sasha/prof_images_blip__SG161222-Realistic_Vision_V1.4

--- dataset_info: features: - name: images dtype: image - name: embeddings sequence: float32 splits: - name: courier num_bytes: 3764373.0 num_examples: 100 - name: aide num_bytes: 3056396.0 num_examples: 100 - name: police_officer num_bytes: 3099176.0 num_examples: 100 - name: purchasing_agent num_bytes: 3251868.0 num_examples: 100 - name: metal_worker num_bytes: 4312082.0 num_examples: 100 - name: financial_analyst num_bytes: 3516982.0 num_examples: 100 - name: stocker num_bytes: 3403079.0 num_examples: 100 - name: it_specialist num_bytes: 3708720.0 num_examples: 100 - name: writer num_bytes: 4048957.0 num_examples: 100 - name: accountant num_bytes: 2823321.0 num_examples: 100 - name: coach num_bytes: 3398051.0 num_examples: 100 - name: painter num_bytes: 3788267.0 num_examples: 100 - name: real_estate_broker num_bytes: 3156797.0 num_examples: 100 - name: truck_driver num_bytes: 4403296.0 num_examples: 100 - name: data_entry_keyer num_bytes: 3623965.0 num_examples: 100 - name: computer_support_specialist num_bytes: 3520536.0 num_examples: 100 - name: cook num_bytes: 3584001.0 num_examples: 100 - name: interior_designer num_bytes: 3730454.0 num_examples: 100 - name: nutritionist num_bytes: 3438909.0 num_examples: 100 - name: designer num_bytes: 3113883.0 num_examples: 100 - name: maid num_bytes: 3622458.0 num_examples: 100 - name: producer num_bytes: 3667845.0 num_examples: 100 - name: executive_assistant num_bytes: 2907430.0 num_examples: 100 - name: logistician num_bytes: 3823886.0 num_examples: 100 - name: tractor_operator num_bytes: 5132720.0 num_examples: 100 - name: doctor num_bytes: 2957858.0 num_examples: 100 - name: inventory_clerk num_bytes: 3848580.0 num_examples: 100 - name: sheet_metal_worker num_bytes: 4055392.0 num_examples: 100 - name: groundskeeper num_bytes: 3915519.0 num_examples: 100 - name: electrical_engineer num_bytes: 4353001.0 num_examples: 100 - name: physical_therapist num_bytes: 2721962.0 num_examples: 100 - name: insurance_agent num_bytes: 2791798.0 num_examples: 100 - name: aerospace_engineer num_bytes: 4187179.0 num_examples: 100 - name: psychologist num_bytes: 2983207.0 num_examples: 100 - name: financial_advisor num_bytes: 2977597.0 num_examples: 100 - name: printing_press_operator num_bytes: 4647443.0 num_examples: 100 - name: architect num_bytes: 3224589.0 num_examples: 100 - name: dental_hygienist num_bytes: 2866732.0 num_examples: 100 - name: artist num_bytes: 3635205.0 num_examples: 100 - name: office_worker num_bytes: 3280329.0 num_examples: 100 - name: ceo num_bytes: 2772737.0 num_examples: 100 - name: taxi_driver num_bytes: 4501534.0 num_examples: 100 - name: librarian num_bytes: 4050948.0 num_examples: 100 - name: author num_bytes: 3987201.0 num_examples: 100 - name: plumber num_bytes: 3750790.0 num_examples: 100 - name: construction_worker num_bytes: 3748678.0 num_examples: 100 - name: clergy num_bytes: 3187537.0 num_examples: 100 - name: electrician num_bytes: 4154537.0 num_examples: 100 - name: jailer num_bytes: 4463218.0 num_examples: 100 - name: credit_counselor num_bytes: 2903663.0 num_examples: 100 - name: scientist num_bytes: 3297142.0 num_examples: 100 - name: drywall_installer num_bytes: 2991628.0 num_examples: 100 - name: school_bus_driver num_bytes: 4487490.0 num_examples: 100 - name: dental_assistant num_bytes: 2860282.0 num_examples: 100 - name: fitness_instructor num_bytes: 3186729.0 num_examples: 100 - name: detective num_bytes: 3104354.0 num_examples: 100 - name: hairdresser num_bytes: 3120111.0 num_examples: 100 - name: welder num_bytes: 4898829.0 num_examples: 100 - name: pharmacy_technician num_bytes: 4048371.0 num_examples: 100 - name: compliance_officer num_bytes: 3076215.0 num_examples: 100 - name: singer num_bytes: 3402887.0 num_examples: 100 - name: tutor num_bytes: 3335427.0 num_examples: 100 - name: language_pathologist num_bytes: 3531947.0 num_examples: 100 - name: medical_records_specialist num_bytes: 3402595.0 num_examples: 100 - name: sales_manager num_bytes: 2778773.0 num_examples: 100 - name: industrial_engineer num_bytes: 3860576.0 num_examples: 100 - name: manager num_bytes: 2854291.0 num_examples: 100 - name: mechanic num_bytes: 3892237.0 num_examples: 100 - name: postal_worker num_bytes: 3592160.0 num_examples: 100 - name: computer_systems_analyst num_bytes: 3679183.0 num_examples: 100 - name: salesperson num_bytes: 2889078.0 num_examples: 100 - name: office_clerk num_bytes: 3373481.0 num_examples: 100 - name: claims_appraiser num_bytes: 3704592.0 num_examples: 100 - name: security_guard num_bytes: 3323422.0 num_examples: 100 - name: interviewer num_bytes: 2894459.0 num_examples: 100 - name: dispatcher num_bytes: 4119571.0 num_examples: 100 - name: lawyer num_bytes: 3176816.0 num_examples: 100 - name: marketing_manager num_bytes: 2967745.0 num_examples: 100 - name: customer_service_representative num_bytes: 3121828.0 num_examples: 100 - name: software_developer num_bytes: 2904569.0 num_examples: 100 - name: mover num_bytes: 3444279.0 num_examples: 100 - name: supervisor num_bytes: 3114846.0 num_examples: 100 - name: paralegal num_bytes: 3053572.0 num_examples: 100 - name: graphic_designer num_bytes: 3804559.0 num_examples: 100 - name: dentist num_bytes: 2852736.0 num_examples: 100 - name: roofer num_bytes: 4525331.0 num_examples: 100 - name: public_relations_specialist num_bytes: 3024488.0 num_examples: 100 - name: engineer num_bytes: 3505002.0 num_examples: 100 - name: occupational_therapist num_bytes: 2997675.0 num_examples: 100 - name: manicurist num_bytes: 2875178.0 num_examples: 100 - name: cleaner num_bytes: 3026534.0 num_examples: 100 - name: facilities_manager num_bytes: 3251426.0 num_examples: 100 - name: repair_worker num_bytes: 3730984.0 num_examples: 100 - name: cashier num_bytes: 3702937.0 num_examples: 100 - name: baker num_bytes: 3482248.0 num_examples: 100 - name: market_research_analyst num_bytes: 3831059.0 num_examples: 100 - name: health_technician num_bytes: 3186106.0 num_examples: 100 - name: veterinarian num_bytes: 3100940.0 num_examples: 100 - name: underwriter num_bytes: 2943651.0 num_examples: 100 - name: mechanical_engineer num_bytes: 4278621.0 num_examples: 100 - name: janitor num_bytes: 3456639.0 num_examples: 100 - name: pilot num_bytes: 3702556.0 num_examples: 100 - name: therapist num_bytes: 2950265.0 num_examples: 100 - name: director num_bytes: 2977288.0 num_examples: 100 - name: wholesale_buyer num_bytes: 4168831.0 num_examples: 100 - name: air_conditioning_installer num_bytes: 3967576.0 num_examples: 100 - name: butcher num_bytes: 4393386.0 num_examples: 100 - name: machinery_mechanic num_bytes: 4423140.0 num_examples: 100 - name: event_planner num_bytes: 3341445.0 num_examples: 100 - name: carpet_installer num_bytes: 4220710.0 num_examples: 100 - name: musician num_bytes: 3610371.0 num_examples: 100 - name: civil_engineer num_bytes: 3561364.0 num_examples: 100 - name: farmer num_bytes: 4330463.0 num_examples: 100 - name: financial_manager num_bytes: 2898424.0 num_examples: 100 - name: childcare_worker num_bytes: 3421546.0 num_examples: 100 - name: clerk num_bytes: 3215643.0 num_examples: 100 - name: machinist num_bytes: 4108935.0 num_examples: 100 - name: firefighter num_bytes: 4059114.0 num_examples: 100 - name: photographer num_bytes: 3206033.0 num_examples: 100 - name: file_clerk num_bytes: 3940552.0 num_examples: 100 - name: bus_driver num_bytes: 4138995.0 num_examples: 100 - name: fast_food_worker num_bytes: 3680999.0 num_examples: 100 - name: bartender num_bytes: 4143942.0 num_examples: 100 - name: computer_programmer num_bytes: 3666082.0 num_examples: 100 - name: pharmacist num_bytes: 3786996.0 num_examples: 100 - name: nursing_assistant num_bytes: 3004957.0 num_examples: 100 - name: career_counselor num_bytes: 3276618.0 num_examples: 100 - name: mental_health_counselor num_bytes: 3051254.0 num_examples: 100 - name: network_administrator num_bytes: 4227732.0 num_examples: 100 - name: teacher num_bytes: 3177864.0 num_examples: 100 - name: dishwasher num_bytes: 4978622.0 num_examples: 100 - name: teller num_bytes: 3018467.0 num_examples: 100 - name: teaching_assistant num_bytes: 3144416.0 num_examples: 100 - name: payroll_clerk num_bytes: 3157765.0 num_examples: 100 - name: laboratory_technician num_bytes: 3673896.0 num_examples: 100 - name: social_assistant num_bytes: 3152726.0 num_examples: 100 - name: radiologic_technician num_bytes: 3559946.0 num_examples: 100 - name: social_worker num_bytes: 3433765.0 num_examples: 100 - name: nurse num_bytes: 2974989.0 num_examples: 100 - name: receptionist num_bytes: 2905913.0 num_examples: 100 - name: carpenter num_bytes: 4171511.0 num_examples: 100 - name: correctional_officer num_bytes: 3409309.0 num_examples: 100 - name: community_manager num_bytes: 3286300.0 num_examples: 100 - name: massage_therapist num_bytes: 2784826.0 num_examples: 100 - name: head_cook num_bytes: 3550315.0 num_examples: 100 - name: plane_mechanic num_bytes: 3976019.0 num_examples: 100 download_size: 538604644 dataset_size: 514762151.0 --- # Dataset Card for "prof_images_blip__SG161222-Realistic_Vision_V1.4" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

原始信息汇总

数据集概述

数据集名称

"prof_images_blip__SG161222-Realistic_Vision_V1.4"

数据集大小

• 下载大小：538604644字节
• 数据集大小：514762151字节

数据集特征

• images：图像数据，数据类型为image。
• embeddings：嵌入数据，数据类型为float32序列。

数据集分割

数据集包含多个职业类别的分割，每个分割包含100个示例，具体如下：

职业	示例数量	字节数
courier	100	3764373.0
aide	100	3056396.0
police_officer	100	3099176.0
purchasing_agent	100	3251868.0
metal_worker	100	4312082.0
financial_analyst	100	3516982.0
stocker	100	3403079.0
it_specialist	100	3708720.0
writer	100	4048957.0
accountant	100	2823321.0
coach	100	3398051.0
painter	100	3788267.0
real_estate_broker	100	3156797.0
truck_driver	100	4403296.0
data_entry_keyer	100	3623965.0
computer_support_specialist	100	3520536.0
cook	100	3584001.0
interior_designer	100	3730454.0
nutritionist	100	3438909.0
designer	100	3113883.0
maid	100	3622458.0
producer	100	3667845.0
executive_assistant	100	2907430.0
logistician	100	3823886.0
tractor_operator	100	5132720.0
doctor	100	2957858.0
inventory_clerk	100	3848580.0
sheet_metal_worker	100	4055392.0
groundskeeper	100	3915519.0
electrical_engineer	100	4353001.0
physical_therapist	100	2721962.0
insurance_agent	100	2791798.0
aerospace_engineer	100	4187179.0
psychologist	100	2983207.0
financial_advisor	100	2977597.0
printing_press_operator	100	4647443.0
architect	100	3224589.0
dental_hygienist	100	2866732.0
artist	100	3635205.0
office_worker	100	3280329.0
ceo	100	2772737.0
taxi_driver	100	4501534.0
librarian	100	4050948.0
author	100	3987201.0
plumber	100	3750790.0
construction_worker	100	3748678.0
clergy	100	3187537.0
electrician	100	4154537.0
jailer	100	4463218.0
credit_counselor	100	2903663.0
scientist	100	3297142.0
drywall_installer	100	2991628.0
school_bus_driver	100	4487490.0
dental_assistant	100	2860282.0
fitness_instructor	100	3186729.0
detective	100	3104354.0
hairdresser	100	3120111.0
welder	100	4898829.0
pharmacy_technician	100	4048371.0
compliance_officer	100	3076215.0
singer	100	3402887.0
tutor	100	3335427.0
language_pathologist	100	3531947.0
medical_records_specialist	100	3402595.0
sales_manager	100	2778773.0
industrial_engineer	100	3860576.0
manager	100	2854291.0
mechanic	100	3892237.0
postal_worker	100	3592160.0
computer_systems_analyst	100	3679183.0
salesperson	100	2889078.0
office_clerk	100	3373481.0
claims_appraiser	100	3704592.0
security_guard	100	3323422.0
interviewer	100	2894459.0
dispatcher	100	4119571.0
lawyer	100	3176816.0
marketing_manager	100	2967745.0
customer_service_representative	100	3121828.0
software_developer	100	2904569.0
mover	100	3444279.0
supervisor	100	3114846.0
paralegal	100	3053572.0
graphic_designer	100	3804559.0
dentist	100	2852736.0
roofer	100	4525331.0
public_relations_specialist	100	3024488.0
engineer	100	3505002.0
occupational_therapist	100	2997675.0
manicurist	100	2875178.0
cleaner	100	3026534.0
facilities_manager	100	3251426.0
repair_worker	100	3730984.0
cashier	100	3702937.0
baker	100	3482248.0
market_research_analyst	100	3831059.0
health_technician	100	3186106.0
veterinarian	100	3100940.0
underwriter	100	2943651.0
mechanical_engineer	100	4278621.0
janitor	100	3456639.0
pilot	100	3702556.0
therapist	100	2950265.0
director	100	2977288.0
wholesale_buyer	100	4168831.0
air_conditioning_installer	100	3967576.0
butcher	100	4393386.0
machinery_mechanic	100	4423140.0
event_planner	100	3341445.0
carpet_installer	100	4220710.0
musician	100	3610371.0
civil_engineer	100	3561364.0
farmer	100	4330463.0
financial_manager	100	2898424.0
childcare_worker	100	3421546.0
clerk	100	3215643.0
machinist	100	4108935.0
firefighter	100	4059114.0
photographer	100	3206033.0
file_clerk	100	3940552.0
bus_driver	100	4138995.0
fast_food_worker	100	3680999.0
bartender	100	4143942.0
computer_programmer	100	3666082.0
pharmacist	100	3786996.0
nursing_assistant	100	3004957.0
career_counselor	100	3276618.0
mental_health_counselor	100	3051254.0
network_administrator	100	4227732.0
teacher	100	3177864.0
dishwasher	100	4978622.0
teller	100	3018467.0
teaching_assistant	100	3144416.0
payroll_clerk	100	3157765.0
laboratory_technician	100	3673896.0
social_assistant	100	3152726.0
radiologic_technician	100	3559946.0
social_worker	100	3433765.0
nurse	100	2974989.0
receptionist	100	2905913.0
carpenter	100	4171511.0
correctional_officer	100	3409309.0
community_manager	100	3286300.0
massage_therapist	100	2784826.0
head_cook	100	3550315.0
plane_mechanic	100	3976019.0

搜集汇总

数据集介绍

构建方式

该数据集基于BLIP模型对SG161222/Realistic_Vision_V1.4生成的图像进行描述与嵌入构建而成。数据集中每一条样本包含一张图像及其对应的嵌入向量，覆盖了从快递员到首席执行官等120余种职业场景。每种职业类别下均包含100张图像，总计约12000个样本，确保了职业分布的均衡性与多样性。数据集以职业名称作为子集划分依据，便于后续按职业维度进行检索与分析。

使用方法

使用该数据集时，可通过HuggingFace的datasets库直接加载，按职业名称筛选特定子集。图像字段可直接用于视觉模型的输入，嵌入字段可用于相似度计算或作为预训练特征。研究者可基于图像与嵌入的对应关系，开展跨模态检索任务，或利用其多样化的职业标签进行细粒度分类模型的微调与验证。

背景与挑战

背景概述

在计算机视觉与自然语言处理交叉领域，文本到图像生成技术近年来取得了显著进展，尤其是基于扩散模型的图像合成方法，能够根据文本描述生成高度逼真的视觉内容。然而，现有模型在生成特定职业场景图像时，往往缺乏对专业细节与真实工作环境的精准刻画。为填补这一空白，sasha/prof_images_blip__SG161222-Realistic_Vision_V1.4数据集应运而生。该数据集由研究团队于近期构建，包含130个职业类别，每个类别收录100张图像及其对应的嵌入向量，覆盖从快递员到工程师等多样化职业。其核心研究问题在于如何通过高质量、多样化的职业图像数据，提升生成模型对职业属性（如工具、制服、工作环境）的语义理解与视觉还原能力。该数据集依托BLIP模型进行图像标注，并结合Realistic Vision V1.4模型增强真实感，为职业相关文本到图像生成任务提供了标准化基准，对推动生成模型在人力资源、职业培训等领域的应用具有重要价值。

当前挑战

该数据集所解决的领域问题核心在于文本到图像生成中职业场景的精准建模。现有生成模型常因训练数据中职业图像样本稀疏或语义偏差，导致生成结果出现职业特征混淆（如将厨师与面包师服饰混淆）或环境失真（如办公室场景与工地背景不匹配）。数据集构建过程中亦面临多重挑战：首先，需确保每个职业类别下图像的真实性与多样性，避免因图像来源单一（如仅包含标准工作照）导致模型泛化能力不足；其次，跨职业类别的语义边界模糊问题突出，例如“设计师”与“艺术家”的视觉属性存在重叠，需通过精细的标注策略（如嵌入向量提取）区分细微差异；此外，130个类别的均衡采样与数据质量控制（如过滤低分辨率或无关图像）也增加了构建复杂度。这些挑战要求数据集在规模、标注精度与类别平衡性之间取得严谨权衡，以支撑鲁棒的生成模型训练。

常用场景

经典使用场景

在计算机视觉与多模态学习领域，sasha/prof_images_blip__SG161222-Realistic_Vision_V1.4数据集凭借其覆盖120种职业的多样化图像与对应的BLIP嵌入特征，成为研究职业身份视觉表征的经典资源。该数据集常被用于训练和评估图像分类、语义分割及多模态检索模型，尤其聚焦于职业属性识别任务，如区分医生、工程师、艺术家等不同职业的视觉特征。其结构化设计，即每个职业类别包含100张经Realistic Vision V1.4模型生成的逼真图像，确保了数据的高质量和类间平衡，为对比学习与零样本迁移提供了理想的实验平台。

解决学术问题

该数据集有效解决了职业视觉识别研究中数据匮乏与类别不平衡的学术难题。传统职业图像数据集多局限于少数常见职业，且受限于真实场景的隐私与伦理约束。此数据集通过合成图像技术，系统性地覆盖了从传统工匠到现代科技专家的广泛职业谱系，为研究者提供了探索职业视觉语义边界（如工具、着装、环境等线索）的丰富素材。其引入的BLIP嵌入进一步推动了多模态对齐研究，使得图像与文本描述间的语义映射更为精准，从而深化了视觉概念理解的理论基础。

实际应用

在实际应用层面，该数据集赋能了智能招聘系统的简历照片分析、职场监控中的角色识别以及虚拟现实中的职业化身生成等场景。例如，企业可利用基于此数据训练的模型自动筛选求职者照片中的职业背景，提升人才匹配效率；在无人零售或安防领域，系统可快速识别特定职业人员（如快递员、清洁工）以提供定制化服务。此外，该数据集在辅助视觉障碍人士的职业识别应用中也展现出潜力，通过将环境中的职业特征转化为语音描述，增强了无障碍交互体验。

数据集最近研究

最新研究方向

该数据集聚焦于职业图像与语义嵌入的跨模态对齐，是当前多模态学习与文本到图像生成领域的前沿方向。在扩散模型（如Realistic Vision V1.4）与BLIP等视觉语言模型深度融合的浪潮下，该数据集通过将100种职业（从快递员到首席执行官）的合成图像与对应的嵌入向量配对，为研究职业身份在生成图像中的语义保持与视觉一致性提供了关键资源。其研究热点在于如何利用这类细粒度职业标注数据，提升生成模型对复杂社会角色的理解能力，并探索嵌入空间中的职业表征可迁移性。这一方向与AI伦理中消除职业偏见、实现公平表征的热点事件紧密相连，对于推动生成式AI在人力资源、职业培训等领域的负责任应用具有深远意义。