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Shop our selection of dolls, books, and more that help build girls of strong character. It contains 491K diverse images of 13 popular clothing categories from both commercial shopping stores and consumers.
At each row, we partition the images into two groups, the left three columns represent clothes from commercial stores, while the right three columns are from customers. In each group, the three images indicate three levels of difficulty with respect to the corresponding variation. Furthermore, at each row, the items in these two groups of images are from the same clothing identity but from two different domains, that is, commercial and customer. The items of the same identity may have different styles such as color and printing. Each item is annotated with landmarks and masks. Please refer to Data Description below for detailed information about dataset.
Style : a number to distinguish between clothing items from images with the same pair id. Bounding_box : where x1 and y_1 represent the upper left point coordinate of bounding box, x_2 and y_2 represent the lower right point coordinate of bounding box.
The orders of landmark annotations are listed in figure 2. Segmentation : , ], where represents a polygon and a single clothing item may contain more than one polygon. 'Style' of other clothing items whose bonding boxes are not drawn in the figure is 0, and they can not construct positive commercial-consumer pairs.
One positive commercial-consumer pair is the annotated short sleeve top in the first image and the annotated short sleeve top in the last image. Our dataset makes it possible to construct instance-level pairs in a flexible way.
We provide code to generate coco-type annotations from our dataset in deepfashion2_to_coco.py. Please note that during evaluation, image_id is the digit number of the image name.
In this way, you can generate ground truth Jason files for evaluation for clothes detection task and clothes segmentation task, which are not listed in DeepFashion2 Challenge. You can get validation score locally using Evaluation Code and above Jason files.
You can also submit your results to evaluation server in our DeepFashion2 Challenge. You need to submit your results to evaluation server in our DeepFashion2 Challenge.
(For statistics of released images and annotations, please refer to DeepFashion2 Challenge). Train ValidationTestOverallimages390,88433,66967,342491,895bboxes636,62454,910109,198800,732landmarks636,62454,910109,198800,732masks636,62454,910109,198800,732pairs685,584query: 12,550gallery: 37183query: 24,402gallery: 75,347873,234Figure 3 shows the statistics of different variations and the numbers of items of the 13 categories in DeepFashion2.
This task detects clothes in an image by predicting bounding boxes and category labels to each detected clothing item. The evaluation metrics are the bounding box's average precision ,.
Table 2: Clothes detection trained with released DeepFashion2 Dataset evaluated on validation set. APAP50AP750.6380.7890.745Table 3: Clothes detection on different validation subsets, including scale, occlusion, zoom-in, and viewpoint.
ScaleOcclusionZoom_inViewpointOverallsmallmoderatelargeslightmediumheavynomediumlargeno wearfrontalside or backAP0.6040.7000.6600.7120.6540.3720.6950.6290.4660.6240.6810.6410.667AP500.7800.8510.7680.8440.8100.5310.8480.7550.5630.7130.8320.7960.814AP750.7170.8090.7440.8120.7680.4330.8060.7180.5250.6880.7910.7440.773 This task aims to predict landmarks for each detected clothing item in an image. Similarly, we employ the evaluation metrics used by COCO for human pose estimation by calculating the average precision for waypoints , where OKs indicates the object landmark similarity. Table 4: Landmark estimation trained with released DeepFashion2 Dataset evaluated on validation set.
ScaleOcclusionZoom_inViewpointOverallsmallmoderatelargeslightmediumheavynomediumlargeno wearfrontalside or backAP0.5870.4970.6870.6070.5990.5550.6690.6430.6310.5300.3980.2480.6880.6160.5590.4890.3750.3190.5270.5100.6770.5960.5360.4560.6410.563AP500.7800.7640.8540.8390.7820.7740.8510.8470.8130.7990.5340.4790.8550.8480.7570.7440.5710.5490.7240.7160.8460.8320.7480.7270.8200.805AP750.6710.5510.7790.7030.6780.6250.7600.7390.7180.6000.4400.2360.7860.7140.6330.5370.3900.3070.5710.5500.7710.6840.6100.5060.7280.641 Figure 4 shows the results of landmark and pose estimation. Figure 4: Results of landmark and pose estimation.
This task assigns a category label (including background label) to each pixel in an item. The evaluation metrics is the average precision including , computed over masks. Table 6: Clothes segmentation trained with released DeepFashion2 Dataset evaluated on validation set.
APAP50AP750.6400.7970.754Table 7: Clothes Segmentation on different validation subsets, including scale, occlusion, zoom-in, and viewpoint. ScaleOcclusionZoom_inViewpointOverallsmallmoderatelargeslightmediumheavynomediumlargeno wearfrontalside or backAP0.6340.7030.6660.7200.6560.3810.7010.6370.4780.6640.6890.6350.674AP500.8110.8650.7980.8630.8240.5430.8610.7910.5910.7570.8490.8110.834AP750.7520.8260.7730.8360.7800.4440.8230.7510.5590.7370.8100.7550.793 Figure 5 shows the results of clothes segmentation.
In this task, top-k retrieval accuracy is employed as the evaluation metric. We emphasize the retrieval performance while still consider the influence of detector.
Table 8: Consumer-to-Shop Clothes Retrieval trained with released DeepFashion2 Dataset using detected box evaluated on validation set. ScaleOcclusionZoom_inViewpointOverallsmallmoderatelargeslightmediumheavynomediumlargeno wearfrontalside or backtop-1top-10top-20class0.5200.4850.6300.5370.5400.5020.5720.5270.5630.5080.5580.3830.6180.5530.5470.4960.4440.4050.5460.4990.5840.5230.5330.4870.1020.0910.3610.3120.4700.415pose0.7210.6370.7780.7020.7350.6910.7560.7100.7370.6700.7280.5800.7750.7100.7510.7010.6210.5600.7310.6900.7630.7000.7110.6450.2640.2430.5620.4970.6540.588mask0.6240.5520.7140.6570.6460.6080.6750.6390.6510.5930.6320.5550.7110.6540.6550.6130.5260.4950.6440.6150.6820.6300.6370.5650.1930.1860.4740.4220.5710.520pose+class0.7520.6910.7860.7300.7330.7050.7540.7250.7500.7060.7280.6050.7890.7460.7500.7090.6200.5820.7260.6990.7710.7230.7190.6840.2680.2440.5740.5220.6650.617mask+class0.6560.6100.7280.6660.6870.6490.7140.6760.6760.6230.6540.5490.7250.6740.7020.6550.5650.5360.6840.6480.7120.6610.6580.6040.2120.2080.4960.4510.5950.542 Figure 6 shows queries with top-5 retrieved clothing items.
Phil Spector was viewed as a man with two distinct personas: The late music producer was regarded as a rock ‘n’ roll genius who elevated the genre with his “Wall of Sound” style and creating hits for several big names from the Beatles to Tina Turner A dream of harnessing steam technology to link Africa’s vast south to north, around the awe-inspiring mountain ranges that dot the continent, through the apparently arid desert landscapes, over untamed Savannah grasslands teeming with wildlife.
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In the easy-living heyday of the 1920s, boasting everything from card tables to ceiling fans, to hot and cold water on tap. Withdrawn from service during the dark days of World War II, extensively refurbished and modernized in the Seventies and Nineties.
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