“Wow. So this is the canvas that can do image classification and object detection?” Vixel asked.
“Yes, I am VGG. VGG stands for Visual Geometry Group.” the Canvas replied. “More exactly, I’m VGG19, which means I have 19 weight layers (16 convolutional + 3 fully connected).”
“So, there are more than one type of VGG?” Vixel asked.
“Yes, other than VGG19, another famous VGG is VGG16, whom have 16 weight layers (13 convolutional + 3 fully connected)” a spider joined in the conversation.
“Ok. Why are they so famous?” Another spider asked.
“Well, because they are revolutionary.” A fairy replied, which made Vixel very surprised.
“They have simplified design, though. Just stack 3×3 filters and go deep — no fancy tricks.” a giant eagle remarked.
“But still have high accuracy. We are also a go-to model for feature extraction in other tasks.” the spirit of the canvas suddenly appeard and gave Vixel a paper roll on VGGNet.
🧠 What Is VGG?
VGG stands for Visual Geometry Group, a research team at the University of Oxford. They introduced the VGGNet architecture in 2014, which became famous for its simplicity and effectiveness in image classification tasks.
The most popular versions are:
- VGG16: 16 weight layers (13 convolutional + 3 fully connected)
- VGG19: 19 weight layers (16 convolutional + 3 fully connected)
🧱 VGG Architecture Highlights
| Feature | Description |
|---|---|
| 🔍 Small Filters | Uses only 3×3 convolutional filters stacked deep |
| 🔁 Repetition | Repeats the same block structure throughout the network |
| 🧠 Deep Structure | More layers → better feature extraction |
| 🔥 ReLU Activation | Adds non-linearity after each convolution |
| 📉 Max Pooling | Reduces spatial dimensions (2×2 pooling) |
| 🎯 Fully Connected Layers | Final layers for classification |
| 📊 Softmax Output | Predicts class probabilities |
🧪 Why VGG Was Revolutionary
- Simplified Design: Just stack 3×3 filters and go deep — no fancy tricks.
- High Accuracy: Achieved 92.7% top-5 accuracy on ImageNet with VGG16.
- Transfer Learning: Became a go-to model for feature extraction in other tasks.
⚖️ VGG vs. AlexNet
| Feature | AlexNet | VGGNet |
|---|---|---|
| Filter Size | Up to 11×11 | Only 3×3 |
| Depth | 8 layers | 16–19 layers |
| Parameters | ~60M | ~138M (VGG16) |
| Performance | Good | Better |
🧰 Use Cases
- Object detection
- Classification
- Neural style transfer
- Feature extraction for other models
📉 Limitations
- Heavy: VGG16 has ~138 million parameters — slow and memory-intensive
- Outdated: Surpassed by newer models like ResNet, Inception, and EfficientNet