diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..c87076e1ba20d136b2082b44c1f1019a1dd2ca94 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,30 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_0926.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_1872.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_1774.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_0982.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_0947.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_0985.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_1087.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_1826.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_0858.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_1403.JPG filter=lfs diff=lfs merge=lfs -text +table3-dataset/images/IMG_1731.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_0946.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1581.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1577.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1624.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_0921.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1608.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_0893.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_0874.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_0892.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1579.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_0931.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1711.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1417.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1723.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1880.JPG filter=lfs diff=lfs merge=lfs -text +table1-dataset/images/IMG_1755.JPG filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..05a80245db0d764a4174230b6c91c8a6ee2addeb --- /dev/null +++ b/README.md @@ -0,0 +1,147 @@ +--- +license: cc-by-nc-4.0 +pretty_name: NavOCR Test Sets +language: +- ko +- en +task_categories: +- object-detection +- image-to-text +tags: +- scene-text +- ocr +- robot-navigation +- signboard +size_categories: +- 1K + +"Navigation-relevant" text is text a person or robot would use to say where they +are, mostly shop and place names. Menus, prices, slogans and opening hours are +not navigation-relevant. + +| Set | Images | Ground truth | Used for | +| --- | --- | --- | --- | +| `table3-dataset` | 1,554 | 1,695 boxes, COCO format | Detection AP (paper Table 3) | +| `table1-dataset` | 304 (subset of Table 3) | Hand-written text list per image | Text-list F1 (paper Table 1) | + +These are test sets only. There is no training split. + +## Files + +``` +table3-dataset/ + images/ 1,554 JPEG images + instances_test.json COCO ground truth, one category: navigation-oriented-text +table1-dataset/ + images/ 304 JPEG images, identical to their Table 3 copies + f1-score-GT_final.txt one line per image +croissant.json Croissant metadata (core + RAI) +``` + +### Table 3: `instances_test.json` + +Standard COCO detection format. `bbox` is `[x, y, width, height]` in pixels of the +released image. Each box marks one text instance. + +### Table 1: `f1-score-GT_final.txt` + +One line per image: the file name, then three brace fields. + +``` +20251215_135837.jpg, {H&M, CHARLES & KEITH, McDonald's}, {H&M, 찰스앤키스, 맥도날드}, {에이치앤엠, Charles & Keith, McDonald's} +``` + +1. The navigation-relevant strings exactly as written on the signs. **This is the + ground truth that every method is scored against.** +2. The place names as listed on Naver Map. +3. Translations of those place names (Korean to English or English to Korean). + +Fields 2 and 3 are the query used by the NavOCR generator, which keeps OCR +strings similar to a known place name. Methods that are meant to work without a +map, such as the OCR + LLM baseline, should not read them. + +## Annotation guideline + +Table 3 boxes follow these rules: + +- One text instance per place. When the same shop shows several signs, only one + is annotated. +- Different places get separate boxes. +- Text more than about 20 m away is not annotated. +- Text on surfaces more than 50° off the viewing direction is not annotated. + +Two of the authors annotated the images. Each image was annotated by one +person, who drew the boxes by hand in a custom tool, with no OCR pre-annotation. +For Table 1, the on-sign strings and the Naver Map place names were written by +hand; the translations in field 3 were generated by an AI model. + +Known issue: 13 images contain a second box for a place that is already +annotated. They are left as they were annotated and used for the reported +results. + +## Collection + +The authors photographed storefronts and street scenes with smartphones, from a +pedestrian's point of view, between October 2025 and October 2026. Most images +are from South Korea, mainly Seoul and Daejeon; a few show signs abroad. Most +scenes are shopping malls and street-level shops. Phones: iPhone 14 Pro, +iPhone 15 Pro, iPhone 17, Galaxy S24+ and Galaxy S26. + +Every image was capped at 1080 px on the long side before annotation (Lanczos, +JPEG quality 95), so the released resolution is the annotated resolution. +Most images are 1080×810 or 810×1080; 191 are 360×480 and 13 are 1024×768. + +## Privacy + +- **Faces are blurred.** Detector proposals (CenterFace via + [deface](https://github.com/ORB-HD/deface), and SCRFD-10G) were reviewed image + by image for all 1,554 images. Missed people were added, including people seen + from behind or from the side, and false positives were removed. 2,050 faces in + 711 images are blurred. No blur overlaps a ground-truth text box. +- **Faces on advertisements, posters and mannequins may remain visible.** +- **Licence plates are not masked.** +- **All metadata is removed:** EXIF (including GPS, which 204 originals carried), + XMP, and the second image embedded in MPO files. Images without faces keep + their original pixels. + +If you find an identifiable person or other content that should be removed, +contact chlee@kc-ml2.com and it will be removed in a new revision. + +## Intended use + +Use these sets to evaluate: + +- detectors and OCR pipelines that find navigation-relevant text (Table 3, COCO AP); +- methods that pick place names out of OCR output (Table 1, text-list F1). + +Do not use them to identify, track or recognise people, for surveillance, or for +any commercial purpose. + +## Limitations and biases + +- Mostly South Korea (Seoul, Daejeon), mostly commercial areas and indoor + malls. Text is mostly Korean and English. +- Taken with five phone models. +- Place names favour chain brands, which language models may recognise more + easily than independent shops. +- 191 images are low resolution (360×480). +- The ground truth reflects the navigation-relevance definition above, not all + visible text. + +## License + +[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Non-commercial +use only, with attribution. diff --git a/croissant.json b/croissant.json new file mode 100644 index 0000000000000000000000000000000000000000..64ebbc078163d7097535b9d3d31d769f0cd8c7cb --- /dev/null +++ b/croissant.json @@ -0,0 +1,351 @@ +{ + "@context": { + "@language": "en", + "@vocab": "https://schema.org/", + "citeAs": "cr:citeAs", + "column": "cr:column", + "conformsTo": "dct:conformsTo", + "cr": "http://mlcommons.org/croissant/", + "rai": "http://mlcommons.org/croissant/RAI/", + "data": { + "@id": "cr:data", + "@type": "@json" + }, + "dataType": { + "@id": "cr:dataType", + "@type": "@vocab" + }, + "dct": "http://purl.org/dc/terms/", + "equivalentProperty": "cr:equivalentProperty", + "examples": { + "@id": "cr:examples", + "@type": "@json" + }, + "extract": "cr:extract", + "field": "cr:field", + "fileProperty": "cr:fileProperty", + "fileObject": "cr:fileObject", + "fileSet": "cr:fileSet", + "format": "cr:format", + "includes": "cr:includes", + "isLiveDataset": "cr:isLiveDataset", + "jsonPath": "cr:jsonPath", + "key": "cr:key", + "md5": "cr:md5", + "parentField": "cr:parentField", + "path": "cr:path", + "recordSet": "cr:recordSet", + "references": "cr:references", + "regex": "cr:regex", + "repeated": "cr:repeated", + "replace": "cr:replace", + "samplingRate": "cr:samplingRate", + "sc": "https://schema.org/", + "separator": "cr:separator", + "source": "cr:source", + "subField": "cr:subField", + "transform": "cr:transform" + }, + "@type": "sc:Dataset", + "conformsTo": "http://mlcommons.org/croissant/1.0", + "name": "NavOCR-TestSets", + "description": "Test sets for navigation-relevant text detection, released with 'NavOCR: A Dataset Generator for Navigation-Relevant Text Detection in Mobile Robots' (NeurIPS 2026 Evaluations & Datasets Track). Table 3: 1,554 street and mall images with 1,695 hand-drawn boxes (COCO). Table 1: a 304-image subset with hand-written lists of navigation-relevant strings. Faces are blurred and all metadata removed.", + "license": "https://creativecommons.org/licenses/by-nc/4.0/", + "url": "https://huggingface.co/datasets/ML2/navocr-testsets", + "version": "1.0.0", + "citeAs": "NavOCR: A Dataset Generator for Navigation-Relevant Text Detection in Mobile Robots. NeurIPS 2026 Evaluations & Datasets Track.", + "keywords": [ + "scene text detection", + "OCR", + "robot navigation", + "signboards", + "Korean" + ], + "inLanguage": [ + "ko", + "en" + ], + "distribution": [ + { + "@type": "cr:FileObject", + "@id": "table3-annotations", + "name": "table3-annotations", + "description": "Table 3 ground truth in COCO format (1,554 images, 1,695 boxes).", + "contentUrl": "table3-dataset/instances_test.json", + "encodingFormat": "application/json", + "sha256": "ea93c4bad23e61858ba6545f86c6fa7ed72454e58757350de26289b46b03f2bb" + }, + { + "@type": "cr:FileObject", + "@id": "table3-image-entries", + "name": "table3-image-entries", + "description": "Same file as table3-annotations, read for its 'images' list.", + "contentUrl": "table3-dataset/instances_test.json", + "encodingFormat": "application/json", + "sha256": "ea93c4bad23e61858ba6545f86c6fa7ed72454e58757350de26289b46b03f2bb" + }, + { + "@type": "cr:FileSet", + "@id": "table3-images", + "name": "table3-images", + "description": "Table 3 images.", + "encodingFormat": "image/jpeg", + "includes": "table3-dataset/images/*" + }, + { + "@type": "cr:FileObject", + "@id": "table1-gt", + "name": "table1-gt", + "description": "Table 1 ground truth, one line per image: name, {sign text}, {Naver Map place names}, {translations}.", + "contentUrl": "table1-dataset/f1-score-GT_final.txt", + "encodingFormat": "text/plain", + "sha256": "d284d3692f5b2736259a1f747c290fb782af2755f18d0fdb0fce6ea0b48fc6d1" + }, + { + "@type": "cr:FileSet", + "@id": "table1-images", + "name": "table1-images", + "description": "Table 1 images (subset of Table 3).", + "encodingFormat": "image/jpeg", + "includes": "table1-dataset/images/*" + } + ], + "recordSet": [ + { + "@type": "cr:RecordSet", + "@id": "table3_images", + "name": "table3_images", + "description": "Images of table3-images.", + "key": { + "@id": "table3_images/file_name" + }, + "field": [ + { + "@type": "cr:Field", + "@id": "table3_images/file_name", + "name": "file_name", + "description": "Image file name.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "table3-images" + }, + "extract": { + "fileProperty": "filename" + } + } + }, + { + "@type": "cr:Field", + "@id": "table3_images/image", + "name": "image", + "description": "Image content.", + "dataType": "sc:ImageObject", + "source": { + "fileSet": { + "@id": "table3-images" + }, + "extract": { + "fileProperty": "content" + } + } + } + ] + }, + { + "@type": "cr:RecordSet", + "@id": "table3_image_info", + "name": "table3_image_info", + "description": "COCO image entries of the Table 3 ground truth.", + "field": [ + { + "@type": "cr:Field", + "@id": "table3_image_info/id", + "name": "id", + "description": "COCO image id.", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "table3-image-entries" + }, + "extract": { + "jsonPath": "$.images[*].id" + } + } + }, + { + "@type": "cr:Field", + "@id": "table3_image_info/file_name", + "name": "file_name", + "description": "Image file name.", + "dataType": "sc:Text", + "source": { + "fileObject": { + "@id": "table3-image-entries" + }, + "extract": { + "jsonPath": "$.images[*].file_name" + } + } + }, + { + "@type": "cr:Field", + "@id": "table3_image_info/width", + "name": "width", + "description": "Width in pixels.", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "table3-image-entries" + }, + "extract": { + "jsonPath": "$.images[*].width" + } + } + }, + { + "@type": "cr:Field", + "@id": "table3_image_info/height", + "name": "height", + "description": "Height in pixels.", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "table3-image-entries" + }, + "extract": { + "jsonPath": "$.images[*].height" + } + } + } + ] + }, + { + "@type": "cr:RecordSet", + "@id": "table3_boxes", + "name": "table3_boxes", + "description": "Navigation-relevant text boxes (COCO annotations, single category 'navigation-oriented-text').", + "field": [ + { + "@type": "cr:Field", + "@id": "table3_boxes/id", + "name": "id", + "description": "Annotation id.", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "table3-annotations" + }, + "extract": { + "jsonPath": "$.annotations[*].id" + } + } + }, + { + "@type": "cr:Field", + "@id": "table3_boxes/image_id", + "name": "image_id", + "description": "COCO image id the box belongs to.", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "table3-annotations" + }, + "extract": { + "jsonPath": "$.annotations[*].image_id" + } + } + }, + { + "@type": "cr:Field", + "@id": "table3_boxes/category_id", + "name": "category_id", + "description": "Always 1.", + "dataType": "sc:Integer", + "source": { + "fileObject": { + "@id": "table3-annotations" + }, + "extract": { + "jsonPath": "$.annotations[*].category_id" + } + } + }, + { + "@type": "cr:Field", + "@id": "table3_boxes/bbox", + "name": "bbox", + "description": "Box as [x, y, width, height] in pixels.", + "dataType": "sc:Float", + "source": { + "fileObject": { + "@id": "table3-annotations" + }, + "extract": { + "jsonPath": "$.annotations[*].bbox" + } + }, + "repeated": true + } + ] + }, + { + "@type": "cr:RecordSet", + "@id": "table1_images", + "name": "table1_images", + "description": "Images of table1-images.", + "key": { + "@id": "table1_images/file_name" + }, + "field": [ + { + "@type": "cr:Field", + "@id": "table1_images/file_name", + "name": "file_name", + "description": "Image file name.", + "dataType": "sc:Text", + "source": { + "fileSet": { + "@id": "table1-images" + }, + "extract": { + "fileProperty": "filename" + } + } + }, + { + "@type": "cr:Field", + "@id": "table1_images/image", + "name": "image", + "description": "Image content.", + "dataType": "sc:ImageObject", + "source": { + "fileSet": { + "@id": "table1-images" + }, + "extract": { + "fileProperty": "content" + } + } + } + ] + } + ], + "rai:dataCollection": "Photographs of storefronts and street scenes taken by the authors with smartphones (iPhone 14 Pro, iPhone 15 Pro, iPhone 17, Galaxy S24+, Galaxy S26) while walking, from a pedestrian's point of view, between October 2025 and October 2026. Mostly South Korea (mainly Seoul and Daejeon), a few signs abroad; mostly shopping malls and street-level shops.", + "rai:dataCollectionType": "Manual Human Curated; Direct measurement (smartphone photographs)", + "rai:dataCollectionRawData": "Full-resolution phone originals are not released. Images were capped at 1080 px on the long side before annotation (Lanczos, JPEG q95), so the released resolution is the annotated resolution. 191 images are 360x480 reduced exports and 13 are 1024x768.", + "rai:dataCollectionMissingData": "None. Every image has at least one Table 3 box; Table 1 lists one GT line per image.", + "rai:dataPreprocessingProtocol": "1) Long side capped at 1080 px, ground truth rescaled accordingly. 2) Faces blurred: candidate boxes from CenterFace (deface 1.5.0) and SCRFD-10G (insightface buffalo_l), then every one of the 1,554 images was reviewed by hand in a custom GUI, adding missed people (including back-of-head and side views) and removing false positives. 2,050 faces in 711 images are blurred with an elliptical downsample-then-Gaussian blur covering 1.3x the reviewed box; no blur touches a ground-truth text box. 3) All EXIF/XMP metadata (including GPS in 204 originals) and the second embedded image of MPO files were removed; images without faces keep their pixels unchanged.", + "rai:dataAnnotationProtocol": "Table 3 (detection): one axis-aligned box per navigation-relevant text instance. Rules: one text instance per place, even when the same shop shows several signs; separate places get separate boxes; text more than about 20 m away or on surfaces more than 50 degrees off the viewing direction is not annotated. Table 1 (text lists): for each image, the navigation-relevant strings exactly as written on the sign, the place names as listed on Naver Map, and their translations. Boxes were drawn by hand with no OCR pre-annotation. Naver Map place names were looked up by hand; translations were generated by an AI model.", + "rai:dataAnnotationPlatform": "Custom in-house box-drawing tool.", + "rai:dataAnnotationAnalysis": "An audit of all 110 Table 3 images with more than one box found 13 images where the same place was boxed twice, against the one-per-place rule; they are left as annotated. Each image was annotated once, with no second-pass review.", + "rai:annotationsPerItem": "One annotator per image.", + "rai:annotatorDemographics": "Two of the authors.", + "rai:machineAnnotationTools": "Text annotations: none for boxes and on-sign strings (hand-drawn and hand-written); translations of place names (Table 1, field 3) generated by an AI model. Face blur: CenterFace (deface 1.5.0) and SCRFD-10G proposals, all reviewed by hand.", + "rai:personalSensitiveInformation": "Images are taken in public places and contain bystanders. Faces of real people are blurred after full manual review; faces printed on advertisements, posters and mannequins may remain visible. Licence plates are not masked. All EXIF metadata, including GPS coordinates and device information, is removed. Shop names and signage are public business information.", + "rai:dataBiases": "Geographically concentrated in South Korea (mainly Seoul and Daejeon) and dominated by commercial areas, many of them indoor shopping malls; text is mostly Korean and English. Captured with five phone models in daylight or indoor lighting. Place names favour chain brands, which a language model may recognise more easily than independent shops.", + "rai:dataLimitations": "Test sets only, with no training split. Modest size (1,554 and 304 images). Ground truth reflects the navigation-relevance definition above, not all visible text. 13 Table 3 images contain a duplicate box for the same place. 191 images are low resolution (360x480). Blurred regions may slightly change OCR behaviour near people.", + "rai:dataUseCases": "Intended: evaluating detectors and OCR pipelines that find navigation-relevant text (Table 3, COCO AP) and methods that pick place names from OCR output (Table 1, text-list F1), as in the NavOCR paper. Not intended: identifying or tracking people, face recognition, surveillance, or any commercial use (CC BY-NC 4.0).", + "rai:dataSocialImpact": "Supports robots and assistive navigation tools that orient themselves by reading shop signs. Privacy risk to bystanders is reduced by face blurring and metadata removal; remaining identifiable content can be reported for removal.", + "rai:dataReleaseMaintenancePlan": "Versioned on Hugging Face; fixes are released as new revisions. Removal requests and error reports: chlee@kc-ml2.com." +} diff --git a/table1-dataset/f1-score-GT_final.txt b/table1-dataset/f1-score-GT_final.txt new file mode 100644 index 0000000000000000000000000000000000000000..631d0183efa2b3f7363ee827c4a9f799e81e0ffc --- /dev/null +++ b/table1-dataset/f1-score-GT_final.txt @@ -0,0 +1,304 @@ +20251215_135837.jpg, {H&M, CHARLES & KEITH, McDonald's}, {H&M, 찰스앤키스, 맥도날드}, {에이치앤엠, Charles & Keith, McDonald's} +20251215_135913.jpg, {emart24}, {이마트24}, {emart24} +20251215_135925.jpg, {THE NORTH FACE}, {노스페이스}, {The North Face} +20251215_135936.jpg, {WHO.A.U}, {후아유}, {WHO.A.U} +20251215_140118.jpg, {Champion}, {챔피언}, {Champion} +20251215_140148.jpg, {STUDIO TOMBOY}, {스튜디오톰보이}, {Studio Tomboy} +20251215_140152.jpg, {SWATCH, 8 seconds}, {스와치, 8 세컨즈}, {Swatch, 8 Seconds} +20251215_140246.jpg, {ARC'TERYX}, {아크테릭스}, {Arc'teryx} +20251215_140324.jpg, {HOLLYS}, {할리스}, {Hollys} +20251215_140410.jpg, {LEGO}, {레고}, {LEGO} +20251215_140416.jpg, {PLATFORM PLACE}, {플랫폼플레이스}, {Platform Place} +20251215_140439.jpg, {Bene Bene}, {베네베네}, {Bene Bene} +20251215_140520.jpg, {ZARA}, {자라}, {Zara} +20251215_140541.jpg, {TWEE}, {트위}, {Twee} +20251215_140604.jpg, {skinband}, {스킨밴드}, {Skinband} +20251215_140622.jpg, {MIXXO}, {미쏘}, {MIXXO} +20251215_140644.jpg, {LEVI'S}, {리바이스}, {Levi's} +20251215_140701.jpg, {TRUE RELIGION}, {트루릴리젼}, {True Religion} +20251215_140725.jpg, {ZARA HOME}, {ZARA HOME}, {자라홈} +20251215_140753.jpg, {crocs}, {크록스}, {Crocs} +20251215_140833.jpg, {SHOOPEN}, {슈펜}, {Shoopen} +20251215_140847.jpg, {Saboten}, {사보텐}, {Saboten} +20251215_140915.jpg, {BUTTER}, {버터}, {Butter} +20251215_140944.jpg, {CHICOR}, {시코르}, {Chicor} +20251215_141015.jpg, {KERVAN, Le Saigon}, {케르반, 르사이공}, {Kervan, Le Saigon} +20251215_141035.jpg, {PALAZZO}, {빨라쪼델프레도}, {Palazzo del Freddo} +20251215_141149.jpg, {서울무역센터우체국}, {서울무역센터우체국}, {Seoul Trade Center Post Office} +20251215_141221.jpg, {팜엑스약국}, {팜엑스약국}, {Pharm X Pharmacy} +20251215_141259.jpg, {안경진정성, WINENARA}, {안경진정성, 와인나라}, {Glasses Sincerity, Wine Nara} +20251215_141511.jpg, {PAPAYA LEAF}, {파파야리프}, {Papaya Leaf} +20251215_141521.jpg, {HOTSTORY}, {핫스토리}, {Hot Story} +20251215_141559.jpg, {CUCHARA}, {쿠차라}, {Cuchara} +20251215_141624.jpg, {NUNU COFFEE}, {누누커피}, {Nunu Coffee} +20251215_141639.jpg, {기소야}, {기소야}, {Gisoya} +20251215_141647.jpg, {일상정원}, {일상정원}, {Ilsang Garden} +20251215_141658.jpg, {홍수계찜닭, FIREBELL}, {홍수계찜닭, 파이어벨}, {Hong Sugye Jjimdak, Firebell} +20251215_141751.jpg, {인드라망}, {인드라망}, {Indramang} +20251215_141814.jpg, {BIEN}, {비앙갤러리}, {Bien Gallery} +20251215_141900.jpg, {EPICELLAR}, {에피셀라}, {Epicellar} +20251215_142020.jpg, {MUZZEL, Fun it}, {뮤젤, 펀잇}, {Muzzel, Fun it} +20251215_142518.jpg, {Hall B2}, {Hall B}, {홀 B} +20251215_142619.jpg, {Information}, {Information}, {안내} +20251215_142717.jpg, {DEVIL'S DOOR}, {데블스도어}, {Devil's Door} +20251215_142726.jpg, {No Brand Burger}, {노브랜드버거}, {No Brand Burger} +20251215_142826.jpg, {신한은행}, {신한은행}, {Shinhan Bank} +20251215_142859.jpg, {TERAROSA COFFEE}, {테라로사}, {Terarosa} +20251215_142913.jpg, {california PIZZA KITCHEN}, {캘리포니아피자키친}, {California Pizza Kitchen} +20251215_142937.jpg, {Hall A2}, {Hall A}, {홀 A} +20251215_143131.jpg, {Conference Room South}, {Conference Room South}, {컨퍼런스룸 사우스} +20251215_143142.jpg, {Conference Room E}, {Conference Room E}, {컨퍼런스룸 E} +20251215_143213.jpg, {Conference Room South, 무역아카데미}, {Conference Room South, 무역아카데미}, {컨퍼런스룸 사우스, Trade Academy} +20251215_143249.jpg, {무역아카데미 ICT교육센터}, {무역아카데미 ICT교육센터}, {Trade Academy ICT Education Center} +20251215_143612.jpg, {NANING9}, {난닝구}, {Naning9} +20251215_143623.jpg, {VIVIEN}, {비비안}, {Vivien} +20251215_143634.jpg, {LIST}, {리스트}, {LIST} +20251215_143647.jpg, {solb}, {솔브}, {Solb} +20251215_143653.jpg, {innisfree}, {이니스프리}, {Innisfree} +20251215_143710.jpg, {Roem}, {로엠}, {Roem} +20251215_143718.jpg, {ROCKPORT, PANDORA}, {락포트, 판도라}, {Rockport, Pandora} +20251215_143735.jpg, {DrMartens}, {닥터마틴}, {Dr. Martens} +20251215_143751.jpg, {ALDO, IFNE}, {알도, 이프네}, {Aldo, Ifne} +20251215_143850.jpg, {casamia}, {까사미아}, {Casamia} +20251215_143915.jpg, {NABITA}, {나비타월드}, {Navita World} +20251215_144011.jpg, {baskin BR robbins}, {배스킨라빈스}, {Baskin Robbins} +20251215_144221.jpg, {Bath & Body Works}, {배스앤바디웍스}, {Bath & Body Works} +20251223_135636.jpg, {HERMES, Sulwhasoo, VALMONT, TIFFANY&CO}, {에르메스, 설화수, 발몽, 티파니앤코}, {Hermes, Sulwhasoo, Valmont, Tiffany & Co.} +20251223_135657.jpg, {oera, SK-II, LOUIS VUITTON}, {오에라, SK-II, 루이비통}, {Oera, 에스케이투, Louis Vuitton} +20251223_135746.jpg, {Cartier}, {까르띠에}, {Cartier} +20251223_135758.jpg, {HERA, GOYARD, CHANTECAILLE}, {헤라, 고야드, 샹테카이}, {Hera, Goyard, Chantecaille} +20251223_135852.jpg, {Roger Vivien, SAINT LAURENT}, {Roger Vivien, 입생로랑뷰티}, {로저 비비에, Yves Saint Laurent Beauty} +20251223_135901.jpg, {CELINE}, {셀린느}, {Celine} +20251223_135904.jpg, {BALENCIAGA}, {발렌시아가}, {Balenciaga} +20251223_135925.jpg, {BOTTEGA VENETA}, {보테가 베네타}, {Bottega Veneta} +20251223_140150.jpg, {Hastens}, {해스텐스}, {Hastens} +20251223_140245.jpg, {a shop}, {에이샵}, {A Shop} +20251223_140329.jpg, {ETTOI, bugaboo, STOKKE}, {에뜨와, 부가부, 스토케}, {Ettoi, Bugaboo, Stokke} +20251223_140354.jpg, {Panasonic}, {파나소닉}, {Panasonic} +20251223_140503.jpg, {BERENICE}, {베르니스}, {Bernice} +20251223_140511.jpg, {NUMBERPROJECT}, {넘버프로젝트}, {Number Project} +20251223_140553.jpg, {ARTIRRISO}, {ARTIRRISO}, {아티리소} +20251223_140602.jpg, {COS}, {COS}, {코스} +20251223_140948.jpg, {sisley}, {시슬리}, {Sisley} +20251223_141031.jpg, {ORIGINS}, {오리진스}, {Origins} +20251223_141351.jpg, {THE ITALIAN CLUB}, {더이탈리안클럽}, {The Italian Club} +20261011_165618.jpg, {o'slo}, {오슬로}, {O'slo} +20261011_171129.jpg, {DESIGN SKIN}, {디자인스킨}, {Design Skin} +20261011_171233.jpg, {DELIETO}, {델리에또}, {Delietto} +20261011_171608.jpg, {BURBERRY}, {버버리}, {Burberry} +20261011_172412.jpg, {CDG}, {꼼데가르송CDG}, {Comme des Garcons CDG} +20261013_132025.jpg, {COMPOSE COFFEE}, {컴포즈커피}, {Compose Coffee} +20261013_132155.jpg, {LOTTERIA}, {롯데리아}, {Lotteria} +20261013_132354.jpg, {HANS LABELLE}, {한스라벨르}, {Hans Labelle} +20261013_132427.jpg, {KINK COFFEE}, {킨크커피}, {Kink Coffee} +20261013_132816.jpg, {동천약국}, {동천약국}, {Dongcheon Pharmacy} +20261013_132840.jpg, {CAFE OHANA}, {카페오하나}, {Cafe Ohana} +20261013_132857.jpg, {She's}, {Shes}, {쉬즈} +20261013_133611.jpg, {MEGA COFFEE}, {메가MGC커피}, {Mega MGC Coffee} +20261013_133640.jpg, {DUNKIN}, {던킨}, {Dunkin} +20261013_133655.jpg, {Apartmentary}, {아파트멘터리}, {Apartmentary} +20261013_133720.jpg, {YOGURT WORLD}, {요거트월드}, {Yogurt World} +20261013_134146.jpg, {SERIAL GOURMET 2.0}, {시리얼고메2.0}, {Serial Gourmet 2.0} +IMG_0703.jpg, {Q CopyQ24}, {카피큐24}, {CopyQ24} +IMG_0712.jpg, {YELLOW TRUNK}, {옐로트렁크}, {Yellow Trunk} +IMG_0731.jpg, {GROUND B}, {그라운드 비}, {Ground B} +IMG_0738.jpg, {MAD COFFEE}, {매드커피}, {Mad Coffee} +IMG_0760.JPG, {MONTBLANC}, {몽블랑}, {Montblanc} +IMG_0785.JPG, {RAIVE}, {레이브}, {Raive} +IMG_0800.JPG, {SATUR}, {세터}, {Satur} +IMG_0810.JPG, {Garrett}, {가렛팝콘}, {Garrett Popcorn} +IMG_0821.JPG, {ALEXANDRE}, {알렉산드르드파리}, {Alexandre de Paris} +IMG_0868.JPG, {LE LABO}, {르라보}, {Le Labo} +IMG_0874.JPG, {bills}, {빌즈}, {bills} +IMG_0880.JPG, {PALACE}, {팔라스스케이트보드}, {Palace Skateboards} +IMG_0883.JPG, {UNIQLO}, {유니클로}, {UNIQLO} +IMG_0885.JPG, {ADERERROR}, {아더에러}, {ADER Error} +IMG_0892.JPG, {Aesop}, {이솝}, {Aesop} +IMG_0893.JPG, {NONFICTION}, {논픽션}, {Nonfiction} +IMG_0896.JPG, {BYREDO}, {바이레도}, {Byredo} +IMG_0902.JPG, {LENWICH}, {렌위치}, {Lenwich} +IMG_0909.JPG, {PRETTICA}, {프레티카}, {Prettica} +IMG_0914.JPG, {ARKET}, {아르켓}, {ARKET} +IMG_0921.JPG, {Atelier Nain}, {아틀리에 나인}, {Atelier Nain} +IMG_0924.JPG, {MYSHELL}, {마이쉘}, {My Shell} +IMG_0931.JPG, {NATIONAL GEOGRAPHIC}, {내셔널지오그래픽}, {National Geographic} +IMG_0933.JPG, {VANS}, {반스}, {Vans} +IMG_0936.JPG, {HUNTER}, {헌터}, {Hunter} +IMG_0937.JPG, {Carols}, {캐롤스}, {Carols} +IMG_0939.JPG, {Reebok}, {리복}, {Reebok} +IMG_0940.JPG, {snow peak}, {스노우피크}, {Snow Peak} +IMG_0942.JPG, {HDEX}, {HDEX}, {에이치덱스} +IMG_0946.JPG, {umbro}, {엄브로}, {Umbro} +IMG_0949.JPG, {GREGORY}, {그레고리}, {Gregory} +IMG_0950.JPG, {AIGLE}, {에이글}, {Aigle} +IMG_0954.JPG, {Giants}, {롯데자이언츠샵}, {Lotte Giants Shop} +IMG_0958.JPG, {ABC-MART}, {ABC마트}, {ABC-Mart} +IMG_0960.JPG, {sloggi}, {슬로기}, {Sloggi} +IMG_0961.JPG, {GIORDANO}, {지오다노}, {Giordano} +IMG_0971.JPG, {TOPTEN KIDS}, {탑텐키즈}, {Topten Kids} +IMG_0977.JPG, {ORGANIC MOM}, {오가닉맘}, {Organic Mom} +IMG_0978.JPG, {MLB KIDS}, {MLB키즈}, {MLB Kids} +IMG_0981.JPG, {FAO SCHWARZ}, {FAO슈와츠}, {FAO Schwarz} +IMG_1010.JPG, {BASILUR SUNSET AVENUE}, {베질루르선셋에비뉴}, {Basilur Sunset Avenue} +IMG_1088.JPG, {STAR CLASS}, {스타클래스}, {Star Class} +IMG_1093.JPG, {COFFEENIE}, {커피니}, {Coffeenie} +IMG_1097.JPG, {Feel}, {필}, {Feel} +IMG_1099.JPG, {GANJI BATTLE}, {간지배틀}, {Ganji Battle} +IMG_1115.JPG, {BakeHouse}, {베이크하우스}, {Bake House} +IMG_1119.JPG, {브라운돈까스}, {브라운돈까스}, {Brown Tonkatsu} +IMG_1120.JPG, {CU}, {CU}, {씨유} +IMG_1128.JPG, {Nice Caffein Club}, {나이스카페인클럽}, {Nice Caffeine Club} +IMG_1142.JPG, {MARTHA}, {마르타}, {Martha} +IMG_1174.JPG, {Say Cheese}, {세이치즈}, {Say Cheese} +IMG_1177.JPG, {N.N.N. FLOWER}, {엔엔엔플라워}, {N.N.N. Flower} +IMG_1188.JPG, {여기정육식당}, {여기정육식당}, {Yeogi Butcher Restaurant} +IMG_1195.JPG, {J&J BOXING}, {제이앤제이복싱짐}, {J&J Boxing Gym} +IMG_1200.JPG, {OOZY COFFEE}, {우지커피}, {Oozy Coffee} +IMG_1212.JPG, {Pointe Pointe}, {포인테커피}, {Pointe Coffee} +IMG_1218.JPG, {@NOLEATTER}, {놀잇터}, {Noleatter} +IMG_1226.JPG, {ALL THE WAY}, {올더웨이}, {All The Way} +IMG_1227.JPG, {LOOMP}, {LOOMP}, {룸프} +IMG_1232.JPG, {JESSE's Pizza}, {JESSES PIZZA}, {제시스 피자} +IMG_1236.JPG, {PANGZ}, {팡즈}, {Pangz} +IMG_1248.JPG, {BULKUK}, {벌컥}, {Bulkuk} +IMG_1255.JPG, {Pizzeria Lago}, {피제리아라고}, {Pizzeria Lago} +IMG_1261.JPG, {SEOUL PANCAKE}, {서울 팬케이크}, {Seoul Pancake} +IMG_1392.JPG, {BODYFRIEND}, {바디프랜드}, {Bodyfriend} +IMG_1394.JPG, {MAPLE BURGER}, {메이플버거}, {Maple Burger} +IMG_1417.JPG, {U+}, {LG유플러스}, {LG U+} +IMG_1443.JPG, {ISAAC TOAST}, {이삭토스트}, {Isaac Toast} +IMG_1463.JPG, {Kawasaki}, {가와사키}, {Kawasaki} +IMG_1465.JPG, {ELFAMA}, {엘파마}, {Elfama} +IMG_1483.JPG, {GARMIN}, {가민 브랜드샵}, {Garmin Brand Shop} +IMG_1487.JPG, {TOUS les JOURS}, {뚜레쥬르}, {Tous les Jours} +IMG_1545.JPG, {VALENTINO}, {발렌티노}, {Valentino} +IMG_1550.JPG, {CHANEL}, {샤넬}, {Chanel} +IMG_1552.JPG, {ROLEX}, {롤렉스}, {Rolex} +IMG_1553.JPG, {Dior}, {디올}, {Dior} +IMG_1555.JPG, {Loro Piana}, {로로피아나}, {Loro Piana} +IMG_1561.JPG, {JAEGER-LECOULTRE}, {예거르쿨트르}, {Jaeger-LeCoultre} +IMG_1566.JPG, {BREGUET}, {브레게}, {Breguet} +IMG_1570.JPG, {FOPE}, {포페}, {Fope} +IMG_1574.JPG, {TASAKI}, {타사키}, {Tasaki} +IMG_1575.JPG, {BLANCPAIN}, {블랑팡}, {Blancpain} +IMG_1577.JPG, {OMEGA}, {오메가}, {Omega} +IMG_1578.JPG, {IWC SCHAFFHAUSEN}, {IWC 샤프하우젠}, {IWC Schaffhausen} +IMG_1579.JPG, {PIAGET}, {피아제}, {Piaget} +IMG_1581.JPG, {LOEWE}, {로에베}, {Loewe} +IMG_1589.JPG, {TOD'S}, {토즈}, {Tod's} +IMG_1592.JPG, {BALMAIN}, {발망}, {Balmain} +IMG_1594.JPG, {EMPORIO ARMANI}, {엠포리오아르마니}, {Emporio Armani} +IMG_1599.JPG, {Absolut}, {앱솔루트캐시미어}, {Absolut Cashmere} +IMG_1600.JPG, {CAROLINA HERRERA}, {CH 캐롤리나 헤레라}, {CH Carolina Herrera} +IMG_1604.JPG, {Acne Studios}, {아크네스튜디오}, {Acne Studios} +IMG_1608.JPG, {Ten c}, {텐씨}, {Ten C} +IMG_1609.JPG, {JIMMY CHOO}, {지미추}, {Jimmy Choo} +IMG_1611.JPG, {KENZO}, {겐조}, {Kenzo} +IMG_1615.JPG, {ETRO}, {에트로}, {Etro} +IMG_1617.JPG, {COLOMBO}, {콜롬보}, {Colombo} +IMG_1619.JPG, {FENDI}, {펜디}, {Fendi} +IMG_1621.JPG, {CANADA GOOSE}, {캐나다구스}, {Canada Goose} +IMG_1622.JPG, {Paul Smith}, {폴스미스}, {Paul Smith} +IMG_1624.JPG, {DOLCE & GABBANA}, {돌체앤가바나}, {Dolce & Gabbana} +IMG_1626.JPG, {ZEGNA}, {제냐}, {Zegna} +IMG_1627.JPG, {S.T. Dupont}, {S.T듀퐁}, {S.T. Dupont} +IMG_1628.JPG, {Brioni}, {브리오니}, {Brioni} +IMG_1630.JPG, {STONE ISLAND}, {스톤아일랜드}, {Stone Island} +IMG_1635.JPG, {BOSS}, {휴고보스}, {Hugo Boss} +IMG_1637.JPG, {WOOYOUNGMI}, {우영미}, {Wooyoungmi} +IMG_1639.JPG, {TOM FORD}, {톰 포드}, {Tom Ford} +IMG_1642.JPG, {BERLUTI}, {벨루티}, {Berluti} +IMG_1645.JPG, {rag & bone}, {랙앤본}, {rag & bone} +IMG_1647.JPG, {WEEKEND MaxMara}, {위크엔드막스마라}, {Weekend Max Mara} +IMG_1648.JPG, {LE MONT ST MICHEL}, {르몽생미셸}, {Le Mont St Michel} +IMG_1649.JPG, {ALLSAINTS}, {올세인츠}, {AllSaints} +IMG_1650.JPG, {maje}, {마쥬}, {Maje} +IMG_1651.JPG, {SANDRO}, {산드로}, {Sandro} +IMG_1652.JPG, {Theory}, {띠어리}, {Theory} +IMG_1655.JPG, {SAN FRANCISCO MARKET}, {샌프란시스코마켓}, {San Francisco Market} +IMG_1659.JPG, {FUJIFILM}, {후지필름}, {Fujifilm} +IMG_1660.JPG, {CLUB MONACO}, {클럽모나코}, {Club Monaco} +IMG_1662.JPG, {A.P.C.}, {아페쎄}, {A.P.C.} +IMG_1664.JPG, {GANNI}, {가니}, {Ganni} +IMG_1665.JPG, {L'AGENCE}, {라장스}, {L'Agence} +IMG_1667.JPG, {THE PAIRING}, {더 페어링}, {The Pairing} +IMG_1670.JPG, {js garden}, {js가든}, {JS Garden} +IMG_1672.JPG, {TWG}, {TWG Tea}, {티더블유지 티} +IMG_1673.JPG, {MONCLER}, {몽클레르}, {Moncler} +IMG_1674.JPG, {FRED}, {프레드}, {Fred} +IMG_1675.JPG, {PRADA}, {프라다}, {Prada} +IMG_1686.JPG, {POLA}, {POLA}, {폴라} +IMG_1688.JPG, {LONGINES}, {론진}, {Longines} +IMG_1689.JPG, {alo}, {알로}, {Alo} +IMG_1691.JPG, {J.ESTINA, SWAROVSKI}, {제이에스티나, 스와로브스키}, {J.ESTINA, Swarovski} +IMG_1694.JPG, {TORY BURCH}, {토리버치}, {Tory Burch} +IMG_1698.JPG, {BAGGU}, {바쿠}, {Baggu} +IMG_1700.JPG, {paprika}, {파프리카}, {Paprika} +IMG_1702.JPG, {GUESS}, {게스}, {Guess} +IMG_1706.JPG, {marie claire}, {마리끌레르}, {Marie Claire} +IMG_1707.JPG, {Matin Kim}, {마뗑킴}, {Matin Kim} +IMG_1711.JPG, {TÉTO}, {테토}, {Teto} +IMG_1712.JPG, {MUSINSA}, {무신사}, {Musinsa} +IMG_1713.JPG, {UGG}, {어그}, {UGG} +IMG_1715.JPG, {fennec}, {페넥}, {Fennec} +IMG_1717.JPG, {MONDAY EDITION}, {먼데이에디션}, {Monday Edition} +IMG_1721.JPG, {RAWROW}, {로우로우}, {Rawrow} +IMG_1723.JPG, {MUJI}, {MUJI}, {무지} +IMG_1724.JPG, {BOTTLE BUNKER}, {보틀벙커}, {Bottle Bunker} +IMG_1726.JPG, {LOTTE HIMART}, {롯데하이마트}, {Lotte Hi-Mart} +IMG_1727.JPG, {LOTTE WORLD ADVENTURE}, {롯데월드 어드벤처}, {Lotte World Adventure} +IMG_1733.JPG, {Salad Box}, {샐러드박스}, {Salad Box} +IMG_1737.JPG, {STARBUCKS}, {스타벅스}, {Starbucks} +IMG_1744.JPG, {A TWOSOME PLACE}, {투썸플레이스}, {A Twosome Place} +IMG_1745.JPG, {banapresso}, {바나프레소}, {Banapresso} +IMG_1746.JPG, {With Me 플러스}, {위드미플러스}, {With Me Plus} +IMG_1748.JPG, {ESLOW COFFEE}, {에슬로우}, {Eslow Coffee} +IMG_1750.JPG, {SHABURO 21}, {샤브로21}, {Shaburo 21} +IMG_1755.JPG, {BAS BURGER}, {바스버거}, {Bas Burger} +IMG_1757.JPG, {DUTCH}, {더치앤빈}, {Dutch & Bean} +IMG_1778.JPG, {7-ELEVEN}, {세븐일레븐}, {7-Eleven} +IMG_1781.JPG, {MEKONG THAI}, {메콩타이}, {Mekong Thai} +IMG_1783.JPG, {목삼연구소}, {목삼연구소}, {Moksam Lab} +IMG_1824.JPG, {우리은행}, {우리은행}, {Woori Bank} +IMG_1829.JPG, {KB국민은행}, {KB국민은행}, {KB Kookmin Bank} +IMG_1859.JPG, {STAGE9}, {스테이지나인}, {Stage Nine} +IMG_1877.JPG, {Brian's Coffee}, {브라이언스커피}, {Brian's Coffee} +IMG_1880.JPG, {The Coffee Bean}, {커피빈}, 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