--- dataset_info: features: - name: image dtype: image - name: label dtype: class_label: names: '0': 1043-n000001-Shiba_Dog '1': 1121-n000002-French_bulldog '2': 1160-n000003-Siberian_husky '3': 1324-n000004-malamute '4': 1936-n000005-Pomeranian '5': 200-n000008-Airedale '6': 200-n000010-miniature_poodle '7': 200-n000012-affenpinscher '8': 201-n000024-schipperke '9': 202-n000020-Australian_terrier '10': 202-n000023-Welsh_springer_spaniel '11': 202-n000028-curly_coated_retriever '12': 203-n000015-Staffordshire_bullterrier '13': 203-n000016-Norwich_terrier '14': 203-n000021-Tibetan_terrier '15': 203-n000022-English_setter '16': 203-n000027-Norfolk_terrier '17': 205-n000029-Pembroke '18': 205-n000030-Tibetan_mastiff '19': 206-n000007-Border_terrier '20': 206-n000035-Great_Dane '21': 206-n000037-Scotch_terrier '22': 206-n000047-flat_coated_retriever '23': 206-n000051-Saluki '24': 207-n000011-Irish_setter '25': 207-n000026-Blenheim_spaniel '26': 207-n000036-Irish_terrier '27': 207-n000044-bloodhound '28': 207-n000045-redbone '29': 209-n000040-West_Highland_white_terrier '30': 209-n000042-Brabancon_griffo '31': 209-n000043-dhole '32': 209-n000049-kelpie '33': 209-n000054-Doberman '34': 210-n000006-Ibizan_hound '35': 210-n000048-vizsla '36': 211-n000009-cairn '37': 211-n000018-German_shepherd '38': 211-n000025-African_hunting_dog '39': 211-n000052-Dandie_Dinmont '40': 211-n000058-Sealyham_terrier '41': 211-n000059-German_short_haired_pointer '42': 211-n000061-Bernese_mountain_dog '43': 211-n000068-Saint_Bernard '44': 214-n000019-Leonberg '45': 214-n000033-Bedlington_terrier '46': 215-n000031-Newfoundland '47': 215-n000038-Lhasa '48': 215-n000075-Chesapeake_Bay_retriever '49': 216-n000017-Lakeland_terrier '50': 216-n000063-Walker_hound '51': 216-n000078-American_Staffordshire_terrier '52': 217-n000014-otterhound '53': 217-n000034-Sussex_spaniel '54': 217-n000046-Norwegian_elkhound '55': 217-n000050-bluetick '56': 217-n000079-dingo '57': 219-n000066-Irish_water_spaniel '58': 2192-n000088-Samoyed '59': 220-n000032-Fila_Braziliero '60': 220-n000053-standard_schnauzer '61': 220-n000069-Mexican_hairless '62': 221-n000055-EntleBucher '63': 222-n000013-Afghan_hound '64': 223-n000067-kuvasz '65': 223-n000074-English_foxhound '66': 223-n000092-keeshond '67': 224-n000039-Irish_wolfhound '68': 224-n000056-Scottish_deerhound '69': 224-n000060-Rottweiler '70': 225-n000062-black_and_tan_coonhound '71': 225-n000073-Great_Pyrenees '72': 225-n000082-boxer '73': 226-n000057-wire_haired_fox_terrier '74': 226-n000064-borzoi '75': 227-n000070-groenendael '76': 227-n000094-collie '77': 228-n000085-Gordon_setter '78': 229-n000087-Kerry_blue_terrier '79': 230-n000041-briard '80': 230-n000080-Rhodesian_ridgeback '81': 230-n000084-Boston_bull '82': 231-n000077-bull_mastiff '83': 231-n000081-silky_terrier '84': 232-n000076-Brittany_spaniel '85': 232-n000083-Eskimo_dog '86': 232-n000089-giant_schnauzer '87': 233-n000071-malinois '88': 233-n000072-Bouvier_des_Flandres '89': 234-n000065-whippet '90': 234-n000091-Appenzeller '91': 234-n000093-Chinese_Crested_Dog '92': 2342-n000102-miniature_schnauzer '93': 235-n000090-soft_coated_wheaten_terrier '94': 235-n000096-Weimaraner '95': 235-n000097-clumber '96': 237-n000086-Greater_Swiss_Mountain_dog '97': 237-n000095-toy_terrier '98': 238-n000099-Italian_greyhound '99': 241-n000100-basset '100': 243-n000103-basenji '101': 245-n000098-Australian_Shepherd '102': 249-n000101-Maltese_dog '103': 249-n000106-Japanese_spaniel '104': 253-n000105-Cane_Carso '105': 253-n000107-Japanese_Spitzes '106': 257-n000108-Old_English_sheepdog '107': 258-n000104-Black_sable '108': 2594-n000109-Border_collie '109': 274-n000110-Shetland_sheepdog '110': 276-n000112-English_springer '111': 276-n000113-beagle '112': 286-n000111-cocker_spaniel '113': 2909-n000116-Cardigan '114': 2925-n000114-toy_poodle '115': 3083-n000117-Bichon_Frise '116': 316-n000118-standard_poodle '117': 318-n000115-komondor '118': 329-n000119-chow '119': 3336-n000121-chinese_rural_dog '120': 340-n000120-Yorkshire_terrier '121': 3580-n000122-Labrador_retriever '122': 361-n000123-Shih_Tzu '123': 420-n000124-Chihuahua '124': 480-n000125-Pekinese '125': 5355-n000126-golden_retriever '126': 561-n000127-miniature_pinscher '127': 7449-n000128-teddy '128': 798-n000130-pug '129': 806-n000129-papillon splits: - name: train num_bytes: 2735788925.032 num_examples: 65228 - name: validation num_bytes: 171938704.4 num_examples: 5200 download_size: 2662313600 dataset_size: 2907727629.432 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* license: cc-by-4.0 task_categories: - image-classification language: - en tags: - dogs pretty_name: Tsinghua Dogs Dataset size_categories: - 10K Tsinghua Dogs Dataset from https://cg.cs.tsinghua.edu.cn/ThuDogs/ ## Dataset Details ### Dataset Description Images of dogs divided in classes. The original link above https://cg.cs.tsinghua.edu.cn/ThuDogs/ has also bounding boxes and other annotations. - **Curated by:** https://cg.cs.tsinghua.edu.cn/ThuDogs/ - **Funded by [optional]:** https://cg.cs.tsinghua.edu.cn/ThuDogs/ - **License:** CC-4.0 Attribution ### Dataset Sources [optional] https://cg.cs.tsinghua.edu.cn/ThuDogs/ - **Repository:** https://cg.cs.tsinghua.edu.cn/ThuDogs/ - **Paper [optional]:** https://doi.org/10.1007/s41095-020-0184-6 ## Dataset Structure Image (PIL) and label (string) ## Dataset Creation ### Curation Rationale Made this dataset available in HF format ### Source Data https://cg.cs.tsinghua.edu.cn/ThuDogs/ ## Citation [optional] "A new dataset of dog breed images and a benchmark for fine-grained classification", Computational Visual Media, 2020 **BibTeX:** ``` @article{Zou2020ThuDogs, title={A new dataset of dog breed images and a benchmark for fine-grained classification}, author={Zou, Ding-Nan and Zhang, Song-Hai and Mu, Tai-Jiang and Zhang, Min}, journal={Computational Visual Media}, year={2020}, url={https://doi.org/10.1007/s41095-020-0184-6} } ```