If you would like a detailed explanation of this project, please refer to the Medium article below.

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Animal Image Classification Using InceptionV3

A complete end-to-end pipeline for building a clean, reliable deep-learning classifier.

This project implements a full deep-learning workflow for classifying animal images using TensorFlow + InceptionV3, with a major focus on dataset validation and cleaning. Before training the model, I built a comprehensive system to detect corrupted images, duplicates, brightness/contrast issues, mislabeled samples, and resolution outliers.

This repository contains the full pipeline—from dataset extraction to evaluation and model saving.

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Features

Full Dataset Validation

The project includes automated checks for:

• Corrupted or unreadable images
• Hash-based duplicate detection
• Duplicate filenames
• Misplaced or incorrectly labeled images
• File naming inconsistencies
• Extremely dark/bright images
• Very low-contrast (blank) images
• Outlier resolutions

Preprocessing & Augmentation

• Resize to 256×256
• Normalization
• Light augmentation (probabilistic)
• Efficient tf.data pipeline with caching, shuffling, prefetching

Transfer Learning with InceptionV3

• Pretrained ImageNet weights
• Frozen base model
• Custom classification head (GAP → Dense → Dropout → Softmax)
• EarlyStopping + ModelCheckpoint + ReduceLROnPlateau callbacks

Clean & Reproducible Training

• 80% training
• 10% validation
• 10% test
• High stability due to dataset cleaning

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1. Dataset Extraction

The dataset is stored as a ZIP file (Google Drive). After mounting the drive, it is extracted and indexed into a Pandas DataFrame:

drive.mount('/content/drive')

zip_path = '/content/drive/MyDrive/Animals.zip'
extract_to = '/content/my_data'

with zipfile.ZipFile(zip_path, 'r') as zip_ref:
    zip_ref.extractall(extract_to)

Each image entry records:

• Class
• Filename
• Full path

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2. Dataset Exploration

Before any training, I analyzed:

• Class distribution
• Image dimensions
• Grayscale vs RGB
• Unique sizes
• Folder structures

Example class-count visualization:

plt.figure(figsize=(32, 16))
class_count.plot(kind='bar')

This revealed imbalance and inconsistent image sizes early.

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3. Visual Sanity Checks

Random images were displayed with their brightness, contrast, and shape to manually confirm dataset quality.

This step prevents hidden issues—especially in community-created or scraped datasets.

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4. Data Quality Detection

The system checks for:

Duplicate Images (Using MD5 Hashing)

def get_hash(path):
    with open(path, 'rb') as f:
        return hashlib.md5(f.read()).hexdigest()

df['file_hash'] = df['full_path'].apply(get_hash)
duplicate_hashes = df[df.duplicated('file_hash', keep=False)]

Corrupted Files

try:
    with Image.open(file_path) as img:
        img.verify()
except:
    corrupted_files.append(file_path)

Brightness/Contrast Outliers

Using PIL’s ImageStat to detect very dark/bright samples.

Label Consistency Check

folder = os.path.basename(os.path.dirname(row["full_path"]))

This catches mislabeled entries where folder name ≠ actual class.

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5. Preprocessing Pipeline

Custom preprocessing:

• Resize → Normalize
• Optional augmentation
• Efficient tf.data batching

def preprocess_image(path, target_size=(256, 256), augment=True):
    img = tf.image.decode_image(...)
    img = tf.image.resize(img, target_size)
    img = img / 255.0

Split structure:

Split	Percent
Train	80%
Validation	10%
Test	10%

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6. Model — Transfer Learning with InceptionV3

from tensorflow.keras.applications import InceptionV3

base_model = InceptionV3(weights='imagenet', include_top=False)

for layer in base_model.layers:
    layer.trainable = False

Classification head:

• GlobalAveragePooling2D
• Dense(512, ReLU)
• Dropout(0.5)
• Softmax

Compile:

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

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7. Training

history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=10,
    callbacks=[...]
)

Using:

• EarlyStopping
• ModelCheckpoint
• ReduceLROnPlateau

The model converged quickly thanks to the cleaned dataset.

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8. Evaluation & Saving

model.evaluate(test_ds)
model.save("Simple_CNN_Classification.h5")

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Key Takeaways

The biggest lesson from this project:

A strong deep-learning model starts with a clean dataset.

Cleaning the data took more time than training the model—but it directly improved accuracy, stability, and model reliability.

If you're building your own image classification project, always verify:

• Dataset quality
• Brightness/contrast issues
• Duplicate removal
• Class consistency
• Resolution outliers

Clean data makes everything else easier.

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Future Improvements

• Advanced augmentation (CutMix, MixUp)
• Fine-tuning InceptionV3’s deeper layers in another dataset
• Converting model to TensorFlow Lite
• Deploying on my own website