Pre-Trained Models

Pre-trained models are neural networks trained on large datasets before being fine-tuned for specific tasks. These models capture intricate patterns and features, making them highly effective for image classification. By leveraging pre-trained models, developers can save time and computational resources. They can also achieve high accuracy with less data. Popular models like VGG, ResNet, and Inception have set benchmarks in the field.

Overview of Pre-Trained Models

Pre-trained models are an essential part of modern deep learning. These models are initially trained on large, general-purpose datasets like ImageNet. They learn to recognise various features, from simple edges to complex textures and objects. This extensive training allows them to generalise well, making them effective starting points for new tasks. By fine-tuning these models on specific datasets, developers can achieve high performance with less data and computation.

The architecture of pre-trained models varies, but they share common traits. They consist of multiple layers that progressively extract features from the input images. Early layers capture low-level features, while deeper layers recognise high-level patterns. Pre-trained models can be adapted to various domains, from medical imaging to autonomous driving. Their versatility and effectiveness make them invaluable tools in the field of computer vision.

Top Pre-Trained Models for Image Classification

Overview of architectures until 2018

Several pre-trained models have become standards in image classification due to their performance and reliability. Here are the key models:

1. ResNet (Residual Networks)

Overview: ResNet, introduced by Microsoft Research, revolutionized deep learning by using residual connections to mitigate the vanishing gradient problem in deep networks.
Variants: ResNet-50, ResNet-101, ResNet-152.
Key Features:
- Deep architectures (up to 152 layers).
- Residual blocks to allow gradients to flow through shortcut connections.
Applications: General image classification, object detection, and feature extraction.

2. Inception (GoogLeNet)

Overview: Developed by Google, the Inception network uses inception modules to capture multi-scale features.
Variants: Inception v3, Inception v4, Inception-ResNet.
Key Features:
- Inception modules with convolutional filters of multiple sizes.
- Efficient architecture balancing accuracy and computational cost.
Applications: General image classification, object detection, and transfer learning.

3. VGG (Visual Geometry Group)

Overview: Developed by the Visual Geometry Group at the University of Oxford, VGG models are known for their simplicity and depth.
Variants: VGG-16, VGG-19.
Key Features:
- Deep networks with 16 or 19 layers.
- Simple architecture using only 3×3 convolutions.
Applications: General image classification and feature extraction.

4. EfficientNet

Overview: Developed by Google, EfficientNet models achieve high accuracy with fewer parameters and computational resources.
Variants: EfficientNet-B0 to EfficientNet-B7.
Key Features:
- Compound scaling method to scale depth, width, and resolution.
- Efficient and accurate.
Applications: General image classification and transfer learning.

5. DenseNet (Dense Convolutional Network)

Overview: Developed by researchers at Cornell University, DenseNet connects each layer to every other layer in a feed-forward fashion.
Variants: DenseNet-121, DenseNet-169, DenseNet-201.
Key Features:
- Dense connections to improve gradient flow and feature reuse.
- Reduces the number of parameters compared to traditional convolutional networks.
Applications: General image classification and feature extraction.

6. MobileNet

Overview: Developed by Google, MobileNet models are designed for mobile and embedded vision applications.
Variants: MobileNetV1, MobileNetV2, MobileNetV3.
Key Features:
- Lightweight architecture optimized for mobile devices.
- Depthwise separable convolutions.
Applications: Mobile image classification and embedded vision applications.

7. NASNet (Neural Architecture Search Network)

Overview: Developed by Google using neural architecture search techniques to optimize the network structure.
Variants: NASNet-A, NASNet-B, NASNet-C.
Key Features:
- Automatically designed architectures using reinforcement learning.
- High accuracy with efficient performance.
Applications: General image classification and transfer learning.

8. Xception (Extreme Inception)

Overview: Developed by Google, Xception is an extension of the Inception architecture with depthwise separable convolutions.
Key Features:
- Fully convolutional architecture.
- Depthwise separable convolutions for improved performance.
Applications: General image classification and transfer learning.

9. AlexNet

Overview: Developed by Alex Krizhevsky, AlexNet is one of the earliest deep learning models that popularized the use of CNNs in image classification.
Key Features:
- Simple architecture with 8 layers.
- ReLU activation functions and dropout regularization.
Applications: General image classification and historical benchmarks.

10. Vision Transformers (ViT)

Overview: Developed by Google, Vision Transformers apply the transformer architecture, initially designed for NLP, to image classification.
Key Features:
- Transformer encoder architecture.
- Scales well with large datasets and computational resources.
Applications: General image classification and large-scale vision tasks.

YOLO - You Only Look Once

YOLO (You Only Look Once) is an object detection algorithm that uses a convolutional neural network (CNN), that's known for its speed and accuracy.

Here's how YOLO works:

Grid: YOLO's CNN divides an image into a grid.
Bounding boxes: Each cell in the grid predicts a number of bounding boxes.
Class probabilities: Each cell also predicts a class probability, which indicates the likelihood of an object being present in the box.

YOLO is popular because of its single-stage architecture, real-time performance, and accuracy. It's well-suited for real-time applications like self-driving cars, video surveillance, and augmented reality.