Pre-trained models (ResNet, VGG, EfficientNet) in Computer Vision - Model Pipeline Trace

Model Pipeline - Pre-trained models (ResNet, VGG, EfficientNet)

This pipeline uses pre-trained models like ResNet, VGG, and EfficientNet to recognize images. These models have already learned from millions of pictures, so they can quickly understand new images with less training.

Data Flow - 3 Stages

1Input Image

1 image x 224 x 224 x 3 channels→Resize and normalize image pixels to 0-1 range→1 image x 224 x 224 x 3 channels

A photo of a cat resized to 224x224 pixels with RGB colors

↓

2Feature Extraction

1 image x 224 x 224 x 3 channels→Pass image through pre-trained model layers (ResNet/VGG/EfficientNet) to extract features→1 vector x 2048 (ResNet) or 4096 (VGG) or 1280 (EfficientNet) features

Extracted features represent edges, shapes, and textures from the cat image

↓

3Classification Layer

1 vector x model-specific feature size→Apply a fully connected layer with softmax to predict class probabilities→1 vector x 1000 classes (ImageNet classes)

Model predicts probabilities for classes like 'tabby cat', 'tiger cat', 'dog', etc.

Training Trace - Epoch by Epoch

Loss
1.2 |*       
1.0 | *      
0.8 |  *     
0.6 |   *    
0.4 |    *   
    +---------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.60	Initial training with frozen pre-trained layers, classifier learns basic distinctions
2	0.8	0.75	Loss decreases as classifier improves, accuracy rises
3	0.6	0.82	Model fine-tunes classifier, better class separation
4	0.5	0.87	Further improvement, model converging
5	0.45	0.89	Training stabilizes with high accuracy

Prediction Trace - 4 Layers

Layer 1: Input preprocessing

Layer 2: Feature extraction (e.g., ResNet convolutional layers)

Layer 3: Fully connected classification layer with softmax

Layer 4: Prediction output

Model Quiz - 3 Questions

Why do we use pre-trained models like ResNet or VGG instead of training from scratch?

A They only work with black and white images

B They are smaller and faster to train

C They already learned useful features from many images

D They do not need any input preprocessing

Key Insight

Pre-trained models save time by using knowledge from large datasets. They extract meaningful features automatically, allowing quick and accurate image classification with less training.