Upload dataset (part 00001-of-00002)

README.md

@@ -1,5 +1,10 @@
 ---
 license: mit
+size_categories:
+- 100M<n<1B
+task_categories:
+- image-to-text
+pretty_name: vision2ui
 dataset_info:
   features:
   - name: image
@@ -10,16 +15,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 4189624847.8344607
-    num_examples: 19514
+    num_bytes: 21648361007.165703
+    num_examples: 67847
   - name: test
-    num_bytes: 523649431.37584555
-    num_examples: 2439
+    num_bytes: 2706085010.417149
+    num_examples: 8481
   - name: val
-    num_bytes: 523864129.7896938
-    num_examples: 2440
-  download_size: 4642434734
-  dataset_size: 5237138409
+    num_bytes: 2706085010.417149
+    num_examples: 8481
+  download_size: 24385961099
+  dataset_size: 27060531028.0
 configs:
 - config_name: default
   data_files:
@@ -29,13 +34,8 @@ configs:
     path: data/test-*
   - split: val
     path: data/val-*
-task_categories:
-- image-to-text
 tags:
 - code
-pretty_name: vision2ui
-size_categories:
-- 100M<n<1B
 ---
 VISION2UI: A Real-World Dataset with Layout for Code Generation from UI Designs
 > Automatically generating UI code from webpage design visions can significantly alleviate the burden of developers, enabling beginner developers or designers to directly generate Web pages from design diagrams. Currently, prior research has accomplished the objective of generating UI code from rudimentary design visions or sketches through designing deep neural networks. Inspired by the groundbreaking advancements achieved by Multimodal Large Language Models (MLLMs), the automatic generation of UI code from high-fidelity design images is now emerging as a viable possibility. Nevertheless, our investigation reveals that existing MLLMs are hampered by the scarcity of authentic, high-quality, and large-scale datasets, leading to unsatisfactory performance in automated UI code generation. To mitigate this gap, we present a novel dataset, termed VISION2UI, extracted from real-world scenarios, augmented with comprehensive layout information, tailored specifically for finetuning MLLMs in UI code generation. Specifically, this dataset is derived through a series of operations, encompassing collecting, cleaning, and filtering of the open-source Common Crawl dataset. In order to uphold its quality, a neural scorer trained on labeled samples is utilized to refine the data, retaining higher-quality instances. Ultimately, this process yields a dataset comprising 2,000 (Much more is coming soon) parallel samples encompassing design visions and UI code. 

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