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Updated 24 days ago
Learn how to classify 10 famous personalities image dataset using Residual Network in PyTorch with over 90% accuracy in just 1 minute on a single GPU. Follow our step-by-step guide now!
Classifying 10 Famous Personality Image Dataset using Residual Network in PyTorch
A.K.A. Training an image classifier from scratch to over 90% accuracy in around 1 minute on a single GPU
In this project, we'll use the following techniques to train a state-of-the-art model in around 1 minute to achieve over 90% accuracy in classifying images from the 10 Famous personality Image Dataset,
- Data normalization
- Data augmentation
- Residual connections
- Batch normalization
- Learning rate scheduling
- Weight Decay
- Gradient clipping
- Adam optimizer
project_name='10-famous-personality-classification'import torch
import torchvision
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['figure.facecolor'] = '#ffffff'
%matplotlib inlineFetching Kaggle Datasets into Google Colab
Follow the below step to download kaggle dataset. This applies to any of Kaggle dataset.