[{"@type":"PropertyValue","name":"Data size","value":"50,000 sets"},{"@type":"PropertyValue","name":"Object types","value":"person, animals, products, plants, landscapes, etc."},{"@type":"PropertyValue","name":"Editing types","value":"human attribute editing, image semantic editing, image structural editin"},{"@type":"PropertyValue","name":"Resolution","value":"no less than 1080p in principle"},{"@type":"PropertyValue","name":"Data parameters","value":"image formats include .jpg, .jpeg, .png, and other common formats; editing text format is .txt"},{"@type":"PropertyValue","name":"Annotation content","value":"edit the object s in the image according to the editing instructions"},{"@type":"PropertyValue","name":"Accuracy rate","value":"the edited image data must comply with the data requirements, with no significant mismatches with the original image; the accuracy rate must not be lower than 95%. The mask edges should be within a 5-"}]
{"id":1785,"datatype":"1","titleimg":"https://www.nexdata.ai/shujutang/static/image/index/datatang_tuxiang_default.webp","type1":"226","type1str":null,"type2":"254","type2str":null,"dataname":"50,000 Image Editing Datasets – Object Removal, Addition & Modification Dataset for AI Training","datazy":[{"isCheckLength":true,"title":"Data size","content":"50,000 sets"},{"isCheckLength":true,"title":"Object types","content":"person, animals, products, plants, landscapes, etc."},{"isCheckLength":true,"title":"Editing types","content":"human attribute editing, image semantic editing, image structural editin"},{"isCheckLength":true,"title":"Resolution","content":"no less than 1080p in principle"},{"isCheckLength":true,"title":"Data parameters","content":"image formats include .jpg, .jpeg, .png, and other common formats; editing text format is .txt"},{"isCheckLength":true,"title":"Annotation content","content":"edit the object s in the image according to the editing instructions"},{"isCheckLength":true,"title":"Accuracy rate","content":"the edited image data must comply with the data requirements, with no significant mismatches with the original image; the accuracy rate must not be lower than 95%. The mask edges should be within a 5-"}],"datatag":"Image Editing","technologydoc":null,"downurl":null,"datainfo":null,"standard":null,"dataylurl":null,"flag":null,"publishtime":null,"createby":null,"createtime":null,"ext1":null,"samplestoreloc":null,"hosturl":null,"datasize":null,"industryPlan":null,"keyInformation":"","samplePresentation":[{"name":"1.png","url":"https://storage-product.datatang.com/damp/product/sample_presentation/20250722165023/1.png?Expires=4102415999&OSSAccessKeyId=LTAI5tEBeSWUJiqjXvBMsxEu&Signature=3JSJEjg1oR%2FMsPj9QuayDBEBovc%3D","intro":"","size":189612,"progress":100,"type":"jpg"},{"name":"2.png","url":"https://storage-product.datatang.com/damp/product/sample_presentation/20250722165023/2.png?Expires=4102415999&OSSAccessKeyId=LTAI5tEBeSWUJiqjXvBMsxEu&Signature=YpquvgOg4rcnZufbX3cEnqTKEVQ%3D","intro":"","size":570136,"progress":100,"type":"jpg"},{"name":"3.png","url":"https://storage-product.datatang.com/damp/product/sample_presentation/20250722165023/3.png?Expires=4102415999&OSSAccessKeyId=LTAI5tEBeSWUJiqjXvBMsxEu&Signature=q%2B63crH9jNz%2BSstv8xU35doYYeQ%3D","intro":"","size":346413,"progress":100,"type":"jpg"}],"officialSummary":"50,000 Sets - Image Editing Data. The editing types include human attribute editing, image semantic editing, and image structure editing. The editing targets cover scenes such as people, animals, goods, plants, and landscapes. In terms of annotation, based on the editing instructions, the targets that need to be edited in the image are edited. The data can be used for tasks such as image synthesis, data augmentation, and virtual scene generation.","dataexampl":null,"datakeyword":["image editing dataset","image synthesis data","object removal dataset","object addition data","AI image generation dataset","virtual scene dataset","annotated image editing data","inpainting dataset","AI training data for image manipulation","generative image dataset"],"isDelete":null,"ids":null,"idsList":null,"datasetCode":null,"productStatus":null,"tagTypeEn":"Type","tagTypeZh":null,"website":null,"samplePresentationList":null,"datazyList":null,"keyInformationList":null,"dataexamplList":null,"bgimg":null,"datazyScriptList":null,"datakeywordListString":null,"sourceShowPage":"llm","dataShowType":"[{\"code\":\"0\",\"language\":\"ZH\"},{\"code\":\"1\",\"language\":\"ZH\"},{\"code\":\"2\",\"language\":\"EN,PT,DE,KO,FR,ES\"},{\"code\":\"3\",\"language\":\"EN\"}]","productNameEn":"50,000 Sets - Image Editing Data","BGimg":"","voiceBg":["/shujutang/static/image/comm/audio_bg.webp","/shujutang/static/image/comm/audio_bg2.webp","/shujutang/static/image/comm/audio_bg3.webp","/shujutang/static/image/comm/audio_bg4.webp","/shujutang/static/image/comm/audio_bg5.webp"]}
50,000 Image Editing Datasets – Object Removal, Addition & Modification Dataset for AI Training
image editing dataset
image synthesis data
object removal dataset
object addition data
AI image generation dataset
virtual scene dataset
annotated image editing data
inpainting dataset
AI training data for image manipulation
generative image dataset
50,000 Sets - Image Editing Data. The editing types include human attribute editing, image semantic editing, and image structure editing. The editing targets cover scenes such as people, animals, goods, plants, and landscapes. In terms of annotation, based on the editing instructions, the targets that need to be edited in the image are edited. The data can be used for tasks such as image synthesis, data augmentation, and virtual scene generation.
This is a paid dataset licensed for commercial use. Ready-made datasets are available for immediate integration into AI projects.
Specifications
Data size
50,000 sets
Object types
person, animals, products, plants, landscapes, etc.
Editing types
human attribute editing, image semantic editing, image structural editin
Resolution
no less than 1080p in principle
Data parameters
image formats include .jpg, .jpeg, .png, and other common formats; editing text format is .txt
Annotation content
edit the object s in the image according to the editing instructions
Accuracy rate
the edited image data must comply with the data requirements, with no significant mismatches with the original image; the accuracy rate must not be lower than 95%. The mask edges should be within a 5-
Nexdata’s LLM datasets can support a wide range of large language model development tasks, including pre-training, supervised fine-tuning, instruction tuning, preference optimization, evaluation, and domain-specific model development. Depending on the dataset, data may include text, instruction-response pairs, conversations, question-answer pairs, reasoning data, and other structured or annotated content.
Can Nexdata customize LLM datasets based on our specific model and requirements?
Yes. If our off-the-shelf LLM datasets do not fully match your requirements, Nexdata provides flexible custom data collection, generation, annotation, curation, and quality control services. We can customize datasets based on your target languages, domains, use cases, data formats, task types, volume, and quality requirements to support specific LLM training and evaluation projects.
Can Nexdata provide large-scale and high-quality data for LLM development?
Yes. Nexdata can support large-scale LLM data projects across multiple languages, domains, and data types. Our data services include multi-stage quality control, data cleaning, annotation, validation, and curation to help ensure consistency and usability. For projects requiring data beyond our existing datasets, our customized data services can be scaled according to the required volume, specifications, and delivery schedule.
Specifications
Sample
Recommended Dataset
