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license: mit
datasets:
- zh-plus/tiny-imagenet
metrics:
- accuracy
pipeline_tag: image-classification
library_name: transformers
tags:
- Mobile
- edge
- image
- clf
---
# Modern MobileNetV1 (Modernized MobileNet Architecture)
**Modern MobileNetV1** is an enhanced, highly optimized variant of the classic MobileNetV1 architecture. It incorporates modern deep learning design choices—including **SiLU activations**, **FP32 Layer Normalization**, and **learnable residual scaling**—delivering stabilized training and high inference accuracy while keeping memory footprint and computational complexity low.
---
## Key Architectural Improvements (vs. Original MobileNetV1)
Compared to the classic MobileNetV1 (Howard et al., 2017), this modernized implementation introduces several key architectural upgrades:
| Feature | Legacy MobileNetV1 | Modern MobileNetV1 (This Model) |
| :--- | :--- | :--- |
| **Activation Function** | Standard ReLU | **SiLU (Swish)** |
| **Normalization** | Batch Normalization | **FP32 Layer Normalization (`GroupNorm(1, C)`)** |
| **Residual Connections** | None (pure feed-forward) | **Learnable Residual Block Scaling (`identity + scale * out`)** |
| **Batch Size Dependency** | High (sensitive to batch statistics) | **Zero (Inference identical across any batch size)** |
| **Precision Stability** | Standard FP32 / FP16 | **FP32-Capped Normalization (Prevents Underflow/Overflow)** |
---
## Benchmark & Evaluation
- **Evaluation Dataset:** Tiny-ImageNet (200-Class Test Split)
- **Input Resolution:** 64 × 64 pixels (native)
- **Top-1 Accuracy:** 44.38%
- **Top-5 Accuracy:** 67.26%
---
## Target Use Cases & Applications
Due to its parameter efficiency and depthwise separable convolution structure, Modern MobileNetV1 is optimized for edge deployment:
- **Edge & Embedded AI:** Deployment on Raspberry Pi, NVIDIA Jetson, microcontrollers, and IoT vision devices.
- **Mobile Vision Applications:** Real-time on-device classification (Android ONNX / iOS CoreML).
- **High-Throughput Microservices:** Lightweight backbone for low-latency web services and microservices.
- **Robotics & Drones:** Compact feature extractor for fast object recognition and navigational awareness.
---
## How to Use
### Fast Inference with Hugging Face `pipeline`
```python
from transformers import pipeline
# Initialize the classification pipeline (requires trust_remote_code=True for custom code)
classifier = pipeline(
"image-classification",
model="kd13/Modern-MobileNet",
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}") |