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Learn how to detect sign language using image classification with the American Sign Language dataset. With 87,000 images and 29 classes, including real-time applications, this project is perfect for beginners.

Sign Language Detection - An Image Classification Project


The data set is a collection of images of alphabets from the American Sign Language, separated in 29 folders which represent the various classes.


The training data set contains 87,000 images which are 200x200 pixels. There are 29 classes, of which 26 are for the letters A-Z and 3 classes for SPACE, DELETE and NOTHING. These 3 classes are very helpful in real-time applications, and classification. The test data set contains a mere 28 images, to encourage the use of real-world test images.


Importing important libraries and project creation.

import os
import torch
import torchvision
import tarfile
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from torchvision.datasets.utils import download_url
from torchvision.datasets import ImageFolder
from import DataLoader
import torchvision.transforms as tt
from import random_split
from torchvision.utils import make_grid
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

matplotlib.rcParams['figure.facecolor'] = '#ffffff'

Retrieving sign language dataset from Kaggle datasets.