Train a Custom YOLOv5 Model on Google Colab and Detect Objects In Real Time on Windows | Part 3

Weights & Biases ยท Beginner ยท๐Ÿ‘๏ธ Computer Vision ยท4y ago
๐Ÿš€Hey everyone and welcome to the final part of the YOLOv5 series! In this video (Part 3) we'll learn about training a custom YOLOv5 PyTorch model on Google Colab, and using Weights & Biases to understand the training process. We'll start off by discussing the differences between pre-trained and custom-trained models, and how we can train using the custom dataset that we collected in part 2. Then, we'll use Weights & Biases to determine which of the models we trained is the best one, and deploy that model locally on Windows to perform real-time detection using a custom-trained model. --- Links ๐Ÿ“Google Colab to train a custom YOLOv5: https://wandb.me/train-yolov5 ๐Ÿ“ My WandB dashboard from the video: https://wandb.ai/ivangoncharov/custom_yolov5 ๐Ÿ“Artifacts docs: https://docs.wandb.ai/guides/artifacts ๐Ÿ“ COCO dataset: https://cocodataset.org/#home --- You can watch the whole YOLOv5 Series here: ๐Ÿš€Part 0 - Overview of the YOLOv5 and W&B integration: https://youtu.be/yyecuhBmLxERocket ๐Ÿš€Part 1 - Install YOLOv5 on Windows and Google Colab: https://youtu.be/gDoMYuyY_qwRocket ๐Ÿš€Part 2 - Collect & Label a Custom Dataset: https://youtu.be/a9Bre0YJ8L8Rocket ๐Ÿš€Part 3 - Train a Custom YOLOv5 Model to Detect Bus Numbers: https://youtu.be/5h5UtLau3Vc --- Follow Ivan: ๐Ÿ‘‰ Twitter: https://twitter.com/Ivangrov ๐Ÿ‘‰ YouTube: https://www.youtube.com/c/IvanGoncharovAI --- โณ Timestamps โณ 00:00 Intro 00:50 How to think about training a YOLOv5 to detect new classes 03:28 Training a custom YOLOv5 model on Google Colab 08:23 How Weights & Biases helps you understand the training process 12:12 Training a large YOLOv5 model 16:18 Deploying a custom-trained model on Windows 18:28 Outro
Watch on YouTube โ†— (saves to browser)
Sign in to unlock AI tutor explanation ยท โšก30

Playlist

Uploads from Weights & Biases ยท Weights & Biases ยท 0 of 60

โ† Previous Next โ†’
1 0. What is machine learning?
0. What is machine learning?
Weights & Biases
2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
Weights & Biases
3 Intro to ML: Course Overview
Intro to ML: Course Overview
Weights & Biases
4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
Weights & Biases
5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
Weights & Biases
6 Weights & Biases at OpenAI
Weights & Biases at OpenAI
Weights & Biases
7 Why Experiment Tracking is Crucial to OpenAI
Why Experiment Tracking is Crucial to OpenAI
Weights & Biases
8 4. Autoencoders
4. Autoencoders
Weights & Biases
9 5. Sentiment Analysis
5. Sentiment Analysis
Weights & Biases
10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
Weights & Biases
11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
Weights & Biases
12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
Weights & Biases
13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
Weights & Biases
14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
Weights & Biases
15 Weights and Biases - Developer Tools for Deep Learning
Weights and Biases - Developer Tools for Deep Learning
Weights & Biases
16 Introducing Weights & Biases
Introducing Weights & Biases
Weights & Biases
17 10. Seq2Seq Models
10. Seq2Seq Models
Weights & Biases
18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
Weights & Biases
19 12. One-shot learning for teaching neural networks to classify objects never seen before
12. One-shot learning for teaching neural networks to classify objects never seen before
Weights & Biases
20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
Weights & Biases
21 14. Data Augmentation | Keras
14. Data Augmentation | Keras
Weights & Biases
22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
Weights & Biases
23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Weights & Biases
24 Grading Rubric for AI Applications with Sergey Karayev  (2019)
Grading Rubric for AI Applications with Sergey Karayev (2019)
Weights & Biases
25 16. Video Frame Prediction using CNNs and LSTMs (2019)
16. Video Frame Prediction using CNNs and LSTMs (2019)
Weights & Biases
26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
Image to LaTeX - Applied Deep Learning Fellowship (2019)
Weights & Biases
27 17.  Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
Weights & Biases
28 Applied Deep Learning - Data Management with Josh Tobin (2019)
Applied Deep Learning - Data Management with Josh Tobin (2019)
Weights & Biases
29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Weights & Biases
30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Weights & Biases
31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
Weights & Biases
32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
Weights & Biases
33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
Weights & Biases
34 Building Machine Learning Teams with Josh Tobin (2019)
Building Machine Learning Teams with Josh Tobin (2019)
Weights & Biases
35 Pieter Abeel on Potential Deep Learning Research Directions  (2019)
Pieter Abeel on Potential Deep Learning Research Directions (2019)
Weights & Biases
36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Weights & Biases
37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
Five Lessons for Team-Oriented Research with Peter Welder (2019)
Weights & Biases
38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
Applied Deep Learning - Rosanne Liu on AI Research (2019)
Weights & Biases
39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Weights & Biases
40 Organizing ML projects โ€” W&B walkthrough (2020)
Organizing ML projects โ€” W&B walkthrough (2020)
Weights & Biases
41 Brandon Rohrer โ€” Machine Learning in Production for Robots
Brandon Rohrer โ€” Machine Learning in Production for Robots
Weights & Biases
42 Nicolas Koumchatzky โ€” Machine Learning in Production for Self-Driving Cars
Nicolas Koumchatzky โ€” Machine Learning in Production for Self-Driving Cars
Weights & Biases
43 My experiments with Reinforcement Learning with Jariullah Safi
My experiments with Reinforcement Learning with Jariullah Safi
Weights & Biases
44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Weights & Biases
45 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
Weights & Biases
46 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
Weights & Biases
47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
Weights & Biases
48 Rachael Tatman โ€” Conversational AI and Linguistics
Rachael Tatman โ€” Conversational AI and Linguistics
Weights & Biases
49 Reformer by Han Lee
Reformer by Han Lee
Weights & Biases
50 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
Weights & Biases
51 GitHub Actions & Machine Learning Workflows with Hamel Husain
GitHub Actions & Machine Learning Workflows with Hamel Husain
Weights & Biases
52 Look Mom, No Indices! Vector Calculus with the Frรฉchet Derivative by Charles Frye
Look Mom, No Indices! Vector Calculus with the Frรฉchet Derivative by Charles Frye
Weights & Biases
53 Jack Clark โ€” Building Trustworthy AI Systems
Jack Clark โ€” Building Trustworthy AI Systems
Weights & Biases
54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
Weights & Biases
55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
Weights & Biases
56 Antipatterns in open source research code with Jariullah Safi
Antipatterns in open source research code with Jariullah Safi
Weights & Biases
57 Attention for time series forecasting & COVID predictions - Isaac Godfried
Attention for time series forecasting & COVID predictions - Isaac Godfried
Weights & Biases
58 Made with ML - Goku Mohandas
Made with ML - Goku Mohandas
Weights & Biases
59 Angela & Danielle โ€” Designing ML Models for Millions of Consumer Robots
Angela & Danielle โ€” Designing ML Models for Millions of Consumer Robots
Weights & Biases
60 Deep Learning Salon by Weights & Biases
Deep Learning Salon by Weights & Biases
Weights & Biases

Related AI Lessons

โšก
Inside SAM 3D: how Meta turns a single image into 3D
Learn how Meta's SAM 3D technology turns a single image into 3D, revolutionizing the field of computer vision
Medium ยท Machine Learning
โšก
Inside SAM 3D: how Meta turns a single image into 3D
Learn how Meta's SAM 3D technology generates 3D models from single images, revolutionizing the field of computer vision
Medium ยท Deep Learning
โšก
Demystifying CNNs: How Convolutional Filters and Max-Pooling Actually Work
Learn how Convolutional Neural Networks (CNNs) use convolutional filters and max-pooling to recognize images
Medium ยท Data Science
โšก
Your "Biometric Age Check" Isn't Verifying Identity โ€” And Defense Lawyers Know It
Biometric age checks don't verify identity, a crucial distinction for developers in computer vision and biometrics
Dev.to AI

Chapters (7)

Intro
0:50 How to think about training a YOLOv5 to detect new classes
3:28 Training a custom YOLOv5 model on Google Colab
8:23 How Weights & Biases helps you understand the training process
12:12 Training a large YOLOv5 model
16:18 Deploying a custom-trained model on Windows
18:28 Outro
Up next
How Transformers Finally Ate Vision โ€“ Isaac Robinson, Roboflow
AI Engineer
Watch โ†’