Computer Vision Project with Advanced Deep Learning

Introduction

This tutorial guides you in building a complete computer vision project using advanced deep learning.

You will learn how to:

✅ Select and prepare a dataset.
✅ Train a CNN and apply transfer learning.
✅ Evaluate and visualize results.
✅ Deploy your model for inference.

Project Scope: Flower Classification

Objective: Classify flower images into their respective categories using advanced deep learning techniques.

Suggested dataset:

• Flowers Recognition Dataset (Kaggle)

Project Workflow

1️⃣ Dataset preparation and augmentation.
2️⃣ Model selection (CNN and transfer learning).
3️⃣ Training and evaluation.
4️⃣ Deployment options.

1️⃣ Dataset Preparation

• Organize images into train, validation, and test folders.
• Apply augmentations using ImageDataGenerator:
  • Rescaling.
  • Random flips and rotations.
  • Zoom and shift augmentations.

2️⃣ Model Building with Transfer Learning

Using EfficientNetB0 (or MobileNetV2) with Keras:

from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Data generators
train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=20, zoom_range=0.2, horizontal_flip=True, validation_split=0.2)

train_gen = train_datagen.flow_from_directory('flowers_dataset', target_size=(224,224), batch_size=32, class_mode='categorical', subset='training')
val_gen = train_datagen.flow_from_directory('flowers_dataset', target_size=(224,224), batch_size=32, class_mode='categorical', subset='validation')

# Load pre-trained model
base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224,224,3))
base_model.trainable = False

x = GlobalAveragePooling2D()(base_model.output)
output = Dense(train_gen.num_classes, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=output)

3️⃣ Training and Evaluation

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(train_gen, epochs=10, validation_data=val_gen)

Visualize accuracy:

import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'], label='Train Accuracy')
plt.plot(history.history['val_accuracy'], label='Validation Accuracy')
plt.legend()
plt.title('Model Accuracy')
plt.show()

Fine-Tuning

After initial training:

✅ Unfreeze some layers of base_model.
✅ Re-train with a lower learning rate to improve performance.

4️⃣ Deployment Options

✅ Convert your model using TensorFlow Lite for edge deployment.
✅ Build a Streamlit app for interactive demo.
✅ Use Flask or FastAPI to create a REST API for serving predictions.

Conclusion

✅ You now know how to build an advanced computer vision project using deep learning and transfer learning techniques.
✅ You can structure your projects from dataset preparation to deployment for real-world applications.

What’s Next?

✅ Experiment with object detection models (YOLO, SSD).
✅ Explore Vision Transformers for advanced CV tasks.
✅ Learn model optimization for faster inference on edge devices.

Join our SuperML Community to share your computer vision projects and learn collaboratively with other deep learning practitioners.

Happy Building! 📸