Stock Charts Detection Using Image Classification Model ResNet

Stock Charts Detection Using Image Classification Model ResNet

Intro

This tutorial explores image classification in PyTorch using state-of-the-art computer vision models. The dataset used in this tutorial will have 3 classes that are very imbalanced. So, we will explore augmentation as a solution to the imbalance problem.

Data used in this notebook can be found at https://www.nbshare.io/blog/datasets/

Contents:

1. Data loading
   • Loading labels
   • Train-test splitting
   • Augmentation
   • Creating Datasets
   • Random Weighted Sampling and DataLoaders
2. CNN building and fine-tuning ResNet
   • CNN
   • ResNet
3. Setup and training
4. Evaluation
5. Testing

Data Loading

In [1]:

import os
import random
import numpy as np
import pandas as pd
from PIL import Image
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
from torchvision import datasets, models
from torchvision import transforms
import matplotlib.pyplot as plt

Setting the device to make use of the GPU.

In [2]:

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

Out[2]:

device(type='cuda')

Identifying the data paths.

In [4]:

data_dir = "images/"
labels_file = "images_labeled.csv"

Loading Labels

Since the labels are in a CSV file, we use

(continued...)

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