Switch dataset to parquet shards

.gitattributes

@@ -1,60 +1 @@
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README.md

@@ -17,48 +17,78 @@ configs:
 - config_name: default
   data_files:
   - split: train
-    path: train/**
+    path: train-*.parquet
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+        - The Eiffel Tower
+        - airplane
+        - angel
+        - bed
+        - chair
+        - clock
+        - diamond
+        - donut
+        - fork
+        - frog
+        - hourglass
+        - leaf
+        - line
+        - mushroom
+        - octagon
+        - palm tree
+        - pants
+        - pencil
+        - square
+        - squiggle
+  - name: label_name
+    dtype: string
 ---
 
 # QuickDraw-MNIST
 
 QuickDraw-MNIST is a 20-class sketch-recognition dataset prepared for Texas A&M's CSCE 624 (Sketch Recognition) class.
 
-The data is sourced from Google's Quick, Draw! dataset and repackaged into a Hugging Face `ImageFolder` dataset so students can load it with a single `load_dataset(...)` call.
+The data is sourced from Google's Quick, Draw! dataset and repackaged into Hugging Face parquet shards so students can load it with a single `load_dataset(...)` call.
 
 ## Dataset Structure
 
 - Split included here: `train` only
 - Number of images: `100,000`
 - Number of classes: `20`
-- Images: `64 x 64` grayscale PNGs
-- Labels are inferred from the class folder names
+- Images: `64 x 64` grayscale
+- Labels: integer class ids with a human-readable `label_name` column
 
 Classes: `The Eiffel Tower`, `airplane`, `angel`, `bed`, `chair`, `clock`, `diamond`, `donut`, `fork`, `frog`, `hourglass`, `leaf`, `line`, `mushroom`, `octagon`, `palm tree`, `pants`, `pencil`, `square`, `squiggle`
 
 ## Loading The Dataset
 
-Replace `YOUR_USERNAME/quickdraw-mnist` with the final Hugging Face dataset repo name.
-
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("YOUR_USERNAME/quickdraw-mnist", split="train")
+dataset = load_dataset("oriyonay/quickdraw-mnist", split="train")
 print(dataset)
 print(dataset[0])
 ```
 
-To convert examples into tensors for PyTorch:
+For PyTorch:
 
 ```python
-import torch
+from datasets import load_dataset
 from torchvision import transforms
 
+dataset = load_dataset("oriyonay/quickdraw-mnist", split="train")
 to_tensor = transforms.ToTensor()
 
 example = dataset[0]
 image = to_tensor(example["image"])   # shape: [1, 64, 64], values in [0, 1]
 label = example["label"]
+label_name = example["label_name"]
 ```
 
 ## Source
@@ -69,4 +99,4 @@ label = example["label"]
 ## Notes For Students
 
 - This repository intentionally contains only the training split.
-- Use your own train/validation split during model development.
+- Create your own train/validation split for model development.

dataset_manifest.json

@@ -1,6 +1,8 @@
 {
   "pretty_name": "QuickDraw-MNIST",
-  "num_images": 100000,
+  "format": "parquet",
+  "split": "train",
+  "num_examples": 100000,
   "num_classes": 20,
   "image_size": 64,
   "class_names": [
@@ -25,28 +27,7 @@
     "square",
     "squiggle"
   ],
-  "class_folder_names": [
-    "the_eiffel_tower",
-    "airplane",
-    "angel",
-    "bed",
-    "chair",
-    "clock",
-    "diamond",
-    "donut",
-    "fork",
-    "frog",
-    "hourglass",
-    "leaf",
-    "line",
-    "mushroom",
-    "octagon",
-    "palm_tree",
-    "pants",
-    "pencil",
-    "square",
-    "squiggle"
-  ],
   "source_dataset": "Google Quick, Draw!",
-  "course": "Texas A&M CSCE 624 (Sketch Recognition)"
+  "course": "Texas A&M CSCE 624 (Sketch Recognition)",
+  "num_shards": 20
 }

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