README.md

---
tags:
- image-classification
- tensorflow
- keras
- computer-vision
- animal-recognition
license: apache-2.0
library_name: keras
language: en
datasets:
- custom-dataset
metrics:
- accuracy
model_creator: AEROVERSE
model_type: Custom CNN
num_classes: 10
training_data: Custom animal dataset
validation_split: 20%
epochs: 500
early_stopping: patience=5, restore_best_weights=True
dropout: 0.5
optimizer: Adam
loss: sparse_categorical_crossentropy
input_shape: (256, 256, 3)
output_activation: softmax
checkpoint: best_model.weights.h5
pipeline_tag: image-classification
---

# Animal Recognition Model

## Model Overview
This model is designed to classify images of animals into predefined categories. It uses a ResNet50V2 base model and has been trained on a custom dataset.

## Classes
The model was trained on the following classes:
- cat
- dog
- horse
- lion
- tiger
- elephant

## Usage
1. Load the model using TensorFlow/Keras.
2. Preprocess the input image to a size of 256x256 and normalize it.
3. Pass the preprocessed image to the model for prediction.

```python
from keras.models import load_model
import numpy as np
from tensorflow.keras.utils import load_img, img_to_array

def predict_image(image_path, model):
    img = load_img(image_path, target_size=(256, 256))
    img_array = img_to_array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    prediction = model.predict(img_array)
    return np.argmax(prediction, axis=1)

model = load_model('best_model.weights.h5')
predicted_class = predict_image('image.jpg', model)
print(f"Predicted class: {predicted_class}")
```

## Training Details
- **Base Model:** ResNet50V2 (pretrained on ImageNet)
- **Dataset:** Custom animal dataset
- **Optimizer:** Adam
- **Loss Function:** Sparse Categorical Crossentropy
- **Metrics:** Accuracy
- **Augmentation:** Applied during training

## Model Performance
Will be updated soon