"x = keras.applications.vgg16.preprocess_input(x)\n",
"x = conv_base(x)\n",
"x = layers.Flatten()(x)\n",
"x = layers.Dense(256)(x)\n",
"x = layers.Dropout(0.5)(x)\n",
"outputs = layers.Dense(8, activation=\"softmax\")(x)\n",
"model = keras.Model(inputs, outputs)\n",
"model.compile(loss=\"categorical_crossentropy\",\n",
" optimizer=\"rmsprop\",\n",
" metrics=[\"accuracy\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With this setup, only the weights from the two Dense layers that we added will be\n",
"trained. That’s a total of four weight tensors: two per layer (the main weight matrix\n",
"and the bias vector). \n",
"\n",
"Note that in order for these changes to take effect, you must first\n",
"compile the model. If you ever modify weight trainability after compilation, you\n",
"should then recompile the model, or these changes will be ignored.\n",
"\n",
"Let’s train our model. Thanks to data augmentation, it will take much longer for\n",
"the model to start overfitting, so we can train for more epochs — let’s do 50.\n",
"\n",
"__NOTE__ This technique is expensive enough that you should only attempt it if\n",
"you have access to a GPU (such as the free GPU available in Colab) — it’s\n",
"intractable on CPU. If you can’t run your code on GPU, then the previous\n",
"technique is the way to go."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
"\n",
"\n",
" keras.callbacks.ModelCheckpoint(filepath=\"feature_extraction_with_augmentation.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
" tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
"]\n"
]
},
{
"cell_type": "code",
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"source": [
"history = model.fit(\n",
"train_dataset,\n",
"epochs=50,\n",
"validation_data=validation_dataset,\n",
"callbacks=callbacks)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let’s plot the results again. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(history.history['accuracy'])\n",
"plt.plot(history.history['val_accuracy'])\n",
"plt.title('model accuracy')\n",
"plt.ylabel('accuracy')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='lower right')\n",
"plt.show()\n",
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])\n",
"plt.title('model loss')\n",
"plt.ylabel('loss')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='upper right')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, we reach a validation accuracy of over 98%. This is a strong improvement over the previous model."
]
},
{
"cell_type": "markdown",
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the TensorBoard notebook extension\n",
"%load_ext tensorboard\n",
"%tensorboard --logdir logs"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"## Fine Tuning\n",
"\n",
"Another widely used technique for model reuse, complementary to feature extraction, is _fine-tuning_. \n",
"Fine-tuning consists of unfreezing a few of the top layers of a frozen model base used\n",
"for feature extraction, and jointly training both the newly added part of the model (in this case, the\n",
"fully connected classifier) and these top layers. This is called _fine-tuning_ because it slightly \n",
"adjusts the more abstract representations of the model being reused in order to make them more relevant for the problem at hand.\n",
"\n",
"I stated earlier that it’s necessary to freeze the convolution base of VGG16 in order to be able to\n",
"train a randomly initialized classifier on top. For the same reason, it’s only possible to fine-tune the top\n",
"layers of the convolutional base once the classifier on top has already been trained. If the classifier isn’t\n",
"already trained, the error signal propagating through the network during training will be too\n",
"large, and the representations previously learned by the layers being fine-tuned will be destroyed. Thus\n",
"the steps for fine-tuning a network are as follows:\n",
"The steps for fine-tuning are as follows:\n",
"\n",
"1. Add our custom network on top of an already-trained base network.\n",
"2. Freeze the base network.\n",
"3. Train the part we added.\n",
"4. Unfreeze some layers in the base network. (Note that you should not unfreeze “batch normalization” layers, which are not relevant here since there are no such layers in VGG16. )\n",
"5. Jointly train both these layers and the part we added.\n",
"We already completed the first three steps when doing feature extraction. Let’s proceed with step 4:\n",
"we’ll unfreeze our `conv_base` and then freeze individual layers inside it.\n",
"\n",
"As a reminder, this is what our convolutional base looks like:"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "cnObzTupGhLV",
"outputId": "3754b2b3-8885-44b3-cb87-82612d223ec3"
},
"outputs": [],
"source": [
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"We will fine-tune the last three convolutional layers, which means all layers up to `block4_pool` should be frozen, and the layers `block5_conv1`, `block5_conv2`, and `block5_conv3` should be trainable.\n",
"Why not fine-tune more layers? Why not fine-tune the entire convolutional base?\n",
"You could. But you need to consider the following:\n",
"- Earlier layers in the convolutional base encode more generic, reusable features, whereas layers higher up encode more specialized features. It’s more useful to fine-tune the more specialized features, because these are the ones that need to be repurposed on your new problem. There would be fast-decreasing returns in fine-tuning lower layers.\n",
"- The more parameters you’re training, the more you’re at risk of overfitting. The convolutional base has 15 million parameters, so it would be risky to attempt to train it on your small dataset. \n",
"Thus, in this situation, it’s a good strategy to fine-tune only the top two or three layers in the convolutional base. Let’s set this up, starting from where we left off in the previous example."
]
},
{
"cell_type": "markdown",
"#### Freezing all layers until the fourth from the last"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "tBXYN1t2GhLc",
"outputId": "b33ae8d1-925b-4e8a-f15d-a62356070896"
},
"conv_base.trainable = True\n",
"for layer in conv_base.layers[:-4]:\n",
" layer.trainable = False"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "XWw1mYfUGhLg"
},
"source": [
"Now we can begin fine-tuning the model. We’ll do this with the `RMSprop` optimizer, using a very low learning rate. The reason for using a low learning rate is that we want to limit the magnitude of the modifications we make to the representations of the three\n",
"layers we’re fine-tuning. Updates that are too large may harm these representations."
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4YBjFhSVGhLh",
"outputId": "c688820a-0f28-4aa0-b247-15a9684fa08f"
},
"outputs": [],
"source": [
"model.compile(loss=\"binary_crossentropy\",\n",
" optimizer=keras.optimizers.RMSprop(learning_rate=1e-5),\n",
" metrics=[\"accuracy\"])\n",
"logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
"callbacks = [\n",
" keras.callbacks.ModelCheckpoint(filepath=\"fine_tuning.keras\", save_best_only=True, monitor=\"val_loss\"),\n",
" tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
"]\n",
"\n",
"history = model.fit(train_dataset,\n",
" epochs=30,\n",
" validation_data=validation_dataset,\n",
" callbacks=callbacks)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "9rwSMMQaGhLx",
"outputId": "0a58db5a-0f22-45e8-d1fb-0a664fceaf4d"
},
"outputs": [
{
"data": {
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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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\n",
1336
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(history.history['accuracy'])\n",
"plt.plot(history.history['val_accuracy'])\n",
"plt.title('model accuracy')\n",
"plt.ylabel('accuracy')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='lower right')\n",
"plt.show()\n",
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])\n",
"plt.title('model loss')\n",
"plt.ylabel('loss')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='upper right')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Tensorboard"
]
},
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the TensorBoard notebook extension\n",
"%load_ext tensorboard\n",
"%tensorboard --logdir logs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"validation_generator_no_shuffle = validation_datagen.flow_from_directory(\n",
" './validation',\n",
" target_size=(image_size, image_size),\n",
" batch_size=num_valid_images,\n",
" classes=class_names,\n",
" shuffle=False)\n",
"\n",
"\n",
"prediction = model_fine_tuned.predict_generator(validation_generator_no_shuffle,1)"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "WoDOi_F8GhL5",
"outputId": "17c21c92-2a5d-4e21-c367-57e818046762"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 8 1 0 1 0 0 0 0 ], brad pitt\n",
"[ 1 7 0 1 0 0 1 0 ], johnny deep\n",
"[ 2 0 8 0 0 0 0 0 ], leonardo dicaprio\n",
"[ 0 0 0 9 0 0 0 1 ], robert de niro\n",
"[ 0 0 0 0 8 0 1 1 ], angelina jolie\n",
"[ 0 0 0 0 1 7 0 2 ], sandra bullock\n",
"[ 1 0 0 0 0 1 7 1 ], catherine deneuve\n",
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"[ 0 0 0 0 2 2 0 6 ], marion cotillard\n"
]
}
],
"source": [
"Y_valid = np.zeros((num_valid_images,1),dtype=int)\n",
"\n",
"step = num_valid_images // num_classes\n",
"for ind in range(num_classes):\n",
" Y_valid[ind*step:(ind+1)*step] = ind\n",
" \n",
"confmat = confusion_matrix(Y_valid,np.argmax(prediction,axis=1)) \n",
"\n",
"for i0 in range(num_classes):\n",
" sys.stdout.write('[')\n",
" for i1 in range(num_classes):\n",
" sys.stdout.write('{:3d} '.format(confmat[i0,i1]))\n",
" \n",
" sys.stdout.write('], {}\\n'.format(class_names[i0]))\n",
" \n",
"sys.stdout.flush()"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "nNp0qChLGhL-",
"outputId": "f22e9bfe-e5da-4d57-fbdc-2ea55d6681e7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"wrong classification for: marion cotillard\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"matched to: angelina jolie\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"matched to: sandra bullock\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"# Choose the class label you want to check\n",
"clbl = 7\n",
"step = num_valid_images // num_classes\n",
"pred_labels = np.argmax(prediction[clbl*step:(clbl+1)*step],axis=1)\n",
"wrong_labels = np.transpose(np.nonzero(pred_labels != clbl))\n",
"\n",
"print('wrong classification for: {}'.format(class_names[clbl]))\n",
"for i0 in wrong_labels:\n",
" img = validation_generator_no_shuffle[0][0][clbl*step + i0,...]\n",
" plot_img(img.reshape(150,150,3))\n",
" print('matched to: {}'.format(class_names[pred_labels[i0][0]]))\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KaLGByZgGhMD"
},
"source": [
"## Tensorboard"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {},
"colab_type": "code",
"id": "RdVEKygQGhMF",
"outputId": "2161da8b-9605-4bcc-e60a-6ea83fccd401"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Traceback (most recent call last):\n",
" File \"/opt/conda/bin/tensorboard\", line 5, in <module>\n",
" from tensorboard.main import run_main\n",
" File \"/opt/conda/lib/python3.7/site-packages/tensorboard/main.py\", line 27, in <module>\n",
" from tensorboard import default\n",
" File \"/opt/conda/lib/python3.7/site-packages/tensorboard/default.py\", line 33, in <module>\n",
" from tensorboard.plugins.audio import audio_plugin\n",
" File \"/opt/conda/lib/python3.7/site-packages/tensorboard/plugins/audio/audio_plugin.py\", line 23, in <module>\n",
" from tensorboard import plugin_util\n",
" File \"/opt/conda/lib/python3.7/site-packages/tensorboard/plugin_util.py\", line 24, in <module>\n",
" import markdown\n",
" File \"/opt/conda/lib/python3.7/site-packages/markdown/__init__.py\", line 29, in <module>\n",
" from .core import Markdown, markdown, markdownFromFile # noqa: E402\n",
" File \"/opt/conda/lib/python3.7/site-packages/markdown/core.py\", line 26, in <module>\n",
" from . import util\n",
" File \"/opt/conda/lib/python3.7/site-packages/markdown/util.py\", line 88, in <module>\n",
" INSTALLED_EXTENSIONS = metadata.entry_points(group='markdown.extensions')\n",
"TypeError: entry_points() got an unexpected keyword argument 'group'\n"
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}
],
"source": [
"!tensorboard --port=8061 --logdir=tensorboard/"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "R0dfpdDOGhM2"
},
"source": [
"# Part IV : Object Detection with Mask R-CNN\n",
"\n",
"### Please run this section on Colab !"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "vOAEQt-pGhM3"
},
"source": [
"Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph.\n",
"\n",
"It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are they).\n",
"\n",
"In recent years, deep learning techniques have achieved state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Most notably is the R-CNN, or Region-Based Convolutional Neural Networks, and the most recent technique called Mask R-CNN that is capable of achieving state-of-the-art results on a range of object detection tasks.\n",
"\n",
"In this section, we will discover how to use the __Mask R-CNN__ model to detect objects in new photographs.\n",
"\n",
"After completing this tutorial, you will know:\n",
"\n",
"- The region-based Convolutional Neural Network family of models for object detection and the most recent variation called Mask R-CNN.\n",
"\n",
"- The best-of-breed open source library implementation of the Mask R-CNN for the Keras deep learning library.\n",
" \n",
"- How to use a pre-trained Mask R-CNN to perform object localization and detection on new photographs.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ra-bXlWXGhM4"
},
"source": [
"## Mask R-CNN for Object Detection\n",
"\n",
"Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image.\n",
"\n",
"It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized.\n",
"\n",
"An extension of object detection involves marking the specific pixels in the image that belong to each detected object instead of using coarse bounding boxes during object localization. This harder version of the problem is generally referred to as object segmentation or semantic segmentation.\n",
"\n",
"The __Region-Based__ Convolutional Neural Network, or R-CNN, is a family of convolutional neural network models designed for object detection, developed by Ross Girshick, et al.\n",
"\n",
"There are perhaps four main variations of the approach, resulting in the current pinnacle called Mask R-CNN. The salient aspects of each variation can be summarized as follows:\n",
"\n",
"- __R-CNN__: Bounding boxes are proposed by the “selective search” algorithm, each of which is stretched and features are extracted via a deep convolutional neural network, such as AlexNet, before a final set of object classifications are made with linear SVMs.\n",
"\n",
"- __Fast R-CNN__: Simplified design with a single model, bounding boxes are still specified as input, but a region-of-interest pooling layer is used after the deep CNN to consolidate regions and the model predicts both class labels and regions of interest directly.\n",
" \n",
"- __Faster R-CNN__: Addition of a Region Proposal Network that interprets features extracted from the deep CNN and learns to propose regions-of-interest directly.\n",
" \n",
"- __Mask R-CNN__: Extension of Faster R-CNN that adds an output model for predicting a mask for each detected object.\n",
"\n",
"The Mask R-CNN model introduced in the 2018 paper titled [Mask R-CNN](https://arxiv.org/abs/1703.06870) is the most recent variation of the family models and supports both object detection and object segmentation. The paper provides a nice summary of the model linage to that point:\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "GlXwuVoOGhM7"
},
"source": [
"### Matterport Mask R-CNN Project\n",
"\n",
"Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model.\n",
"\n",
"Source code is available for each version of the R-CNN model, provided in separate GitHub repositories with prototype models based on the Caffe deep learning framework. For example:\n",
"\n",
"- R-CNN: [Regions with Convolutional Neural Network Features, GitHub](https://github.com/rbgirshick/rcnn)\n",
"\n",
"- Fast R-CNN, [GitHub](https://github.com/rbgirshick/fast-rcnn)\n",
"\n",
"- Faster R-CNN Python Code, [GitHub](https://github.com/rbgirshick/py-faster-rcnn)\n",
"\n",
"- Detectron, Facebook AI, [GitHub](https://github.com/facebookresearch/Detectron)\n",
"\n",
"Instead of developing an implementation of the R-CNN or Mask R-CNN model from scratch, we can use a reliable third-party implementation built on top of the Keras deep learning framework.\n",
"\n",
"The best of breed third-party implementations of Mask R-CNN is the [Mask R-CNN](https://github.com/matterport/Mask_RCNN) Project developed by Matterport. The project is open source released under a permissive license (i.e. MIT license) and the code has been widely used on a variety of projects and Kaggle competitions.\n",
"\n",
"Nevertheless, it is an open source project, subject to the whims of the project developers. As such, I have a fork of the project available, just in case there are major changes to the API in the future.\n",
"\n",
"The project is light on API documentation, although it does provide a number of examples in the form of Python Notebooks that you can use to understand how to use the library by example. Two notebooks that may be helpful to review are:\n",
"\n",
"- Mask R-CNN Demo, [Notebook](https://github.com/matterport/Mask_RCNN/blob/master/samples/demo.ipynb)\n",
"\n",
"- Mask R-CNN – Inspect Trained Model, [Notebook](https://github.com/matterport/Mask_RCNN/blob/master/samples/coco/inspect_model.ipynb)\n",
"\n",
"There are perhaps three main use cases for using the Mask R-CNN model with the Matterport library; they are:\n",
"\n",
"- __Object Detection Application__: Use a pre-trained model for object detection on new images.\n",
"\n",
"- __New Model via Transfer Learning__: Use a pre-trained model as a starting point in developing a model for a new object detection dataset.\n",
" \n",
"- __New Model from Scratch__: Develop a new model from scratch for an object detection dataset.\n",
"\n",
"In order to get familiar with the model and the library, we will look at the first example in the next section.\n",
"\n",
"#### Object Detection With Mask R-CNN\n",
"\n",
"In this section, we will use the Matterport Mask R-CNN library to perform object detection on arbitrary photographs.\n",
"\n",
"Much like using a pre-trained deep CNN for image classification, e.g. such as VGG-16 trained on an ImageNet dataset, we can use a pre-trained Mask R-CNN model to detect objects in new photographs. In this case, we will use a Mask R-CNN trained on the [MS COCO object detection problem](http://cocodataset.org/#home).\n",
"\n",
"#### Mask R-CNN Installation\n",
"\n",
"The first step is to install the library.\n",
"\n",
"At the time of writing, there is no distributed version of the library, so we have to install it manually. The good news is that this is very easy.\n",
"\n",
"Installation involves cloning the GitHub repository and running the installation script on your workstation. If you are having trouble, see the [installation instructions](https://github.com/matterport/Mask_RCNN#installation) buried in the library’s readme file.\n",
"\n",
"#### Step 0. Open Colab and Upload this Notebook\n",
"\n",
"#### Step 1. Clone the Mask R-CNN GitHub Repository\n",
"\n",
"This is as simple as running the following command from your command line:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104
},
"colab_type": "code",
"id": "HGiDmuejGhM8",
"outputId": "ce5ca013-96e5-4766-d2ed-b4cde9b3ca94"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'Mask_RCNN'...\n",
"remote: Enumerating objects: 956, done.\u001b[K\n",
"remote: Total 956 (delta 0), reused 0 (delta 0), pack-reused 956\u001b[K\n",
"Receiving objects: 100% (956/956), 111.84 MiB | 30.52 MiB/s, done.\n",
"Resolving deltas: 100% (570/570), done.\n"
]
}
],
"source": [
"!git clone https://github.com/matterport/Mask_RCNN.git"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "S7uXyFVPGhNA"
},
"source": [
"This will create a new local directory with the name Mask_RCNN that looks as follows:"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "raw",
"id": "DhKn5ytcGhNA"
},
"source": [
"Mask_RCNN\n",
"├── assets\n",
"├── build\n",
"│ ├── bdist.macosx-10.13-x86_64\n",
"│ └── lib\n",
"│ └── mrcnn\n",
"├── dist\n",
"├── images\n",
"├── mask_rcnn.egg-info\n",
"├── mrcnn\n",
"└── samples\n",
" ├── balloon\n",
" ├── coco\n",
" ├── nucleus\n",
" └── shapes"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "WvFlDgvJGhNB"
},
"source": [
"#### Step 2. Install the Mask R-CNN Library\n",
"\n",
"The library can be installed directly via pip.\n",
"\n",
"Change directory into the _Mask_RCNN_ directory and run the installation script.\n",
"\n",
"From the command line, type the following:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"colab_type": "code",
"id": "aEUeZhX5GhNB",
"outputId": "be5de5a1-e821-477c-ce28-91bb9f8c3194"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (1.18.2)\n",
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"WARNING:root:Fail load requirements file, so using default ones.\n",
"running install\n",
"running bdist_egg\n",
"running egg_info\n",
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"reading manifest template 'MANIFEST.in'\n",
"writing manifest file 'mask_rcnn.egg-info/SOURCES.txt'\n",
"installing library code to build/bdist.linux-x86_64/egg\n",
"running install_lib\n",
"running build_py\n",
"creating build\n",
"creating build/lib\n",
"creating build/lib/mrcnn\n",
"copying mrcnn/visualize.py -> build/lib/mrcnn\n",
"copying mrcnn/parallel_model.py -> build/lib/mrcnn\n",
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"copying build/lib/mrcnn/visualize.py -> build/bdist.linux-x86_64/egg/mrcnn\n",
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"creating build/bdist.linux-x86_64/egg/EGG-INFO\n",
"copying mask_rcnn.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
"copying mask_rcnn.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\n",
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"zip_safe flag not set; analyzing archive contents...\n",
"creating dist\n",
"creating 'dist/mask_rcnn-2.1-py3.6.egg' and adding 'build/bdist.linux-x86_64/egg' to it\n",
"removing 'build/bdist.linux-x86_64/egg' (and everything under it)\n",
"Processing mask_rcnn-2.1-py3.6.egg\n",
"Removing /usr/local/lib/python3.6/dist-packages/mask_rcnn-2.1-py3.6.egg\n",
"Copying mask_rcnn-2.1-py3.6.egg to /usr/local/lib/python3.6/dist-packages\n",
"mask-rcnn 2.1 is already the active version in easy-install.pth\n",
"\n",
"Installed /usr/local/lib/python3.6/dist-packages/mask_rcnn-2.1-py3.6.egg\n",
"Processing dependencies for mask-rcnn==2.1\n",
"Finished processing dependencies for mask-rcnn==2.1\n"
]
}
],
"source": [
"import os\n",
"os.chdir('./Mask_RCNN')\n",
"!pip3 install -r requirements.txt\n",
"!python3 setup.py install "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "DlySPeHPGhNE"
},
"source": [
"The library will then install directly and you will see a lot of successful installation messages ending with the following:"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "raw",
"id": "nAww1LboGhNF"
},
"source": [
"...\n",
"Finished processing dependencies for mask-rcnn==2.1"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "55X0zSm7GhNG"
},
"source": [
"#### Step 3: Confirm the Library Was Installed\n",
"\n",
"It is always a good idea to confirm that the library was installed correctly.\n",
"\n",
"You can confirm that the library was installed correctly by querying it via the pip command; for example:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"colab_type": "code",
"id": "kKXRZ1vTGhNG",
"outputId": "9f0df55c-755f-4e11-a6c3-e8b7418eefcb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: mask-rcnn\n",
"Version: 2.1\n",
"Summary: Mask R-CNN for object detection and instance segmentation\n",
"Home-page: https://github.com/matterport/Mask_RCNN\n",
"Author: Matterport\n",
"Author-email: waleed.abdulla@gmail.com\n",
"License: MIT\n",
"Location: /usr/local/lib/python3.6/dist-packages/mask_rcnn-2.1-py3.6.egg\n",
"Requires: \n",
"Required-by: \n"
]
}
],
"source": [
"!pip3 show mask-rcnn"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "f0vwUrMcGhNJ"
},
"source": [
"### Example of Object Localization\n",
"\n",
"We are going to use a pre-trained Mask R-CNN model to detect objects on a new photograph.\n",
"\n",
"#### Step 1. Download Model Weights\n",
"\n",
"First, download the weights for the pre-trained model, specifically a Mask R-CNN trained on the MS Coco dataset.\n",
"\n",
"The weights are available from the project GitHub project and the file is about 250 megabytes. Download the model weights to a file with the name ‘mask_rcnn_coco.h5‘ in your current working directory.\n",
"\n",
"[Download Weights (mask_rcnn_coco.h5)](https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5) (246 megabytes)\n",
"\n",
"#### Step 2. Download Sample Photograph\n",
"\n",
"We also need a photograph in which to detect objects.\n",
"\n",
"Download from Ilias the photograph to your current working directory with the filename ‘african-elephant.jpg‘\n",
"\n",
"\n",
"african-elephant.jpg"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "n8ccmDSvGhNK"
},
"source": [
"#### Step 3. Load Model and Make Prediction\n",
"\n",
"First, the model must be defined via an instance MaskRCNN class.\n",
"\n",
"This class requires a configuration object as a parameter. The configuration object defines how the model might be used during training or inference.\n",
"\n",
"In this case, the configuration will only specify the number of images per batch, which will be one, and the number of classes to predict.\n",
"\n",
"You can see the full extent of the configuration object and the properties that you can override in the [config.py](https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/config.py) file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "qAfMaOOzGhNL"
},
"outputs": [],
"source": [
"%tensorflow_version 1.x\n",
"from mrcnn.config import Config\n",
"from mrcnn.model import MaskRCNN\n",
"# define the test configuration\n",
"class TestConfig(Config):\n",
" NAME = \"test\"\n",
" GPU_COUNT = 1\n",
" IMAGES_PER_GPU = 1\n",
" NUM_CLASSES = 1 + 80"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1CmHYT4RGhNN"
},
"source": [
"We can now define the MaskRCNN instance.\n",
"\n",
"We will define the model as type “inference” indicating that we are interested in making predictions and not training. We must also specify a directory where any log messages could be written, which in this case will be the current working directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Sg482-mcGhNO"
},
"outputs": [],
"source": [
"# define the model\n",
"rcnn = MaskRCNN(mode='inference', model_dir='./', config=TestConfig())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install 'h5py==2.10.0' --force-reinstall"
]
},
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{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9BtI50MlGhNR"
},
"source": [
"The next step is to load the weights that we downloaded. You should save it on google drive and then load it."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"colab_type": "code",
"id": "_TWgehzsNOSV",
"outputId": "73225d99-e9df-4d1c-c733-a092c97e336c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 245
},
"colab_type": "code",
"id": "46t9gwLdGhNR",
"outputId": "842b58f4-2678-4ad9-bbcf-aac4656392b7"
},
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