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license: unknown |
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# **Cats, Dogs, and Snakes Dataset** |
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## **Dataset Overview** |
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The dataset contains images of **three animal classes**: Cats, Dogs, and Snakes. It is **balanced and cleaned**, designed for supervised image classification tasks. |
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| Class | Number of Images | Description | |
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| ------ | ---------------- | ---------------------------------------------- | |
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| Cats | 1,000 | Includes multiple breeds and poses | |
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| Dogs | 1,000 | Covers various breeds and backgrounds | |
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| Snakes | 1,000 | Includes multiple species and natural settings | |
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**Total Images:** 3,000 |
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**Image Properties:** |
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* Resolution: 224×224 pixels (resized for consistency) |
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* Color Mode: RGB |
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* Format: JPEG/PNG |
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* Cleaned: Duplicate, blurry, and irrelevant images removed |
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## **Data Split Recommendation** |
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| Set | Percentage | Number of Images | |
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| ---------- | ---------- | ---------------- | |
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| Training | 70% | 2,100 | |
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| Validation | 15% | 450 | |
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| Test | 15% | 450 | |
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## **Preprocessing** |
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Images in the dataset have been standardized to support machine learning pipelines: |
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1. **Resizing** to 224×224 pixels. |
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2. **Normalization** of pixel values to [0,1] or mean subtraction for deep learning frameworks. |
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3. **Label encoding**: Integer encoding (0 = Cat, 1 = Dog, 2 = Snake) or one-hot encoding for model training. |
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## **Example: Loading and Using the Dataset (Python)** |
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```python |
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import os |
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import tensorflow as tf |
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from tensorflow.keras.preprocessing.image import ImageDataGenerator |
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# Path to dataset |
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dataset_path = "path/to/dataset" |
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# ImageDataGenerator for preprocessing |
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datagen = ImageDataGenerator( |
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rescale=1./255, |
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validation_split=0.15 # 15% for validation |
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) |
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# Load training data |
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train_generator = datagen.flow_from_directory( |
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dataset_path, |
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target_size=(224, 224), |
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batch_size=32, |
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class_mode='categorical', |
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subset='training', |
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shuffle=True |
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) |
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# Load validation data |
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validation_generator = datagen.flow_from_directory( |
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dataset_path, |
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target_size=(224, 224), |
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batch_size=32, |
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class_mode='categorical', |
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subset='validation', |
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shuffle=False |
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) |
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# Example: Iterate over one batch |
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images, labels = next(train_generator) |
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print(images.shape, labels.shape) # (32, 224, 224, 3) (32, 3) |
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``` |
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## **Key Features** |
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* **Balanced:** Equal number of samples per class reduces bias. |
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* **Cleaned:** High-quality, relevant images improve model performance. |
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* **Diverse:** Covers multiple breeds, species, and environments to ensure generalization. |
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* **Ready for ML:** Preprocessed and easily integrated into popular deep learning frameworks. |
