kd13/Modern-SqueezeNet

SqueezeNet-SwiGLU (Modernized SqueezeNet Architecture)

SqueezeNet-SwiGLU is a modernized, ultra-lightweight Convolutional Neural Network (CNN) architecture based on the original SqueezeNet v1.1 design. It incorporates state-of-the-art deep learning architectural enhancements, including SwiGLU gated activations, FP32 Layer Normalization, and residual block scaling, delivering superior feature representation while maintaining a minimal parameter footprint.

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Key Architectural Improvements (vs. Original SqueezeNet)

Compared to the legacy SqueezeNet v1.1 (Iandola et al., 2016), this model introduces several architectural modernizations:

Feature	Legacy SqueezeNet v1.1	SqueezeNet-SwiGLU (This Model)
Activation Function	Standard ReLU	Residual-Scaled SwiGLU Gated Activation
Normalization	Batch Normalization	FP32 Layer Normalization (GroupNorm(1, C))
Batch Size Dependency	High (sensitive to batch stats & EMA lag)	Zero (Inference identical across any batch size)
Gradient Flow	Standard Fire Connections	Residual Fire Block Skip Connections & Scaling
Activation Variance	Prone to un-bounded drift	Strictly bounded via LayerNorm & FP32 Precision

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Benchmark & Evaluation

• Evaluation Dataset: ImageNet 200-Class Test Split (Tiny-ImageNet Categories)
• Input Resolution: 64 × 64 pixels (native) / 224 × 224 (interpolated)
• Top-1 Accuracy: 50.51%
• Top-5 Accuracy: 75.06%

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Target Usecases & Applications

Due to its ultra-compact size and high throughput, SqueezeNet-SwiGLU is optimized for resource-constrained deployment environments:

1. Edge & IoT Intelligence: Microcontrollers, Raspberry Pi, NVIDIA Jetson, and embedded vision hardware.
2. Mobile AI Applications: On-device real-time visual classification (iOS CoreML / Android ONNX).
3. High-FPS Video Analytics: Lightweight feature backbone for real-time surveillance, robotics, and drone navigation.
4. Microservice Backends: Serving high-throughput image classification with minimal memory overhead per GPU/CPU node.

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How to Use

Fast Inference with Hugging Face pipeline

from transformers import pipeline

# Initialize the classification pipeline (requires trust_remote_code=True)
classifier = pipeline(
    "image-classification",
    model="kd13/Modern-SqueezeNet",  
    trust_remote_code=True
)

# Run prediction on an image URL or local PIL Image
results = classifier("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")

for pred in results:
    print(f"Label: {pred['label']} | Score: {pred['score']:.4f}")