Use of CNN in Image Classification

(छवि वर्गीकरण में CNN का उपयोग)

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🔶 1. Image Classification क्या है?

📌 परिभाषा:

Image Classification एक ऐसा task है जिसमें input image को एक predefined class में classify किया जाता है।

उदाहरण: एक model को बताना कि image में dog है या cat।

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🎯 2. CNN क्यों बेहतर है Image Classification के लिए?

CNN में मौजूद:

• Convolution layers → local patterns और textures पहचानती हैं
• Pooling layers → size reduce कर feature को concentrate करती हैं
• Dense layers → final decision लेती हैं

👉 ये सब मिलकर CNN को image data पर बहुत सक्षम बना देती हैं।

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🧠 3. Typical Image Classification Pipeline (Using CNN)

[Input Image]
      ↓
Convolution Layers (Feature Extraction)
      ↓
ReLU + Pooling Layers
      ↓
Flatten Layer
      ↓
Fully Connected (Dense) Layers
      ↓
Softmax (Output → Class Probabilities)

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📷 4. Real-world Examples:

Dataset	Classes	Application
MNIST	10 (digits)	Handwritten digit recognition
CIFAR-10	10 (animals, vehicles)	Object classification
ImageNet	1000+	Large-scale classification

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🔍 5. Feature Hierarchy in CNN:

Layer Depth	Learns What
Shallow (1-2)	Edges, corners, color blobs
Mid (3-4)	Textures, patterns
Deep (5+)	Objects, faces, full shapes

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🔧 6. PyTorch Code Example: CNN for Image Classification (CIFAR-10)

import torch.nn as nn

class CIFAR10CNN(nn.Module):
    def __init__(self):
        super(CIFAR10CNN, self).__init__()
        self.net = nn.Sequential(
            nn.Conv2d(3, 32, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(32, 64, 3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),
            nn.Flatten(),
            nn.Linear(64 * 8 * 8, 128),
            nn.ReLU(),
            nn.Linear(128, 10)  # 10 output classes for CIFAR-10
        )

    def forward(self, x):
        return self.net(x)

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📊 7. Output Layer (Softmax)

nn.Softmax(dim=1)

• Converts raw outputs (logits) to probabilities
• Highest probability class is the predicted label

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📈 8. Training Process (Overview)

Step	Description
Data Preparation	Image resize, normalization
Forward Pass	Prediction
Loss Calculation	CrossEntropyLoss
Backward Pass	Gradients calculate
Optimization	SGD, Adam, etc.
Evaluation	Accuracy, Precision, Recall

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✅ 9. Advantages of CNNs in Image Classification

Benefit	Explanation
Local Feature Extraction	Captures spatial hierarchy
Translation Invariance	Position of object doesn’t matter
Parameter Efficiency	Filters shared across image
End-to-End Learning	No need for manual feature extraction

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📝 Practice Questions:

1. CNN image classification में कैसे मदद करता है?
2. एक simple CNN architecture लिखिए जो 10-class classification कर सके।
3. Feature map क्या होता है?
4. CNN में object recognition की hierarchy क्या होती है?
5. PyTorch में prediction probabilities कैसे निकाली जाती हैं?

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🎯 Summary:

Concept	Use in Classification
Convolution	Extract features from images
Pooling	Downsample and focus on important parts
Dense Layers	Final decision making
Softmax	Class probability distribution
CNN	End-to-end feature learning system