| --- |
| 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}") |
