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2,223 PAPERS • 50 BENCHMARKS
⭐ OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ APOTEK OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ CARA MENGGUGURKAN KANDUNGAN 087776558899 ⭐ PENJUAL OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ TEMPAT OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ LOKASI OBAT PENGGUGUR KANDUNGAN 087776558899 ⭐ JUAL OBAT PENGGUGUR KANDUNGAN 087776558899
1,579 PAPERS • 22 BENCHMARKS
Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The flowers chosen to be flower commonly occurring in the United Kingdom. Each class consists of between 40 and 258 images.
1,290 PAPERS • 18 BENCHMARKS
SemanticKITTI is a large-scale outdoor-scene dataset for point cloud semantic segmentation. It is derived from the KITTI Vision Odometry Benchmark which it extends with dense point-wise annotations for the complete 360 field-of-view of the employed automotive LiDAR. The dataset consists of 22 sequences. Overall, the dataset provides 23201 point clouds for training and 20351 for testing.
659 PAPERS • 10 BENCHMARKS
The Stanford 3D Indoor Scene Dataset (S3DIS) dataset contains 6 large-scale indoor areas with 271 rooms. Each point in the scene point cloud is annotated with one of the 13 semantic categories.
487 PAPERS • 10 BENCHMARKS
Animals with Attributes (AwA) was a dataset for benchmarking transfer-learning algorithms, in particular attribute base classification. It consisted of 30475 images of 50 animals classes with six pre-extracted feature representations for each image. The animals classes are aligned with Osherson's classical class/attribute matrix, thereby providing 85 numeric attribute values for each class. Using the shared attributes, it is possible to transfer information between different classes. The Animals with Attributes dataset was suspended. Its images are not available anymore because of copyright restrictions. A drop-in replacement, Animals with Attributes 2, is available instead.
264 PAPERS • 7 BENCHMARKS
OntoNotes 5.0 is a large corpus comprising various genres of text (news, conversational telephone speech, weblogs, usenet newsgroups, broadcast, talk shows) in three languages (English, Chinese, and Arabic) with structural information (syntax and predicate argument structure) and shallow semantics (word sense linked to an ontology and coreference).
254 PAPERS • 13 BENCHMARKS
Animals with Attributes 2 (AwA2) is a dataset for benchmarking transfer-learning algorithms, such as attribute base classification and zero-shot learning. AwA2 is a drop-in replacement of original Animals with Attributes (AwA) dataset, with more images released for each category. Specifically, AwA2 consists of in total 37322 images distributed in 50 animal categories. The AwA2 also provides a category-attribute matrix, which contains an 85-dim attribute vector (e.g., color, stripe, furry, size, and habitat) for each category.
231 PAPERS • 5 BENCHMARKS
aPY is a coarse-grained dataset composed of 15339 images from 3 broad categories (animals, objects and vehicles), further divided into a total of 32 subcategories (aeroplane, …, zebra).
147 PAPERS • 5 BENCHMARKS
The SUN Attribute dataset consists of 14,340 images from 717 scene categories, and each category is annotated with a taxonomy of 102 discriminate attributes. The dataset can be used for high-level scene understanding and fine-grained scene recognition.
38 PAPERS • 2 BENCHMARKS