metadata
language:
- en
- zh
license: apache-2.0
tags:
- computer vision
- image classification
- screen scene recognition
- display chip AI
- edge AI
size_categories:
- 10B<n<100B
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 3DS
'1': Anime
'2': Apex
'3': CF
'4': CSGO
'5': DeltaForce
'6': Dota2
'7': FJWJ
'8': Face
'9': Forza
'10': Genshin
'11': Kart
'12': LOL
'13': Landscape
'14': Overwatch
'15': PPT
'16': PS
'17': PUBG
'18': QQspeed
'19': Sport
'20': Word
'21': yanyun
splits:
- name: train
pretty_name: Screen Scene Recognition Dataset for Display Chip
Screen Scene Recognition Dataset for Display Chip
Dataset Description
This dataset is specifically designed for edge-side AI model development of display chips, targeting real-time recognition of 22 types of screen scenes. It addresses the pain points of missing public datasets, high category similarity, and poor data quality in screen scene recognition tasks, providing high-quality labeled data for algorithm research and engineering deployment.
Overview
- Total Samples: 53,438 high-quality cleaned images
- Number of Categories: 22 distinct screen scenes
- Average Samples per Category: ~2,429
- Image Quality: All images are processed to remove black borders, duplicate samples (via hash algorithm), and other quality issues
Dataset Structure
Categories & Labels
Dataset Structure
| Category | Subcategory | Quantity (Images) | Dataset Source |
|---|---|---|---|
| Games | CSGO | 4151 | roboflow: skie-u1yzr/csgo-tr4j7 |
| Games | Delta Force | 2237 | roboflow: yolov11-hs6o5/delta-force-wtowy |
| Games | CrossFire (CF) | 2945 | roboflow: ahmed-kakpz/crossfire-q3x9b roboflow: yoloproject-qkr8c/crossfire-aimbot-pnt3a roboflow: t-kch-enemies-gr/crossfire-enemies-gr roboflow: boyang-atqjy/cf-d1lea roboflow: learning-yn484/cf-tqspc roboflow: cfv10/cf-ktskc roboflow: chris-cbzkl/cf-yrljb roboflow: yiku/cf-yolo11 roboflow: lin-swjrf/cf-person roboflow: heavebk/crossfire-zvd56 roboflow: cf-twgc7/cf-kfnnp |
| Games | Overwatch | 2291 | roboflow: divertisseur-g7gui/overwatch-sqw1k roboflow: 872858554-qq-com/overwatch-djt7x |
| Games | PUBG | 2533 | roboflow: legendarynuggets-y8wml/pubg-xggpl roboflow: workspace-5ry2i/pubg-imo8q roboflow: gwycc/pubg-ij9vn roboflow: see-ul5qe/pubg-1gopr roboflow: luizconrado/pubg-rhc8l roboflow: 2799283008-qq-com/pubg-jaw28 roboflow: projects-r3ul8/pubg-v2ujr roboflow: yolo-hsg3o/pubg-hf77u roboflow: aipubg/pubg-lmzib |
| Games | Apex | 3257 | roboflow: new-workspace-kv0mx/apex-saofm roboflow: 1-0jgxn/apex-s9gtn roboflow: new-workspace-kv0mx/apex-wtj6x roboflow: tristen-2bfd5/apex-ssde6 roboflow: edward-ixd04/apex-zbkbp roboflow: auner2456889-gp1gr/apex-rxk8r roboflow: apex-nceuf/apex-x5hna |
| Games | League of Legends (LOL) | 1971 | roboflow: atrashrc-gmail-com/league-of-legends-mtuzl roboflow: glass-cmdst/league-of-legends-wlfmr roboflow: markus-srensen/league-of-legends-7au4o roboflow: league-of-legends-dud09/league-of-legends-ydjt2 roboflow: mapleland-sdm9k/league-of-legends-eyiej |
| Games | Dota2 | 2202 | roboflow: sake-rj73p/dota2-qkzjc roboflow: dota-nk9sm/dota2-c1q0w roboflow: laixuxiang/dota2-mwuk0 roboflow: fds-exzra/dota2-lasthitter roboflow: d2-a2ij4/augmented-dota2 |
| Games | Fantasy Westward Journey (FWJ) | 1483 | roboflow: yolo-1nxke/-s7y0p roboflow: yolo-1nxke/-eaf1c roboflow: dingsu/-iq8dn |
| Games | Yanyun | 2364 | Screen recording capture |
| Games | Genshin Impact | 3242 | roboflow: will-end-o2a5r/genshin-li3di roboflow: samokat/genshin-uus5a |
| Games | KartRider | 2966 | roboflow: testworkspace/kartrider roboflow: kof98/kartrider-i9xqq Screen recording capture |
| Games | Forza Horizon 4 | 2696 | roboflow: gaurav-tpig5/forza-horizon-4 roboflow: robot-my7f9/forza roboflow: placas-n7ft2/forza-gz8bv roboflow: deertracker/forza-roads Screen recording capture |
| Games | QQ Speed | 3650 | Screen recording capture |
| Office | Word | 1952 | huggingface: likaixin/ScreenSpot-Pro Screen recording capture |
| Office | PowerPoint (PPT) | 1046 | Screen recording capture |
| Industrial Software | 3DS | 1430 | Screen recording capture |
| Graphics Software | Photoshop (PS) | 1456 | Screen recording capture |
| Media | Face | 2204 | kaggle: dataturks/face-detection-in-images |
| Media | Landscape | 3237 | kaggle: utkarshsaxenadn/landscape-recognition-image-dataset-12k-images roboflow: master-v2adj/landscape-xtmqp |
| Media | Sport | 2056 | kaggle: sidharkal/sports-image-classification |
| Media | Anime | 2069 | huggingface: yskor/anime_background_city_street huggingface: svjack/Anime_Background_Images huggingface: Sebastian2602/AnimeSceneData roboflow: anime-search/anime-search-g6irh roboflow: anime-search-2/anime-search-2 roboflow: oggidetectiondataset/anime-detecter roboflow: hakdog-tmdnj/anime-ebf4j roboflow: juan-pablo-ruiz-flrez/anime-9cqdu roboflow: test-kgq7k/anime-ymci2 roboflow: noname-4sril/anime-gun Screen recording capture |
Data Splits
- Full Dataset: 53,438 samples (no predefined train/val/test splits; users are recommended to split according to their own needs)
Data Collection & Preprocessing
Data Sources
- Game Scenes: Filtered from Roboflow screen target detection datasets (labels removed) and manually captured gameplay footage
- Office/Productivity: Manually captured via screen recording (Word, PPT, PS, etc.)
- Other Scenes: Collected from public datasets and manual screen captures
Preprocessing Steps
- Black Border Removal: Cropped invalid black border areas to focus on valid screen content
- Deduplication: Used hash algorithm to eliminate duplicate images
- Class Balance: Applied targeted data augmentation and class weight assignment for imbalanced categories
- Quality Control: Manual cleaning of low-quality/blurry images
Usage
This dataset is suitable for:
- Research and training of screen scene classification models for edge devices
- Performance comparison of lightweight CNN models (ResNet18, MobileNetV2) on edge AI tasks
- Engineering optimization of display chip-side real-time scene recognition
License
Apache License 2.0
Citation
If you use this dataset in your research, please cite: @dataset {screen_scene_recognition_2026, author = {amazingtrash}, title = {Screen Scene Recognition Dataset for Display Chip}, year = {2026}, url = {https://huggingface.co/datasets/amazingtrash/scenario-recognition-for-display}, license = {Apache-2.0}}
Contact
For questions about the dataset, please contact: 2350222@tongji.edu.cn