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Gender Recognition
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{
"cells": [
{
"cell_type": "markdown",
"id": "c12accd7",
"metadata": {
"papermill": {
"duration": 0.006949,
"end_time": "2024-07-20T13:06:48.972169",
"exception": false,
"start_time": "2024-07-20T13:06:48.965220",
"status": "completed"
},
"tags": []
},
"source": [
"## **Disclaimer**\n",
"**This is a personnal notebook, comments are AI generated (but code is mine). Do not rely on them.**"
]
},
{
"cell_type": "markdown",
"id": "5f95d640",
"metadata": {
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"start_time": "2024-07-20T13:06:48.978355",
"status": "completed"
},
"tags": []
},
"source": [
"# Image Classification with PyTorch: Gender Recognition\n",
"\n",
"## 1. Introduction\n",
"\n",
"Welcome to this Kaggle notebook on image classification using PyTorch! In this tutorial, we'll be tackling the task of gender classification using facial images. Our objective is to build, train, and evaluate a Convolutional Neural Network (CNN) that can accurately predict whether an image contains a male or female face.\n",
"\n",
"This notebook is designed to guide you through the entire process of creating an image classification model, from data preparation to model evaluation. We'll cover important concepts in deep learning and computer vision, making this an excellent resource for students and practitioners alike.\n",
"\n",
"Let's get started!"
]
},
{
"cell_type": "markdown",
"id": "78b60c00",
"metadata": {
"papermill": {
"duration": 0.006885,
"end_time": "2024-07-20T13:06:48.996820",
"exception": false,
"start_time": "2024-07-20T13:06:48.989935",
"status": "completed"
},
"tags": []
},
"source": [
"## 2. Setup\n",
"\n",
"First, we need to import the necessary libraries and set up our environment. Each library we're using serves a specific purpose in our machine learning pipeline."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1babc866",
"metadata": {
"execution": {
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},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of images: 1195\n",
"Sample image path: /kaggle/input/gender-recognizer/dataset/MEN/20240705_112131.jpg\n",
"Sample image path 2: /kaggle/input/gender-recognizer/dataset/WOMAN/0269.jpg\n"
]
}
],
"source": [
"# Importing necessary libraries\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pathlib import Path\n",
"from PIL import Image\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.utils.data import Dataset, DataLoader\n",
"import torchvision.transforms as transforms\n",
"import torchvision.models as models\n",
"import torch.nn.functional as F\n",
"from tqdm.notebook import tqdm\n",
"from collections import Counter\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from torchvision.models import resnet18\n",
"\n",
"# Setting the random seed for reproducibility\n",
"torch.manual_seed(42)\n",
"np.random.seed(42)\n",
"\n",
"# Setting the paths\n",
"TRAIN_PATH = Path(\"/kaggle/input/gender-recognizer/dataset\")\n",
"IMAGE_PATH_LIST = list(TRAIN_PATH.glob(\"*/*.jpg\"))\n",
"\n",
"print(f\"Total number of images: {len(IMAGE_PATH_LIST)}\")\n",
"print(f\"Sample image path: {IMAGE_PATH_LIST[0]}\")\n",
"print(f\"Sample image path 2: {IMAGE_PATH_LIST[-1]}\")"
]
},
{
"cell_type": "markdown",
"id": "d9429213",
"metadata": {
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"duration": 0.00566,
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},
"tags": []
},
"source": [
"Let's break down the purpose of each imported library:\n",
"\n",
"- `os`, `pathlib`: For handling file and directory paths\n",
"- `numpy`, `pandas`: For numerical operations and data manipulation\n",
"- `PIL`: For opening and processing images\n",
"- `sklearn`: For data splitting and evaluation metrics\n",
"- `torch`, `torch.nn`, `torch.optim`: Core PyTorch libraries for building and training neural networks\n",
"- `torchvision.transforms`, `torchvision.models`: For image transformations and pre-trained models\n",
"- `tqdm`: For progress bars during training\n",
"- `matplotlib`, `seaborn`: For creating visualizations"
]
},
{
"cell_type": "markdown",
"id": "aa2f817b",
"metadata": {
"papermill": {
"duration": 0.005752,
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"status": "completed"
},
"tags": []
},
"source": [
"## 3. Data Preparation\n",
"\n",
"### 3.1 Custom Dataset Class\n",
"\n",
"We'll create a custom `GenderDataset` class that inherits from `torch.utils.data.Dataset`. This class will handle loading and preprocessing our images."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "46eabdb4",
"metadata": {
"execution": {
"iopub.execute_input": "2024-07-20T13:06:56.929112Z",
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"shell.execute_reply": "2024-07-20T13:06:57.205039Z"
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"papermill": {
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"end_time": "2024-07-20T13:06:57.207966",
"exception": false,
"start_time": "2024-07-20T13:06:56.920946",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class distribution:\n",
"Female (0): 549\n",
"Male (1): 646\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"class GenderDataset(Dataset):\n",
" def __init__(self, image_paths, transform=None):\n",
" self.image_paths = image_paths\n",
" self.transform = transform\n",
" self.labels = [0 if 'woman' in str(path).lower() else 1 for path in image_paths]\n",
" \n",
" def __len__(self):\n",
" return len(self.image_paths)\n",
" \n",
" def __getitem__(self, idx):\n",
" img_path = self.image_paths[idx]\n",
" image = Image.open(img_path).convert(\"RGB\")\n",
" label = self.labels[idx]\n",
" \n",
" if self.transform:\n",
" image = self.transform(image)\n",
" \n",
" return image, label\n",
"\n",
"# Let's check the distribution of our dataset\n",
"label_counts = Counter(0 if 'woman' in str(path).lower() else 1 for path in IMAGE_PATH_LIST)\n",
"print(\"Class distribution:\")\n",
"print(f\"Female (0): {label_counts[0]}\")\n",
"print(f\"Male (1): {label_counts[1]}\")\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"plt.bar(['Female', 'Male'], [label_counts[0], label_counts[1]])\n",
"plt.title('Class Distribution')\n",
"plt.ylabel('Number of Images')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "70181458",
"metadata": {
"papermill": {
"duration": 0.006345,
"end_time": "2024-07-20T13:06:57.221049",
"exception": false,
"start_time": "2024-07-20T13:06:57.214704",
"status": "completed"
},
"tags": []
},
"source": [
"The `GenderDataset` class does the following:\n",
"1. Stores the list of image paths and the transformation pipeline\n",
"2. Creates labels based on the image filenames (1 for male, 0 for female)\n",
"3. Implements `__len__` to return the total number of images\n",
"4. Implements `__getitem__` to load and preprocess an image when indexed"
]
},
{
"cell_type": "markdown",
"id": "41dd8318",
"metadata": {
"papermill": {
"duration": 0.006229,
"end_time": "2024-07-20T13:06:57.233747",
"exception": false,
"start_time": "2024-07-20T13:06:57.227518",
"status": "completed"
},
"tags": []
},
"source": [
"### 3.2 Data Augmentation and Normalization\n",
"\n",
"Data augmentation helps our model generalize better by exposing it to various transformations of the input data. Normalization ensures that our input features are on a similar scale, which can help with model convergence."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8191e5e6",
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"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of training samples: 956\n",
"Number of validation samples: 239\n"
]
}
],
"source": [
"# Data augmentation and normalization\n",
"transform = transforms.Compose([\n",
" transforms.Resize((224, 224)),\n",
" # Changed to 224x224 for ResNet transforms.RandomHorizontalFlip(),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
"])\n",
"\n",
"# Splitting the data into train and validation sets\n",
"train_paths, val_paths = train_test_split(IMAGE_PATH_LIST, test_size=0.2, random_state=42)\n",
"train_dataset = GenderDataset(train_paths, transform=transform)\n",
"val_dataset = GenderDataset(val_paths, transform=transform)\n",
"\n",
"train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
"val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n",
"\n",
"print(f\"Number of training samples: {len(train_dataset)}\")\n",
"print(f\"Number of validation samples: {len(val_dataset)}\")"
]
},
{
"cell_type": "markdown",
"id": "a50547c4",
"metadata": {
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"duration": 0.006613,
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"tags": []
},
"source": [
"Let's break down our transformation pipeline:\n",
"1. `Resize`: Ensures all images are the same size (224x224 pixels)\n",
"2. `RandomHorizontalFlip`: Randomly flips images horizontally, increasing dataset variety\n",
"3. `ToTensor`: Converts images to PyTorch tensors\n",
"4. `Normalize`: Normalizes the image using mean and std dev values typical for ImageNet"
]
},
{
"cell_type": "markdown",
"id": "de3663e2",
"metadata": {
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"duration": 0.006455,
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},
"source": [
"## 4. Model Definition\n",
"\n",
"### 4.1 Using Pre-trained ResNet\n",
"\n",
"Instead of building a CNN from scratch, we'll use a pre-trained ResNet model from `torchvision.models` and fine-tune it for our classification task. Pre-trained models have been trained on large datasets like ImageNet and can be adapted to specific tasks with transfer learning."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b633c6a7",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ready to be used\n"
]
}
],
"source": [
"# Initialize model architecture without pre-trained weights\n",
"resnet = resnet18(weights=None)\n",
"\n",
"# Load the model weights from the provided file\n",
"model_weights_path = '/kaggle/input/resnet-18/pytorch/resnet18/1/resnet18-f37072fd.pth'\n",
"resnet.load_state_dict(torch.load(model_weights_path))\n",
"\n",
"# Freeze the convolutional base\n",
"for param in resnet.parameters():\n",
" param.requires_grad = False\n",
"\n",
"# Modify the final layer for binary classification\n",
"num_ftrs = resnet.fc.in_features\n",
"resnet.fc = nn.Linear(num_ftrs, 1) # Binary classification (male vs. female)\n",
"\n",
"# Move the model to the GPU (if available)\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"resnet = resnet.to(device)\n",
"\n",
"print(\"Ready to be used\")"
]
},
{
"cell_type": "markdown",
"id": "d774f456",
"metadata": {
"papermill": {
"duration": 0.006391,
"end_time": "2024-07-20T13:06:58.294276",
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"tags": []
},
"source": [
"### 4.2 Loss Function and Optimizer\n",
"\n",
"We'll use the Binary Cross-Entropy loss function and the Adam optimizer for training our model."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3d8cc197",
"metadata": {
"execution": {
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"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimizer and criterion setuped.\n"
]
}
],
"source": [
"criterion = nn.BCEWithLogitsLoss() # Binary Cross-Entropy loss\n",
"optimizer = optim.Adam(resnet.fc.parameters(), lr=0.001) # Only train the final layer\n",
"print('Optimizer and criterion setuped.')"
]
},
{
"cell_type": "markdown",
"id": "5cf6f005",
"metadata": {
"papermill": {
"duration": 0.006403,
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},
"source": [
"## 5. Training the Model\n",
"\n",
"### 5.1 Training Loop\n",
"\n",
"We'll implement the training loop to train our model for a specified number of epochs. During each epoch, we'll iterate over the training DataLoader, compute the loss, and update the model parameters."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3c671227",
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"outputs": [
{
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"text/plain": [
"Training Epoch 1/20: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
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{
"name": "stdout",
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"text": [
"Epoch [1/20], Training Loss: 0.5775, Validation Loss: 0.4465, Validation Accuracy: 0.8536\n",
"Best model saved!\n"
]
},
{
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"Training Epoch 2/20: 0%| | 0/30 [00:00<?, ?it/s]"
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{
"name": "stdout",
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"text": [
"Epoch [2/20], Training Loss: 0.3920, Validation Loss: 0.3413, Validation Accuracy: 0.8828\n",
"Best model saved!\n"
]
},
{
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"text/plain": [
"Training Epoch 3/20: 0%| | 0/30 [00:00<?, ?it/s]"
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},
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{
"name": "stdout",
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"text": [
"Epoch [3/20], Training Loss: 0.3134, Validation Loss: 0.2870, Validation Accuracy: 0.9205\n",
"Best model saved!\n"
]
},
{
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"text/plain": [
"Training Epoch 4/20: 0%| | 0/30 [00:00<?, ?it/s]"
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},
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"text": [
"Epoch [4/20], Training Loss: 0.2759, Validation Loss: 0.2594, Validation Accuracy: 0.9247\n",
"Best model saved!\n"
]
},
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"text/plain": [
"Training Epoch 5/20: 0%| | 0/30 [00:00<?, ?it/s]"
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},
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"text": [
"Epoch [5/20], Training Loss: 0.2350, Validation Loss: 0.2400, Validation Accuracy: 0.9247\n"
]
},
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"text/plain": [
"Training Epoch 6/20: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
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"text": [
"Epoch [6/20], Training Loss: 0.2258, Validation Loss: 0.2307, Validation Accuracy: 0.9121\n"
]
},
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"text/plain": [
"Training Epoch 7/20: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
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"text": [
"Epoch [7/20], Training Loss: 0.2101, Validation Loss: 0.2190, Validation Accuracy: 0.9289\n",
"Best model saved!\n"
]
},
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"text/plain": [
"Training Epoch 8/20: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
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},
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"text": [
"Epoch [8/20], Training Loss: 0.1935, Validation Loss: 0.2123, Validation Accuracy: 0.9247\n"
]
},
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"text/plain": [
"Training Epoch 9/20: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
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},
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"output_type": "stream",
"text": [
"Epoch [9/20], Training Loss: 0.1835, Validation Loss: 0.2096, Validation Accuracy: 0.9247\n"
]
},
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"text/plain": [
"Training Epoch 10/20: 0%| | 0/30 [00:00<?, ?it/s]"
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},
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"text": [
"Epoch [10/20], Training Loss: 0.1876, Validation Loss: 0.2053, Validation Accuracy: 0.9289\n"
]
},
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"text/plain": [
"Training Epoch 11/20: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
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"text": [
"Epoch [11/20], Training Loss: 0.1737, Validation Loss: 0.2036, Validation Accuracy: 0.9289\n"
]
},
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"text/plain": [
"Training Epoch 12/20: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
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{
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"text": [
"Epoch [12/20], Training Loss: 0.1600, Validation Loss: 0.1991, Validation Accuracy: 0.9331\n",
"Best model saved!\n"
]
},
{
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"text/plain": [
"Training Epoch 13/20: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
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},
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"text": [
"Epoch [13/20], Training Loss: 0.1528, Validation Loss: 0.1933, Validation Accuracy: 0.9331\n"
]
},
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