NASNetLarge is a convolutional neural network (CNN) architecture developed by Google researchers through Neural Architecture Search (NAS) — an automated process that uses reinforcement learning to discover high-performing models. It is one of the most powerful NASNet variants, optimized specifically for high performance on ImageNet classification tasks.

🔍 What is NASNetLarge?

NASNetLarge stands for Neural Architecture Search Network - Large. The term "Large" signifies that this is the most computationally intensive and capable version of the NASNet family.

Key highlights:

• Achieves state-of-the-art accuracy on benchmarks like ImageNet.

• Often outperforms manually-designed CNNs in classification tasks.

🧩 Key Features

✅ AutoML-Based Design

NASNetLarge wasn't handcrafted — it was discovered using reinforcement learning that explored a large space of network architectures.

🧱 Modular Cell Structure

The network is built from two types of repeating cells:

• Normal cells: Maintain spatial dimensions.

• Reduction cells: Reduce spatial dimensions (similar to pooling).

🔁 Transferability

• Initially trained and discovered on CIFAR-10.

• Successfully scaled up to ImageNet with excellent performance.

📊 Performance Metrics

• Top-1 Accuracy on ImageNet: ~82.7%

• Top-5 Accuracy on ImageNet: ~96.2%

These figures placed NASNetLarge among the top CNN models at its peak — though recent transformer-based models like ViT and EfficientNetV2 have since taken the lead.

🏗️ Architecture Specifications

• Input Size: 331x331 RGB images

• Parameters: ~88 million

• Layers: 400+ (based on the number of repeated cells)

• Use Cases: Often used as a feature extractor in tasks like object detection and image segmentation.

📦 Availability & Implementation

Supported Frameworks:

• TensorFlow

• Keras

Sample Keras Code:

• Pretrained Weights: Available (trained on ImageNet)

⚖️ Pros and Cons