Image Classification Pipeline
So far, you have learned many individual concepts: images, pixels, CNNs, transfer learning, and fine-tuning.
Now it is time to connect all of them into a single, clear, real-world process called the image classification pipeline.
This pipeline is how computer vision systems are actually built in practice.
What Is an Image Classification Pipeline?
An image classification pipeline is the step-by-step process used to turn raw images into accurate class predictions.
Instead of thinking about models in isolation, we think in terms of a complete flow:
- Where the data comes from
- How it is prepared
- How the model learns
- How results are evaluated
Every successful CV project follows this structure.
High-Level Pipeline Overview
At a high level, an image classification pipeline looks like this:
- Data collection
- Data preprocessing
- Dataset splitting
- Model selection
- Training
- Evaluation
- Deployment / inference
We will now understand each step clearly.
Step 1: Data Collection
Everything starts with images.
Data can come from:
- Cameras
- Mobile phones
- Medical devices
- Public datasets
- Web scraping
At this stage, the goal is simple: collect enough representative images for each class.
Bad data at this stage cannot be fixed later.
Step 2: Data Preprocessing
Raw images are rarely usable directly. They need to be standardized.
Common preprocessing steps include:
- Resizing images to a fixed size
- Normalizing pixel values
- Converting color spaces if required
This ensures the model receives consistent input.
Step 3: Dataset Splitting
Before training, data is split into separate sets.
| Dataset | Purpose |
|---|---|
| Training set | Used to learn patterns |
| Validation set | Used to tune parameters |
| Test set | Used for final evaluation |
This separation prevents misleading performance results.
Step 4: Model Selection
At this stage, you choose how the model will learn.
Options include:
- Custom CNN from scratch
- Pretrained model with feature extraction
- Pretrained model with fine-tuning
The choice depends on:
- Dataset size
- Task complexity
- Available compute
Step 5: Training the Model
Training is where learning actually happens.
The model:
- Receives images
- Makes predictions
- Calculates loss
- Updates weights
This process repeats for multiple epochs until performance stabilizes.
Step 6: Model Evaluation
After training, we measure how well the model performs.
Common evaluation metrics include:
- Accuracy
- Precision
- Recall
- Confusion matrix
Evaluation tells us whether the model truly understands the data or just memorized it.
Step 7: Deployment and Inference
Once validated, the model is used on new, unseen images.
This step is called inference.
Inference may happen:
- On servers
- On mobile devices
- On edge hardware
This is where the model delivers real value.
Where Does Coding Fit In?
This pipeline contains both:
- Conceptual decisions
- Implementation steps
You should always understand the pipeline before writing code.
In upcoming lessons, each part of this pipeline will be implemented step by step.
Common Mistakes Beginners Make
- Training without proper validation
- Ignoring preprocessing consistency
- Using accuracy alone for evaluation
- Overfitting without realizing it
Understanding the pipeline helps avoid these errors.
Practice Questions
Q1. Why do we split datasets before training?
Q2. What is inference?
Q3. Which step controls input consistency?
Mini Assignment
Pick a simple image classification idea:
- Animals
- Fruits
- Traffic signs
Write down:
- What data you would collect
- How you would split it
- Which model you would choose
This mental exercise builds real project thinking.
Quick Recap
- An image classification pipeline is a complete workflow
- Each step serves a clear purpose
- Good pipelines matter more than fancy models
Next lesson: Data Augmentation — making models robust with smarter data.