Update README.md

README.md

@@ -1,122 +1,127 @@
-# 🧁 MNIST Bakery Dataset
-
-A procedurally synthesized variant of the classic MNIST dataset, created using **SideFX Houdini** and designed for experimentation in **data augmentation**, **synthetic data generation**, and **model robustness research**.
-
----
-
-## 🎯 Purpose
-
-This dataset demonstrates how **procedural generation pipelines** in 3D tools like Houdini can be used to create **high-quality, synthetic training data** for machine learning tasks. It is intended for:
-
-- Benchmarking model performance using synthetic vs. real data  
-- Training models in **low-data** or **zero-shot** environments  
-- Developing robust classifiers that generalize beyond typical datasets  
-- Evaluating augmentation and generalization strategies in vision models
-
----
-
-## 🛠️ Generation Pipeline
-
-All data was generated using the `.hip` scene:  
-```bash
-./houdini/digitgen_v02.hip
-```
-
-## 🧪 Methodology
-
-### 1. Procedural Digit Assembly
-- Each digit `0`–`9` is generated using a random font in each frame via Houdini’s **Font SOP**.
-- Digits are arranged in a clean **8×8 grid**, forming sprite sheets with **64 digits per render**.
-
-### 2. Scene Variability
-- Fonts are randomly selected per frame.
-- Procedural distortions are applied including:
-  - Rotation
-  - Translation
-  - Skew
-  - Mountain noise displacement
-- This ensures high variability across samples.
-
-### 3. Rendering
-- Scene renders are executed via **Mantra** or **Karma**.
-- Output format: **grayscale 224×224 px** sprite sheets (`.exr` or `.jpg`).
-
-### 4. Compositing & Cropping
-- A **COP2 network** slices the sprite sheet into **28×28** digit tiles.
-- Each tile is labeled by its original digit and saved to:
-```
-./output/0/img_00001.jpg
-./output/1/img_00001.jpg
-...
-```
-
-### 🧾 Dataset Structure
-
-```bash
-mnist_bakery_data/
-├── 0/
-│   ├── img_00001.jpg
-│   ├── ...
-├── 1/
-│   ├── img_00001.jpg
-│   └── ...
-...
-├── 9/
-│   └── img_00001.jpg
-```
-
-
-- All images: grayscale `.jpg`, 28×28 resolution
-- Total: **40,960 samples**
-- ~4,096 samples per digit
-
----
-
-## 📊 Statistics
-
-| Set       | Samples | Mean    | StdDev   |
-|-----------|---------|---------|----------|
-| MNIST     | 60,000  | 0.1307  | 0.3081   |
-| Synthetic | 40,960  | 0.01599 | 0.07722  |
-
-> Combine mean/std using weighted averaging if mixing both datasets.
-
----
-
-## 📚 Usage Example
-
-```python
-from torchvision import transforms, datasets
-
-transform = transforms.Compose([
-    transforms.Grayscale(),
-    transforms.ToTensor(),
-    transforms.Normalize(mean=[0.01599], std=[0.07722])  # Approximate weighted normalization
-])
-
-dataset = datasets.ImageFolder('./mnist_bakery_data', transform=transform)
-```
-
----
-
-#### 🧠 Credits
-
-**Author**: Aaron T. Carter
-
-**Organization**: Arkaen Solutions
-
-**Tools Used**: Houdini, PyTorch, PIL
-
-___
-
-language: 
-- "List of ISO 639-1 code for your language"
-- en
-pretty_name: "MNIST Bakery Dataset"
-tags:
-- vision
-- image
-license: "MIT"
-task_categories:
-- image-classification
-- multi-label-image-classification
+---
+license: mit
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- mnist
+- image
+- digit
+- synthetic
+- houdini
+pretty_name: MNIST Bakery Dataset
+size_categories:
+- 10K<n<100K
+---
+# 🧁 MNIST Bakery Dataset
+
+A procedurally synthesized variant of the classic MNIST dataset, created using **SideFX Houdini** and designed for experimentation in **data augmentation**, **synthetic data generation**, and **model robustness research**.
+
+---
+
+## 🎯 Purpose
+
+This dataset demonstrates how **procedural generation pipelines** in 3D tools like Houdini can be used to create **high-quality, synthetic training data** for machine learning tasks. It is intended for:
+
+- Benchmarking model performance using synthetic vs. real data  
+- Training models in **low-data** or **zero-shot** environments  
+- Developing robust classifiers that generalize beyond typical datasets  
+- Evaluating augmentation and generalization strategies in vision models
+
+---
+
+## 🛠️ Generation Pipeline
+
+All data was generated using the `.hip` scene:  
+```bash
+./houdini/digitgen_v02.hip
+```
+
+## 🧪 Methodology
+
+### 1. Procedural Digit Assembly
+- Each digit `0`–`9` is generated using a random font in each frame via Houdini’s **Font SOP**.
+- Digits are arranged in a clean **8×8 grid**, forming sprite sheets with **64 digits per render**.
+
+### 2. Scene Variability
+- Fonts are randomly selected per frame.
+- Procedural distortions are applied including:
+  - Rotation
+  - Translation
+  - Skew
+  - Mountain noise displacement
+- This ensures high variability across samples.
+
+### 3. Rendering
+- Scene renders are executed via **Mantra** or **Karma**.
+- Output format: **grayscale 224×224 px** sprite sheets (`.exr` or `.jpg`).
+
+### 4. Compositing & Cropping
+- A **COP2 network** slices the sprite sheet into **28×28** digit tiles.
+- Each tile is labeled by its original digit and saved to:
+```
+./output/0/img_00001.jpg
+./output/1/img_00001.jpg
+...
+```
+
+### 🧾 Dataset Structure
+
+```bash
+mnist_bakery_data/
+├── 0/
+│   ├── img_00001.jpg
+│   ├── ...
+├── 1/
+│   ├── img_00001.jpg
+│   └── ...
+...
+├── 9/
+│   └── img_00001.jpg
+```
+
+
+- All images: grayscale `.jpg`, 28×28 resolution
+- Total: **40,960 samples**
+- ~4,096 samples per digit
+
+---
+
+## 📊 Statistics
+
+| Set       | Samples | Mean    | StdDev   |
+|-----------|---------|---------|----------|
+| MNIST     | 60,000  | 0.1307  | 0.3081   |
+| Synthetic | 40,960  | 0.01599 | 0.07722  |
+
+> Combine mean/std using weighted averaging if mixing both datasets.
+
+---
+
+## 📚 Usage Example
+
+```python
+from torchvision import transforms, datasets
+
+transform = transforms.Compose([
+    transforms.Grayscale(),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.01599], std=[0.07722])  # Approximate weighted normalization
+])
+
+dataset = datasets.ImageFolder('./mnist_bakery_data', transform=transform)
+```
+
+---
+
+#### 🧠 Credits
+
+**Author**: Aaron T. Carter
+
+**Organization**: Arkaen Solutions
+
+**Tools Used**: Houdini, PyTorch, PIL
+
+___
+