README.md

---
license: mit
language: en
pipeline_tag: image-classification
library_name: keras
tags:
- image-classification
- alexnet
- cifar10
- tensorflow
- keras
metrics:
- accuracy
datasets:
- cifar10
---

# Revisiting AlexNet: Achieving High-Accuracy on CIFAR-10 with Modern Optimization Techniques

## Model Description

This repository contains a **TensorFlow/Keras** implementation of the **AlexNet** architecture, optimized and trained from scratch on the **CIFAR-10** dataset. The original 2012 architecture has been modernized by replacing Local Response Normalization (LRN) layers with **Batch Normalization** and incorporating robust regularization techniques like L2 weight decay and aggressive data augmentation.

The model was developed in a Kaggle environment, demonstrating a reproducible workflow for achieving high accuracy on a benchmark computer vision task.

---

## Model Details

| Detail | Value |
| :--- | :--- |
| **Architecture** | Modified AlexNet with Batch Normalization |
| **Parameters** | ~46 million |
| **Framework**| TensorFlow / Keras |
| **Task** | Image Classification |
| **Original Paper** | [Revisiting AlexNet: Achieving High-Accuracy on CIFAR-10 with Modern Optimization Techniques]() |
| **Original Paper** | [ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf) |

---

## Training Procedure

### Data
The model was trained on the **CIFAR-10 dataset**.
* **Preprocessing:** All images were resized from 32x32 to 224x224 and pixel values were normalized to a [0, 1] range.
* **Augmentation:** The training data was augmented on-the-fly with Random Horizontal Flips, Random Rotations (10%), and Random Zooms (10%).

### Hyperparameters

| Hyperparameter | Value |
| :--- | :--- |
| **Optimizer** | Adam |
| **Learning Rate** | `1e-4` (with `ReduceLROnPlateau` callback) |
| **Batch Size** | `128` (GPU) / `1024` (TPU) |
| **Epochs** | Trained for a max of 100 with `EarlyStopping` (patience=10) |
| **Regularization** | L2 Weight Decay (`λ=0.0005`), Dropout (`rate=0.5`) |
| **Hardware**| Kaggle GPU (2x T4) or TPU (v5e-8) |

---

## Evaluation

The model achieved the following performance on the CIFAR-10 test set:

* **Test Accuracy:** **95.7%**
* **Test Loss:** **0.6143**

---

## How to Use

This model can be easily loaded from the Hub for inference. Below is a complete example of how to load the model and predict the class of a sample image.

```python
import tensorflow as tf
import numpy as np
from PIL import Image
import requests
from huggingface_hub import from_pretrained_keras

# 1. Load the model from the Hub
# Replace with your actual repo_id
repo_id = "metanthropiclabs/alexnet-cifar10-optimized"
model = from_pretrained_keras(repo_id)

# 2. Define class labels
cifar10_labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

# 3. Load and preprocess a sample image
# Example: an image of a cat from the web
url = '[https://storage.googleapis.com/petbacker/images/blog/2017/cat-in-a-box.jpg](https://storage.googleapis.com/petbacker/images/blog/2017/cat-in-a-box.jpg)'
image = Image.open(requests.get(url, stream=True).raw)
image = image.resize((224, 224)) # Must match model's input size
image_array = np.array(image)

# Normalize and add a batch dimension
image_array = image_array.astype('float32') / 255.0
image_tensor = tf.expand_dims(image_array, 0) # Create a batch of 1

# 4. Make a prediction
predictions = model.predict(image_tensor)
predicted_class_index = np.argmax(predictions[0])
predicted_class_name = cifar10_labels[predicted_class_index]

print(f"Predicted Class: {predicted_class_name}")
# Expected output for this image: Predicted Class: cat

```