R-CNN family overview in Computer Vision - Model Pipeline Trace

Model Pipeline - R-CNN family overview

The R-CNN family is a set of models designed to find and classify objects in images. They work by first proposing regions that might contain objects, then extracting features from these regions, and finally classifying what object is inside each region.

Data Flow - 5 Stages

1Input Image

1 image x 600 x 800 x 3 channels→Original color image input→1 image x 600 x 800 x 3 channels

A photo of a street with cars and people

↓

2Region Proposal

1 image x 600 x 800 x 3 channels→Generate around 2000 candidate boxes (regions) that might contain objects→2000 regions x 4 coordinates each

Boxes around cars, people, signs in the image

↓

3Feature Extraction

2000 regions x variable size→Resize each region and extract features using a CNN→2000 regions x 4096 features

Feature vector representing each proposed region

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4Classification and Bounding Box Regression

2000 regions x 4096 features→Classify each region into object classes and refine box coordinates→2000 regions x (class scores + 4 refined coordinates)

Region classified as 'car' with adjusted box coordinates

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5Non-Maximum Suppression (NMS)

2000 regions x class scores and boxes→Remove overlapping boxes to keep best detections→Final detected objects, e.g., 10 boxes with classes

Final boxes around distinct cars and people without overlap

Training Trace - Epoch by Epoch

Loss
1.2 |*       
1.0 | *      
0.8 |  **    
0.6 |   **   
0.4 |    *** 
0.2 |      **
0.0 +--------
      1 5 10 15 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning to classify and localize objects
5	0.8	0.65	Loss decreases as model improves detection accuracy
10	0.5	0.78	Model shows good convergence with better localization
15	0.35	0.85	High accuracy and low loss indicate strong detection performance

Prediction Trace - 5 Layers

Layer 1: Input Image

Layer 2: Region Proposal

Layer 3: Feature Extraction CNN

Layer 4: Classification and Bounding Box Regression

Layer 5: Non-Maximum Suppression

Model Quiz - 3 Questions

What is the main role of the Region Proposal step in the R-CNN pipeline?

A Suggest areas in the image that might contain objects

B Classify objects inside the image

C Resize the input image

D Remove overlapping boxes

Key Insight

The R-CNN family breaks down object detection into clear steps: proposing regions, extracting features, classifying, and refining boxes. This modular approach helps the model learn to find and identify objects accurately by focusing on smaller parts of the image.