Update README.md

README.md

@@ -11,4 +11,64 @@ tags:
 - edge
 - image
 - clf
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+---
+
+# 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}")