---
license: mit
tags:
- implicit-neural-representation
- siren
- image-compression
- coordinates-based-model
- computer-vision
model_type: SIREN
---

# Neural RAM Representation (SIREN)

This model is an **Implicit Neural Representation (INR)** of a RAM module image. Unlike traditional raster formats (PNG/JPG) that store pixels in a grid, this image is encoded entirely within the weights of a neural network.

## Project Overview
The model utilizes the **SIREN** (Sinusoidal Representation Networks) architecture. It is trained to map continuous 2D coordinates $(x, y)$ to their corresponding $RGB$ color values. 

### Key Features:
- **Mathematical Storage:** The image exists as a continuous function rather than a discrete pixel array.
- **Infinite Resolution:** You can render the image at any scale by increasing the density of the coordinate grid.
- **Efficient Encoding:** The model has been optimized (~12,000 epochs) to capture fine textures of the PCB and gold-plated contacts with high fidelity.

## Technical Specifications
- **Architecture:** Multi-Layer Perceptron (MLP) with periodic (sine) activation functions.
- **Depth:** 2 hidden layers.
- **Width:** 128 neurons per layer.
- **Format:** Provided as a lightweight `.safetensors` file.

## Usage
To render the image, you will need `torch`, `safetensors`, and `numpy`.