Complete 5 ResNet Deep Learning Project From Scratch 2025

Name: Complete 5 ResNet Deep Learning Project From Scratch 2025
Rating: 2.7 (9 reviews)

Complete Deep Learning Project with ResNet | 5 Deep Learning Projects From Scratch | Hands-On Deep Learning Project

Created byARUNNACHALAM SHANMUGARAAJAN

Last updated 5/2025

English

English [Auto],

What you'll learn

Understanding ResNet architecture
Preparing and augmenting datasets
Fine-tuning ResNet for various applications.
Evaluating model performance with metrics and techniques.

Course content

4 sections • 25 lectures • 1h 19m total length

Introduction To Project0:42
Launch a complete ResNet-based facial image prediction project by importing and pre-processing data, building and compiling a ResNet-50 model, training it, and using the trained model to predict facial images.
Face Class 1 : Import Packages4:51
Create a Google Colab notebook with a t4 gpu for facial image prediction using ResNet 50, then import cv2, os, numpy, and TensorFlow Keras tools.
Face Class 2 : Import Dataset6:33
Load and preprocess a face dataset from Google Drive, enumerate label folders, read and resize images with cv2, build numpy arrays for images and labels, and convert to float32.
Face Class 3 : Build ResNet Model4:13
Build a ResNet 50 model for the dataset by defining a buildResNet function with input shape and classes, using ResNet 50 with ImageNet weights and include_top false, then freeze base.
Face Class 4 : Train Dataset Using ResNet Model8:13
Train a three-class face recognition dataset using ResNet50, with train/test split, image data generator, and 30 epochs to reach about 81% accuracy.
Face Class 5 : Output & Conclusion3:52
save the trained model in keras format as face.h5, reload it, and predict face images using a predict function with cv2, numpy, and a label map.

Introduction To Project0:37
Build a yoga pose prediction model with ResNet by importing the dataset and training with ResNet Atlas, guiding beginners through a complete project from scratch with multiple modules.
Yoga Class 1 : Import Packages2:35
Learn how to set up a yoga pose estimation project with ResNet in Google Colab, connect Google Drive, choose GPU runtime, and import essential packages like pandas, numpy, fastai.
Yoga Class 2 : Import Dataset1:23
Import and organize the yoga pose dataset in Google Drive for the yoga pose detection project, setting the dataset path and preparing for image classification.
Yoga Class 3 : Dataset Classification3:06
Build an image classification workflow using data blocks to classify yoga poses—dandasana, padmasana, vajrasana—by sourcing images, splitting 80/20 for train and validation, labeling by folder, resizing, and batching with transforms.
Yoga Class 4 : Train Dataset Using ResNet Model3:25
Train a yoga pose dataset with ResNet50 in a Google Colab workflow, loading data, setting batch sizes, training for 30 epochs, and exporting the trained model.
Yoga Class 5 : Output & Conclusion2:43
Complete a yoga pose detection project with ResNet50 to predict poses such as Dandasana, Vajrasana, and Padmasana from image paths.

Introduction To Project0:39
Launch into sign language prediction with ResNet in this complete machine learning project course, where you create datasets, train them with ResNet Atlas, and predict outputs.
Sign Class 1 : Import Packages1:55
Import packages for sign language prediction with ResNet in Google Colab, including fastai vision and path, then run the notebook after setting the runtime to t4 GPU to prepare dataset.
Sign Class 2 : Import Dataset1:39
Import the dataset from Google Drive, ensure it's connected and contains sign language image folders. Set the data set variable to the dataset path, paste and run for image classification.
Sign Class 3 : Dataset Classification1:39
Connect Google Drive to access a sign language dataset organized by letter folders. Copy the dataset path, paste it into code, and import the data for image classification.
Sign Class 4 : Train Dataset Using ResNet Model3:29
Learn to train a sign language dataset with ResNet-50, configure batch size and epochs, monitor accuracy, and export the model as a pkl file.
Sign Class 5 : Output & Conclusion2:13
Complete a sign language prediction project with a trained ResNet model, predict and print outputs from image paths, verify accuracy, and conclude the project.

Introduction To Project0:31
Explore building a sign language prediction project with ResNet, including data import and training with ResNet Atlas, and learn how to predict outputs for your first ResNet project.
Traffic Class 1 : Import Packages1:47
Set up a Google Colab project for traffic sign detection using ResNet. Create a new notebook, enable GPU runtime, and import pandas as pd and fastai.vision.all to prepare the dataset.
Traffic Class 2 : Import Dataset1:11
Import the traffic sign dataset for a ResNet project from Google Drive or Kaggle, with class folders and a copied path to run the code.
Traffic Class 3 : Dataset Classification2:49
Classify traffic scene images by building a data block with image and category blocks, labeling by folder names like bus stop and crossroad, for ResNet training.
Traffic Class 4 : Train Dataset Using ResNet Model3:34
Train a dataset with Resnet50, configure a data loader with batch size eight, train with learn.find_tune for the specified epoch, monitor accuracy, and export the model as traffic.pkl.
Traffic Class 5 : Output & Conclusion2:13
Complete a traffic sign detection project with ResNet, using a trained model to predict images by path and display the resulting predictions, such as bus stop.
Complete Pnemonia Prediction Project Using ResNet13:28
Build a pneumonia detection model with ResNet 50 by importing data, creating a two-folder dataset (normal and pneumonia), training with fastai in a Jupyter notebook, and predicting outcomes.
Human Pose Prediction Using ResNet Project

Requirements

Basic Python & Deep Learning Is Required

Description

Welcome to the ultimate course on Deep Learning Project focused on ResNet architecture – master 5 complete Deep Learning Projects from scratch.

This course guides you step-by-step through building and training 5 powerful Deep Learning Projects using ResNet models. Whether you are a beginner or have some experience, this course covers practical techniques and project implementations for real-world Deep Learning Projects.

You will gain hands-on experience in designing, training, and evaluating ResNet-based Deep Learning Projects applicable to image recognition and computer vision tasks.

By the end of this course, you will have successfully completed 5 advanced Deep Learning Projects and gained the confidence to tackle more complex deep learning challenges.

Projects Covered:

Image Classification: Build a ResNet model for multi-class image classification tasks.
Object Detection: Integrate ResNet with YOLO or similar frameworks for object detection.
Medical Image Analysis: Develop a ResNet model for detecting diseases from medical imaging datasets.
Image Segmentation: Use ResNet as a backbone for segmenting objects in complex images.
Facial Recognition System: Train a ResNet model for accurate facial recognition.

This course is ideal for:

AI and Machine Learning Practitioners: Professionals seeking hands-on experience in applying ResNet to real-world problems.
Software Developers: Developers wanting to transition into AI or enhance their skills in computer vision projects.
Data Scientists: Experts looking to expand their knowledge of ResNet for image analysis and related applications.

By the end, you’ll have a robust understanding of ResNet and the ability to implement it in diverse applications.

Who this course is for:

Students and Researchers
Aspiring Deep Learning Enthusiasts

Complete 5 ResNet Deep Learning Project From Scratch 2025

What you'll learn

Explore related topics

Course content

Introduction To Facial Image Prediction Project Using ResNet6 lectures • 28min

Yoga Pose Prediction Project Using ResNet Model6 lectures • 14min

Sign Language Prediction Project Using ResNet Model6 lectures • 12min

Traffic Sign Prediction Project Using ResNet Model7 lectures • 26min

Requirements

Description

Who this course is for: