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license: mit
datasets:
- zh-plus/tiny-imagenet
pipeline_tag: image-classification
library_name: transformers
tags:
- Squeeze
- New
- Image
- Clf
metrics:
- accuracy
---
# 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.
---
## 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** |
---
## 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%
---
## 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.
---
## How to Use
### Fast Inference with Hugging Face `pipeline`
```python
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}") |