模型适配进度（拓展） #163

kokuro-asahi · 2023-11-06T09:05:43Z

总计：8+14+9+26+21+10+3+6+6+10+3+9+11+4+88=228

领域	功能	基础模型	支持方式	负责人	状态	展开数量	Onelab负责人	OneLab公开项目链接
cv	classification	EfficientNet_b0	flowvision	ke	完成	8	li	https://www.oneflow.cloud/drill/#/project/public/code?id=fa5438d8c14b8fa64429da52ea3aaa4b
cv	classification	EfficientNet_b1	flowvision	cui	完成	8	li	https://www.oneflow.cloud/drill/#/project/public/code?id=0919cef11021fa3729d9678b27bb5434
cv	classification	EfficientNet_b2	flowvision	cui	完成	8	li	https://www.oneflow.cloud/drill/#/project/public/code?id=fa8c4ace23affba27863ea9f56bbb662
cv	classification	EfficientNet_b3	flowvision	cui	完成	8	li	https://www.oneflow.cloud/drill/#/project/public/code?id=9486b2d7f8523973daf7b90d7fb9fc17
cv	classification	EfficientNet_b4	flowvision	cui	完成	8	li	https://www.oneflow.cloud/drill/#/project/public/code?id=d33198119b667ea422696576e5c67a4e
cv	classification	EfficientNet_b5	flowvision	cui	完成	8	li	https://www.oneflow.cloud/drill/#/project/public/code?id=d4c9a2d6db1a934b3896b56b92a03e74
cv	classification	EfficientNet_b6	flowvision	cui	完成	8	li	https://www.oneflow.cloud/drill/#/project/public/code?id=f9d51026c6d13eaca244a4d4c3eca1c3
cv	classification	EfficientNet_b7	flowvision	cui	完成	8	li	https://www.oneflow.cloud/drill/#/project/public/code?id=9b2d0d5ad34bf2eceff55f77007a260d
cv	classification	regnet_y_400mf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=74db0fcbf0b7b42d74a5c37d1dcd8c8d
cv	classification	regnet_y_800mf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=7af27966f711f8d0cf07b51d40a57992
cv	classification	regnet_y_1_6gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=579f7eb5c7d26076962e0721d1d709f9
cv	classification	regnet_y_3_2gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=5affa8b3893616466b0242397924a247
cv	classification	regnet_y_8gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=a38f54a87436578dafa7ac15927a9af9
cv	classification	regnet_y_16gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=35607cd00b6c22f5fd02254818b400ae
cv	classification	regnet_y_32gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=dfb022ad6b5b7e3b431c84e548dd53fa
cv	classification	regnet_x_400mf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ed0d790aa2d1e911a72daa7e6605a97c
cv	classification	regnet_x_800mf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=d0097d4399eac0f6c31fe3dc29dacb17
cv	classification	regnet_x_1_6gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=9b2b5d00b3ff83198dfdc48a9085deb0
cv	classification	regnet_x_3_2gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=240b02c57ab1c27e98024acaf3aef70a
cv	classification	regnet_x_8gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=4e76ee1a1129ab9c2c5f903e6ffb7d39
cv	classification	regnet_x_16gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=36bca178cb147c64c3251ef38082c593
cv	classification	regnet_x_32gf	flowvision	cui	完成	14	li	https://www.oneflow.cloud/drill/#/project/public/code?id=4b9bb2aa6ad9857fb31c96299fce9051
cv	classification	rexnetv1_1_0	flowvision	cui	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=f8e263b706369a6846c13985edad8433
cv	classification	rexnetv1_1_3	flowvision	cui	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=5acd5c7d3dcffd712779101d3af9e0ba
cv	classification	rexnetv1_1_5	flowvision	cui	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=edc766c2407bb08ca908516322282a19
cv	classification	rexnetv1_2_0	flowvision	cui	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=7e28ee4f9157562fa795899038da2a6e
cv	classification	rexnetv1_3_0	flowvision	cui	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=44f0cd0f722917ff90e921412d2fd775
cv	classification	rexnet_lite_1_0	flowvision	cui	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=8c0935ce218c7fe14721a54de380cf95
cv	classification	rexnet_lite_1_3	flowvision	cui	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=44a6598a4f7ce345a5d7c3ba458eb7e4
cv	classification	rexnet_lite_1_5	flowvision	cui	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=bc98489e6cc245e8348dd3289d237f97
cv	classification	rexnet_lite_2_0	flowvision	cui	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=24f5e35e6056919730c4cabd354144c3
cv	classification	vit_tiny_patch16_224	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=25334d0e04ca945c25b331b67d2eb550
cv	classification	vit_tiny_patch16_384	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=e1302a0101857135ab62132d1e35aeae
cv	classification	vit_small_patch32_224	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=2648e2213ad95a43b35631ca58e0be84
cv	classification	vit_small_patch32_384	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=12768b29f94c5be820d11e9d5a3ae81f
cv	classification	vit_small_patch16_224	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=281132f74d7608078277ee4b46ef1701
cv	classification	vit_small_patch16_384	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=3f579f7f1350223ea18797a1a204c1e8
cv	classification	vit_base_patch32_224	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=18e3f3be24228e68ec8abaf3c51d1a08
cv	classification	vit_base_patch32_384	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=c76367e43afccb54509ef57a47641a2b
cv	classification	vit_base_patch16_384	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=5ef00487de1c66d4b59056aabe95e92a
cv	classification	vit_base_patch8_224	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=9b637c392fe656a10c5401cb205e5a1e
cv	classification	vit_base_patch16_224	flowvision		完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ae6f744c9a856d2f04784742f9161143
cv	classification	vit_large_patch32_384	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=96c37fd309acf0c9a007753e0782a181
cv	classification	vit_large_patch16_224	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=73350d451067d9a7164cf5f60f2915cd
cv	classification	vit_large_patch16_384	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=20efdf3e5ce3526d180e9d68f03c533a
cv	classification	vit_base_patch16_224_sam	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=be4351753e1262778745eb1098c49c57
cv	classification	vit_base_patch32_224_sam	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=c73d578cc1f4059e05e7367b69c69bf7
cv	classification	vit_tiny_patch16_224_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_small_patch32_224_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_small_patch16_224_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_base_patch32_224_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_base_patch16_224_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_base_patch8_224_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_large_patch32_224_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_large_patch16_224_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_huge_patch14_224_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_base_patch16_224_miil_in21k	flowvision	zhang	没有ImageNet 21k数据集	26
cv	classification	vit_base_patch16_224_miil	flowvision	zhang	完成	26	li	https://www.oneflow.cloud/drill/#/project/public/code?id=21423b30bd3ea7e717c486c5c96fabe8
cv	classification	deit_tiny_patch16_224	flowvision	zhang	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=13cfd9108b2f72cb92a99a846e98e6ff
cv	classification	deit_base_patch16_224	flowvision	zhang	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=af5947962e7720e97a570703a9e53694
cv	classification	deit_base_patch16_384	flowvision	zhang	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=606e242389cccb930249be457130dbf3
cv	classification	deit_tiny_distilled_patch16_224	flowvision	zhang	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=5891ddd5f03d6af0b2aeb690ced37b5d
cv	classification	deit_small_distilled_patch16_224	flowvision	zhang	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=849921cbc3a4c0b31f22203c0b60c690
cv	classification	deit_base_distilled_patch16_224	flowvision	zhang	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=9a58d5387296f5673265405938ec8d65
cv	classification	deit_base_distilled_patch16_384	flowvision	zhang	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=b1ebb442d627b9350681e2c8c97c74c2
cv	classification	deit_base_patch16_LS_224	flowvision	zhang	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=d17491afb893e3d0ab7915f53fcb674f
cv	classification	deit_base_patch16_LS_224_in21k	flowvision	zhang	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=425be3891ca118082c05635fd2305914
cv	classification	deit_base_patch16_LS_384	flowvision	cui	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ff8e43ef6752f38d46c6575296d244a6
cv	classification	deit_base_patch16_LS_384_in21k	flowvision	cui	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=6f1deac6410a3db19d7d22befba4fc0b
cv	classification	deit_huge_patch14_LS_224	flowvision	cui	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=8a5517f5f84c5f6a2497c7307c1e6899
cv	classification	deit_huge_patch14_LS_224_in21k	flowvision	cui	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=8a5517f5f84c5f6a2497c7307c1e6899
cv	classification	deit_large_patch16_LS_224_in21k	flowvision	cui	进行中	22	li
cv	classification	deit_large_patch16_LS_384_in21k	flowvision	cui	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=274caa0440f9df12e989678ed205c0d4
cv	classification	deit_small_patch16_LS_224_in21k	flowvision	cui	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=30fbf529f1d61ac77b72514f8e913cc8
cv	classification	deit_small_patch16_224	flowvision	li	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ffabc32455e8966ad33bb07974f194bf
cv	classification	deit_small_patch16_LS_384	flowvision	cui	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=e2e3159b7bcbd13a91e7736ee6ea3960
cv	classification	deit_small_patch16_LS_384_in21k	flowvision	cui	进行中	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=9be2c26a99c860a3f98c3f0a5b854e61
cv	classification	deit_large_patch16_LS_224	flowvision	li	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=37ff4dabe9c2b9132e76badcabce7506
cv	classification	deit_small_patch16_LS_224	flowvision	li	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=21f5789060c865a30c1eec1aade0c2c1
cv	classification	deit_large_patch16_LS_384	flowvision	li	完成	22	li	https://www.oneflow.cloud/drill/#/project/public/code?id=457dd8d362220fe32d340bfee7b7f306
cv	classification	mlp_mixer_s16_224	flowvision	ke	无模型	10
cv	classification	mlp_mixer_s32_224	flowvision	ke	无模型	10
cv	classification	mlp_mixer_b16_224	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=6680d967c8d66d5ad6716cb3e1d4e63a
cv	classification	mlp_mixer_b32_224	flowvision	ke	无模型	10
cv	classification	mlp_mixer_b16_224_in21k	flowvision	ke	infer低	10
cv	classification	mlp_mixer_l16_224	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=9d39de61bbaa96f995494981606131bf
cv	classification	mlp_mixer_l32_224	flowvision	ke	无模型	10
cv	classification	mlp_mixer_l16_224_in21k	flowvision	ke	infer低	10
cv	classification	mlp_mixer_b16_224_miil	flowvision	ke	infer低	10
cv	classification	mlp_mixer_b16_224_miil_in21k	flowvision	ke	infer低	10
cv	classification	convmixer_768_32_relu	flowvision	ke	完成	3	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ea73a6af66d18116bd17159bd43dfd09
cv	classification	convmixer_1024_20	flowvision	ke	完成	3	li	https://www.oneflow.cloud/drill/#/project/public/code?id=d4cae7556245781c1da91665be4a4a2a
cv	classification	convmixer_1536_20	flowvision	ke	完成	3	li	https://www.oneflow.cloud/drill/#/project/public/code?id=9ab2b324f3643859826de05b0e0b0b09
cv	classification	res2net101_26w_4s	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=cd1e229dae70bfe32e33d57798e5b62e
cv	classification	res2net50_14w_8s	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ab6afe87aa33c278311d63b649f6b117
cv	classification	res2net50_26w_4s	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=77a0ad1c8078b75cba5d823bca361c04
cv	classification	res2net50_26w_6s	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=fa0a61d5d72247d22863c9392b48f89c
cv	classification	res2net50_26w_8s	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=156564fb425715a833a255b3736f9e9d
cv	classification	res2net50_48w_2s	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=1ddc5d5f68e2d3a75d9a9c8d9a59c97a
cv	classification	cait_M48	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=405fc951ffa80c9dcc3a605d41587de4
cv	classification	cait_M36	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=c88925b7c61385982b8b25658467ed7d
cv	classification	cait_S36	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=a9165b582fca4851946455e1158fa5d5
cv	classification	cait_S24	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ef302a9ec1241961ad2d9ff21dd28a1f
cv	classification	cait_S24_224	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ef9a11c3daa419e068f913d6c1da49be
cv	classification	cait_XS24	flowvision	ke	完成	6	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ec1e9e9df444a8012a699020d7876c3f
cv	classification	dla34	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=92865fd08123c84dd002ad934dab9f30
cv	classification	dla46_c	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=4a23dbc75bd431049450b709f1e2dc82
cv	classification	dla46x_c	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=48e8f80f732ce1ff49401c239079799b
cv	classification	dla60x_c	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=72a36495db48f1f3f4830108eed027df
cv	classification	dla60	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=0adcb76c0580f05ec53d87b3ed2689bb
cv	classification	dla60x	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=a44cc9aa071ec6171c0aae3ccc7eea69
cv	classification	dla102	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=df2140e6b6ff3c0bebc2a05782bf26ff
cv	classification	dla102x	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=c134390359f1d99647ffc6e814b4b59a
cv	classification	dla102x2	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=a1a5a8c0d0038728372b98116df242db
cv	classification	dla169	flowvision	ke	完成	10	li	https://www.oneflow.cloud/drill/#/project/public/code?id=b02b358f69982312ce01028c25325a3a
cv	classification	genet_small	flowvision	ke	完成	3	li	https://www.oneflow.cloud/drill/#/project/public/code?id=32e02310d717d8fa2c6841cf44b1b398
cv	classification	genet_normal	flowvision	ke	完成	3	li	https://www.oneflow.cloud/drill/#/project/public/code?id=d0ff57b9dd41286da62eeff48365f393
cv	classification	genet_large	flowvision	ke	完成	3	li	https://www.oneflow.cloud/drill/#/project/public/code?id=e395406e16ce22f84d5c6c75945da24c
cv	classification	hrnet_w18_small	flowvision	ke	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=1fd560ba809b25931f26285ee87fbd01
cv	classification	hrnet_w18_small_v2	flowvision	ke	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=ed3a0a67216e00c5e16ca68297e2e51c
cv	classification	hrnet_w18	flowvision	ke	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=477afc03da45d95475c585087263aa16
cv	classification	hrnet_w30	flowvision	ke	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=b7830e267e11d1c24dc2e1f809815b58
cv	classification	hrnet_w32	flowvision	ke	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=02b78b105d109ee2d73b141a84131d5d
cv	classification	hrnet_w40	flowvision	ke	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=c4ebccc6d6c51c4726268327bf0d1277
cv	classification	hrnet_w44	flowvision	ke	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=4ad93af70e75ad40b35b3a4037b42cd2
cv	classification	hrnet_w48	flowvision	ke	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=5260c81e98fb3e315b211143e9fc3d1f
cv	classification	hrnet_w64	flowvision	ke	完成	9	li	https://www.oneflow.cloud/drill/#/project/public/code?id=f16740051ea0350f2c4a7cdbcf3f03c4
cv	classification	fan_vit_tiny	flowvision	zhang	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=5fdc757c88feab49cf3cd5321e191fcc
cv	classification	fan_vit_small	flowvision	zhang	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=f5e4fc22ddab8c1bacf86bc06b1b4f3a
cv	classification	fan_vit_large	flowvision	zhang	没有模型	12	li
cv	classification	fan_hybrid_tiny	flowvision	zhang	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=91286420dfed2958fcdaa44947f5fdd6
cv	classification	fan_hybrid_small	flowvision	zhang	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=0f6c5f287e19e86199d1c651353683bc
cv	classification	fan_hybrid_base	flowvision	zhang	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=dc39a54b6125b8a025d8ee9aa9a56d7e
cv	classification	fan_hybrid_large	flowvision	zhang	没有模型	12	li
cv	classification	fan_base_18_p16_224	flowvision	li	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=f6af8debe2b2cac9ac6a78b6640131fd
cv	classification	fan_hybrid_base_in22k_1k	flowvision	zhang	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=4e48d6211354973f5a25d684e55e204b
cv	classification	fan_hybrid_base_in22k_1k_384	flowvision	zhang	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=727170a01151d1b960dae29560aefd1b
cv	classification	fan_hybrid_large_in22k_1k	flowvision	zhang	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=8fa9548610a845ebde39fe4a9e36e680
cv	classification	fan_hybrid_large_in22k_1k_384	flowvision	zhang	完成	12	li	https://www.oneflow.cloud/drill/#/project/public/code?id=932d7f4b229527e61eeedc46d90f77c3
cv	classification	pvt_small	flowvision	ke	完成	4	li	https://www.oneflow.cloud/drill/#/project/public/code?id=6f2d36e01ff216a91e884e106cc61b01
cv	classification	pvt_tiny	flowvision	zhang	完成	4	li	https://www.oneflow.cloud/drill/#/project/public/code?id=68109071b32382961de42e6b2f6d38b1
cv	classification	pvt_medium	flowvision	zhang	完成	4	li	https://www.oneflow.cloud/drill/#/project/public/code?id=f90ccd277fd99b91728907fbf4ad6052
cv	classification	pvt_large	flowvision	zhang	完成	4	li	https://www.oneflow.cloud/drill/#/project/public/code?id=6b5533383abbf289ab905a6f902b3bbb
cv	classification	regionvit_base	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=8a772ef77e8265dad8e4ef6699b52c61
cv	classification	van_base	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=3e9a2df19f47b4325144885e3a37cbc1
cv	classification	AlexNet	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=72080813882acf841b8de45e88102f3c
cv	classification	SqueezeNet	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=0a5e15b73b1c8289ef43439e3fdac6ba
cv	classification	SqueezeNet 1.1	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=46d7e0a2ef7f55aa9cf82b37a7c530c2
cv	classification	VGG-11	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=61c0fe736758792228d4e0bfb27beb04
cv	classification	VGG-11-BN	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=381d00d5d6ce5ca40dcedd003d98b94a
cv	classification	VGG-13	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=9bc8a6c17172722a7e04cf74f8b3b4de
cv	classification	VGG-13-BN	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=77e3480052ab9bacd2b37caeca5885e8
cv	classification	VGG-16	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=6166d5d2af2b1bd29edadf88b62ef975
cv	classification	VGG-16-BN	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=89ac74c572ee6833d0a70d1650e1708a
cv	classification	VGG-19	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=12256eee1b678bbe45f8610caba00f96
cv	classification	VGG-19-BN	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=e84034d9edd66977cd70130268dda4e0
cv	classification	GoogLeNet	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=0f49a25e9fd85d21c2ae06e200452112
cv	classification	Inception_V3	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=3648dac8652969bdd49848071d0f968a
cv	classification	ResNet-18	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=52e1e7d51e9f340ee18101edac59cc6d
cv	classification	ResNet-34	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=7ba5a5946ee5bdade3a6ed0d421a28d1
cv	classification	ResNet-50	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=64669893332daab2ae874dfe839abd88
cv	classification	ResNet-101	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=d069d9ce05d84290518f1a79c8db0f15
cv	classification	ResNet-152	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=e60699fb4e75a3c9a6efe451c3c4dc4c
cv	classification	ResNeXt-50 32x4d	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=fa231a90a53651b92b9642b56c29dd19
cv	classification	ResNeXt-101 32x8d	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=0eb0c2b9f04381e99a8ed99b0ecf6e8a
cv	classification	ResNeSt-50	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=3669763ea9382906b0132dfb04c50c47
cv	classification	ResNeSt-101	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=b3b28a3065d162df62df6144b3cc8f6e
cv	classification	ResNeSt-200	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=41f5ecf0e37f60b17fa697b972ab5738
cv	classification	ResNeSt-269	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=9d3d1ab2792a86a9d3c02887a9a3b1d2
cv	classification	SE-ResNet101	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=6b50454e5ef09a31c40ada924c199cf9
cv	classification	SE-ResNet152	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=00ffee25af16d5951ec01e6fcaba5a29
cv	classification	SE-ResNet50	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=87aec92114ebc62e5b29575454a29f3f
cv	classification	SE-ResNeXt101-32x4d	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=763356489039b10c91a635f3f59e4029
cv	classification	SE-ResNeXt50-32x4d	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=21fefb3568303b5d0328e64b76bf1bfc
cv	classification	SENet-154	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=eb114bd5bc28f464ee624db70abb1a30
cv	classification	DenseNet-121	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=9383d1ee8cf6b0beb1d82e001327e7e6
cv	classification	DenseNet-161	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=748e5cf1d4d07c3439e61102f41503d1
cv	classification	DenseNet-169	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=2c31515668a6c3d01ac9dbe5d35f4051
cv	classification	DenseNet-201	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=0ed8889b7be3e26a4e0b821cc584380d
cv	classification	ShuffleNet_V2 x0.5	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=75a9ebdce52dbaea88ea921df32a36f2
cv	classification	ShuffleNet_V2 x1.0	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=90d9a3ddcb8bafabc951742ebc4defd5
cv	classification	ShuffleNet_V2 x1.5	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=569fe5043eda185002ab35a08d9707d7
cv	classification	ShuffleNet_V2 x2.0	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=50d954efb04235e6b9f993cbb9492457
cv	classification	MobileNet_V2	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=8fac67899e5bdcc0981da13c3cab1b89
cv	classification	MobileNet_V3 small	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=e5fd35642b7ac9b537110ceb70c9832a
cv	classification	MobileNet_V3 large	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=6529be091cb9c1af73fbe43f85092dee
cv	classification	MNASNet x0.5	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=9c2419d250dc5b2d61c47fdbcbc45b23
cv	classification	MNASNet x0.75	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=dea56da93c65719360b637a842096c7e
cv	classification	MNASNet x1.0	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=92dd8f01260cf7a01db8bc57f0d44cf9
cv	classification	MNASNet x1.3	flowvision	cui	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=f09034e44f7fa3653a108a486defe1a9
cv	classification	GhostNet	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=8b02840745f682ae928da9bc618e6bb0
cv	classification	CrossFormer-T	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=ec876cce29a7d9da57f3f01bcac57bb8
cv	classification	CrossFormer-S	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=31ab1b82aa32df9fd9eca0d99226c6a2
cv	classification	CrossFormer-B	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=ee4ecc63d59f0e26e178185adf5b70e4
cv	classification	CrossFormer-L	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=bc3f08aa3513464d10aab15a75a67fa5
cv	classification	PoolFormer-S12	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=3802898d0df4ab05479425c56cbdc5d8
cv	classification	PoolFormer-S24	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=08b8873535d3179796697808cd470bba
cv	classification	PoolFormer-S36	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=e76ce4d08ffc55277ec18b584acf0d07
cv	classification	PoolFormer-M36	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=03654a36457379ae02da2cda230cf8ef
cv	classification	PoolFormer-M48	flowvision	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=52fe605fc822974a72a359c5ca68a8e2
cv	classification	gMLP	flowvision	ke	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=db86fa73ae42ec1138bbf9c282d715a7
cv	classification	ConvNeXt	flowvision	ke	完成	可拓展至：18	li	https://www.oneflow.cloud/drill/#/project/public/code?id=580631c6005d96e2fc756b697264911c
cv	classification	LeViT	flowvision	ke	完成(infer低)	可拓展至:5	li	https://www.oneflow.cloud/drill/#/project/public/code?id=e53b9e1e52602aa6887e8289aed2ae42
cv	classification	MobileViT	flowvision	li	进行中（infer低）
cv	Semantic Segmentation	fcn_resnet101_coco	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=5a67c4d8f94d50904b00e1ab7dc073b0
cv	Semantic Segmentation	fcn_resnet50_coco	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=5b958d4676e6a873a85f50f7327d5810
cv	Semantic Segmentation	deeplabv3_mobilenet_v3_large_coco	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=7fcd75aeda576e41fcc8841a5d38c44d
cv	Semantic Segmentation	deeplabv3_resnet101_coco	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=98a1dbe2c439f5fae5c24ba7c6109c47
cv	Semantic Segmentation	deeplabv3_resnet50_coco	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=56ee96d906df67c2af2390d36dffb694
cv	Semantic Segmentation	lraspp_mobilenet_v3_large_coco	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=0836620e36d9c728547fde5c08d2939d
cv	Object Detection	fasterrcnn_mobilenet_v3_large_320_fpn	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=83955d0dbd8f6e398384297300031d40
cv	Object Detection	fasterrcnn_mobilenet_v3_large_fpn	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=123c9a849cc8180206e8cfe42e32c7e3
cv	Object Detection	fasterrcnn_resnet50_fpn	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=3e9d4f6d8d81dd3626ff3a2491a4ece3
cv	Object Detection	maskrcnn_resnet50_fpn	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=bd90c25e18860ac4c4f669e0a7a2dadc
cv	Object Detection	retinanet_resnet50_fpn	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=965abe47aeac05155363492ec6b6d6c7
cv	Object Detection	ssd300_vgg16	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=c677f6ab3885b5be9fd482575ee25a21
cv	Object Detection	ssdlite320_mobilenet_v3_large	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=aacf292f065ecfbeee4b3df54bde6e55
cv	Object Detection	fcos_resnet50_fpn	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=dd9230b9351651ffce3df71878fcb470
cv	Neural Style Transfer	style_transfer.fast_neural_style	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=0006f425af1fee25d4dc14385a38a4e6
cv	Face Recognition	iresnet50	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=a61c970743904dc4773e40b0abecd064
cv	Face Recognition	iresnet101	flowvision	zhou	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=b4804fb168d41aa3a94f83abd0019a7d
cv		VisionTransformer	libai	li	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=dc07b87d3a60e36d182f908e8bb660f9
nlp		SwinTransformer	libai	li	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=62f0d66aa7441b9f10f877176af27e7d
nlp		SwinTransformerV2	libai	li	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=47260dd33f92bedfc1f024095dcbc035
nlp		ResMLP	libai	li	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=f76e38f8228c97ecadfe0482ed2fc06c
nlp		BERT	libai	li	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=c972180739924ca78d89f664b7ad135f
nlp		RoBERTa	libai	li	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=d3bbb391e714b1f780aac86e3aa074a9
nlp		T5	libai	li	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=6152fadc8e6997f972ce1a85841daf78
nlp		GPT-2	libai	li	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=161fb0c7e6c609526b97bd30d935e1b8
nlp	text_classfication	Transformer	CoModels	maolin	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=1ad00d3d7db8c504b08345461b3999b0
nlp	odd_numbers	Transformer	CoModels	maolin	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=c6bd57e6b4f6362348783da981f6f0ff
science	Equation inversion-Lorenz system	PINNs	CoModels	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=91f8fb23498318f83dd16e0e875db6bc
science	Fluid simulation-ldc	PINNs	CoModels	zhang	完成		li	https://www.oneflow.cloud/drill/#/project/public/code?id=b1914ead9370947a60b3a9c3ee60b0c0

akeeei · 2023-11-13T03:26:32Z

vit_tiny_patch16_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: ba432de

Training

cd CoModels/cv/classification/vit_tiny_patch16_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 0.003
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_tiny_patch16_224
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 53.941 Acc@5 78.964
INFO Accuracy of the network on the 98 test images: 53.9%
INFO Max accuracy: 64.19%
INFO Training time  6:49:33

推理结果

INFO * Acc@1 45.267 Acc@5 68.073
INFO Accuracy of the network on the 1563 test images: 45.3%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 1757.057121686971

akeeei · 2023-11-13T03:27:15Z

vit_tiny_patch16_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 1c8febd

Training

cd CoModels/cv/classification/vit_tiny_patch16_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 384

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_tiny_patch16_384
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 68.616 Acc@5 89.426
INFO Accuracy of the network on the 98 test images: 68.6%
INFO Max accuracy: 68.62%
INFO Training time 1:40:07

推理结果

INFO  * Acc@1 48.120 Acc@5 70.708
INFO Accuracy of the network on the 1563 test images: 48.1%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 328.12604634424923

akeeei · 2023-11-13T03:27:47Z

vit_small_patch32_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 8990745

Training

cd CoModels/cv/classification/vit_small_patch32_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 256
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_small_patch32_224
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 66.895 Acc@5 87.109
INFO Accuracy of the network on the 49 test images: 66.9%
INFO Max accuracy: 67.58%
INFO Training time 0:57:20

推理结果

INFO  * Acc@1 62.294 Acc@5 84.081
INFO Accuracy of the network on the 1563 test images: 62.3%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 2159.266911487113

akeeei · 2023-11-13T03:29:02Z

vit_small_patch32_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 43e20ad

Training

cd CoModels/cv/classification/vit_small_patch32_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 128
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 384

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_small_patch32_384
bash  infer.sh

训练过程

训练日志：
训练结果：

INFO  * Acc@1 73.145 Acc@5 93.945
INFO Accuracy of the network on the 49 test images: 73.1%
INFO Max accuracy: 74.12%
INFO Training time 2:22:48

推理结果

INFO  * Acc@1 68.424 Acc@5 88.513
INFO Accuracy of the network on the 1563 test images: 68.4%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 965.2715564422288

akeeei · 2023-11-13T03:32:39Z

vit_small_patch16_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 7dff945

Training

cd CoModels/cv/classification/vit_small_patch16_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 0.003
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_small_patch16_384
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 76.015 Acc@5 93.935
INFO Accuracy of the network on the 391 test images: 76.0%
INFO Max accuracy: 76.01%
INFO Training time 1:22:44

推理结果

INFO  * Acc@1 76.932 Acc@5 93.956
INFO Accuracy of the network on the 1563 test images: 76.9%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 425.0662919764819

akeeei · 2023-11-13T03:33:20Z

vit_base_patch32_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: a236ead

Training

cd CoModels/cv/classification/vit_base_patch32_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 256
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_base_patch32_224
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 77.516 Acc@5 93.892
INFO Accuracy of the network on the 98 test images: 77.5%
INFO Max accuracy: 77.52%
INFO Training time 0:41:03

推理结果

INFO * Acc@1 72.105 Acc@5 90.895
INFO Accuracy of the network on the 1563 test images: 72.1%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 880.851543616409

akeeei · 2023-11-13T03:33:27Z

vit_base_patch32_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 53fad98

Training

cd CoModels/cv/classification/vit_base_patch32_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 128
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 384

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_base_patch32_384
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 74.982 Acc@5 93.119
INFO Accuracy of the network on the 196 test images: 75.0%
INFO Max accuracy: 74.98%
INFO Training time 1:21:08

推理结果

INFO  * Acc@1 76.455 Acc@5 93.238
INFO Accuracy of the network on the 1563 test images: 76.5%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 341.92431427372634

akeeei · 2023-11-13T03:38:59Z

vit_base_patch16_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 9fe0c58

Training

cd CoModels/cv/classification/vit_base_patch16_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 384

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_base_patch16_384
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 73.707 Acc@5 92.468
INFO Accuracy of the network on the 391 test images: 73.7%
INFO Max accuracy: 73.71%
INFO Training time 2:21:20

推理结果

INFO  * Acc@1 78.719 Acc@5 94.443
INFO Accuracy of the network on the 1563 test images: 78.7%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 60.235781614383676

akeeei · 2023-11-13T03:39:32Z

vit_base_patch8_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 1bbc96c

Training

cd CoModels/cv/classification/vit_base_patch8_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 16
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 3e-5
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_base_patch8_224
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 80.505 Acc@5 95.880
INFO Accuracy of the network on the 782 test images: 80.5%
INFO Max accuracy: 80.51%
INFO Training time 4:41:53

推理结果

INFO  * Acc@1 76.249 Acc@5 92.587
INFO Accuracy of the network on the 1563 test images: 76.2%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 35.069299488421656

kokuro-asahi · 2023-11-13T08:45:05Z

EfficientNet_b0

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 5ec3acc

Training

cd CoModels/cv/classification/efficientnet_b0
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 32
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: efficientnet_b0
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/efficientnet_b0/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.1
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 1.25e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/efficientnet_b0
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 77.704 Acc@5 93.547

kokuro-asahi · 2023-11-13T08:52:33Z

EfficientNet_b1

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: d34926e

Training

cd CoModels/cv/classification/efficientnet_b1
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 32
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: efficientnet_b0
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/efficientnet_b1/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.1
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 1.25e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/efficientnet_b1
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 77.589 Acc@5 93.641 NFO Accuracy of the network on the 1563 test images: 77.6%

kokuro-asahi · 2023-11-14T06:58:17Z

EfficientNet_b2

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 8557d47

Training

cd CoModels/cv/classification/efficientnet_b2
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 32
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: efficientnet_b2
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/efficientnet_b2/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.1
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 1.25e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/efficientnet_b2
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 57.904 Acc@5 78.367

akeeei · 2023-11-15T07:47:08Z

vit_small_patch16_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 062487c

Training

cd CoModels/cv/classification/vit_small_patch16_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 256
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 3e-4
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_small_patch16_224
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 71.530 Acc@5 91.356
INFO Accuracy of the network on the 98 test images: 71.5%
INFO Max accuracy: 71.53%
INFO Training time 0:33:14

推理结果

INFO  * Acc@1 74.206 Acc@5 92.386
INFO Accuracy of the network on the 1563 test images: 74.2%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 725.5174421915012

akeeei · 2023-11-15T07:47:45Z

vit_large_patch32_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 01fe293

Training

cd CoModels/cv/classification/vit_large_patch32_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 64
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 384

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 3e-5
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_large_patch32_384
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 79.412 Acc@5 95.099
INFO Accuracy of the network on the 391 test images: 79.4%
INFO Max accuracy: 79.41%
INFO Training time 2:27:31

推理结果

INFO  * Acc@1 75.623 Acc@5 93.137
INFO Accuracy of the network on the 1563 test images: 75.6%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 245.38814350421615

akeeei · 2023-11-15T07:48:04Z

vit_large_patch16_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 2e46862

Training

cd CoModels/cv/classification/vit_large_patch16_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-5
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_large_patch16_224
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 84.833 Acc@5 97.480
INFO Accuracy of the network on the 782 test images: 84.8%
INFO Max accuracy: 84.83%
INFO Training time 3:28:39

推理结果

INFO  * Acc@1 83.462 Acc@5 96.732
INFO Accuracy of the network on the 1563 test images: 83.5%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 184.42577691807244

akeeei · 2023-11-15T07:48:24Z

vit_base_patch16_224_sam

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: a36a7a3

Training

cd CoModels/cv/classification/vit_base_patch16_224_sam
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 128
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_base_patch16_224_sam
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 78.311 Acc@5 93.732
INFO Accuracy of the network on the 196 test images: 78.3%
INFO Max accuracy: 78.31%
INFO Training time 0:57:02

推理结果

INFO  * Acc@1 75.530 Acc@5 92.172
INFO Accuracy of the network on the 1563 test images: 75.5%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 267.0716634529662

akeeei · 2023-11-15T07:48:41Z

vit_base_patch32_224_sam

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 72f3e9f

Training

cd CoModels/cv/classification/vit_base_patch32_224_sam
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 128
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_base_patch32_224_sam
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 70.874 Acc@5 89.295
INFO Accuracy of the network on the 196 test images: 70.9%
INFO Max accuracy: 70.87%
INFO Training time 2:21:20

推理结果

INFO  * Acc@1 63.574 Acc@5 83.926
INFO Accuracy of the network on the 1563 test images: 63.6%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 1071.7754732650496

akeeei · 2023-11-15T07:48:55Z

vit_large_patch16_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 7db4688

Training

cd CoModels/cv/classification/vit_large_patch16_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 8
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 384

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 300
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-5
  WARMUP_LR: 5e-7
  
  LR_SCHEDULER:
    NAME: cosine

  OPTIMIZER:
    NAME: adamw
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_large_patch16_384
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 85.411 Acc@5 97.703
INFO Accuracy of the network on the 1563 test images: 85.4%
INFO Max accuracy: 85.41%
INFO Training time 6:10:56

推理结果

INFO  * Acc@1 85.094 Acc@5 97.570
INFO Accuracy of the network on the 1563 test images: 85.1%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 54.038784254131365

kokuro-asahi · 2023-11-15T08:57:07Z

EfficientNet_b3

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 52d1660

Training

cd CoModels/cv/classification/efficientnet_b3
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 32
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: efficientnet_b3
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/efficientnet_b3/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.1
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 1.25e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/efficientnet_b3
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 64.891 Acc@5 74.763

kokuro-asahi · 2023-11-15T09:00:48Z

EfficientNet_b4

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 9209774

Training

cd CoModels/cv/classification/efficientnet_b4
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 32
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: efficientnet_b4
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/efficientnet_b4/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.1
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 1.25e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/efficientnet_b4
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 57.009 Acc@5 62.790

kokuro-asahi · 2023-11-15T09:10:54Z

EfficientNet_b5

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 57640c8

Training

cd CoModels/cv/classification/efficientnet_b5
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 32
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: efficientnet_b4
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/efficientnet_b4/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.1
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 1.25e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/efficientnet_b4
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 57.009 Acc@5 62.790

kokuro-asahi · 2023-11-15T09:11:46Z

EfficientNet_b6

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: e7d7b19

Training

cd CoModels/cv/classification/efficientnet_b6
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 32
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: efficientnet_b6
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/efficientnet_b6/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.1
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 1.25e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/efficientnet_b6
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 47.688 Acc@5 58.346

kokuro-asahi · 2023-11-15T09:16:15Z

EfficientNet_b7

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 834321b

Training

cd CoModels/cv/classification/efficientnet_b7
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 32
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: efficientnet_b7
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/efficientnet_b7/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.1
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 1.25e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/efficientnet_b7
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 59.689 Acc@5 83.967

akeeei · 2023-11-16T06:35:16Z

deit_tiny_patch16_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 55b5dc1

Training

cd CoModels/cv/classification/deit_tiny_patch16_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 256
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 5e-5
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/deit_tiny_patch16_224
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 72.266 Acc@5 91.309
INFO Accuracy of the network on the 49 test images: 72.3%
INFO Max accuracy: 72.27%
INFO Training time 0:38:55

推理结果

INFO  * Acc@1 72.141 Acc@5 91.159
INFO Accuracy of the network on the 1563 test images: 72.1%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 1955.4064216855359

akeeei · 2023-11-16T06:35:30Z

deit_base_patch16_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 3262c83

Training

cd CoModels/cv/classification/deit_base_patch16_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 5e-5
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/deit_base_patch16_224
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 81.535 Acc@5 95.444
INFO Accuracy of the network on the 391 test images: 81.5%
INFO Max accuracy: 81.53%
INFO Training time 2:39:41

推理结果

INFO  * Acc@1 81.816 Acc@5 95.592
INFO Accuracy of the network on the 1563 test images: 81.8%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 134.9496680093491

akeeei · 2023-11-16T06:35:35Z

deit_base_patch16_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 124adb2

Training

cd CoModels/cv/classification/deit_base_patch16_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 16
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 384

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/deit_base_patch16_384
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 82.885 Acc@5 96.207
INFO Accuracy of the network on the 782 test images: 82.9%
INFO Max accuracy: 82.96%
INFO Training time 6:03:41

推理结果

INFO  * Acc@1 82.872 Acc@5 96.234
INFO Accuracy of the network on the 1563 test images: 82.9%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 52.672114994542234

kokuro-asahi · 2023-11-16T07:07:30Z

regnet_y_400mf

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 8a056c8

Training

cd CoModels/cv/classification/regnet_y_400mf
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 128
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: regnet_y_400mf
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/regnet_y_400mf/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.4
  CLIP_GRAD: 5.0
  EPOCHS: 100
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: cosine
  MIN_LR: 5.0e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 5
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 5.0e-05

Inference

cd CoModels/cv/classification/regnet_y_400mf
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 78.245 Acc@5 93.542

kokuro-asahi · 2023-11-16T07:28:57Z

regnet_y_800mf

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 11c79bc

Training

cd CoModels/cv/classification/regnet_y_800mf
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 128
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: regnet_y_800mf
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/regnet_y_800mf/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.4
  CLIP_GRAD: 5.0
  EPOCHS: 100
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: cosine
  MIN_LR: 5.0e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 5
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 5.0e-05

Inference

cd CoModels/cv/classification/regnet_y_800mf
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 58.245 Acc@5 72.457

kokuro-asahi · 2023-11-16T07:33:35Z

regnet_y_1_6gf

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 6162652

Training

cd CoModels/cv/classification/regnet_y_1_6gf
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 128
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 4
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: regnet_y_1_6gf
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/regnet_y_1_6gf/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.4
  CLIP_GRAD: 5.0
  EPOCHS: 100
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: cosine
  MIN_LR: 5.0e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 5
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 5.0e-05

Inference

cd CoModels/cv/classification/regnet_y_1_6gf
bash  infer.sh

训练过程

训练日志：

推理结果

INFO * Acc@1 40.984 Acc@5 69.236

iwkkk · 2023-11-20T10:29:59Z

dla46_c

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: b696752

Training

cd CoModels/cv/classification/dla46_c
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 16
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 8
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: dla46_c
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/dla46_c/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.001
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 6.25e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/dla46_c
bash  infer.sh

训练过程（2 epochs）

训练日志：

推理结果

INFO * Acc@1 64.493 Acc@5 86.099 INFO Accuracy of the network on the 3125 test images: 64.5%

iwkkk · 2023-11-20T10:34:09Z

dla46x_c

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: f81242e

Training

cd CoModels/cv/classification/dla46x_c
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 16
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 8
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: dla46x_c
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/dla46x_c/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.001
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 6.25e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/dla46x_c
bash  infer.sh

训练过程（3 epochs）

训练日志：

推理结果

INFO * Acc@1 65.656 Acc@5 86.662 INFO Accuracy of the network on the 3125 test images: 65.7%

iwkkk · 2023-11-20T10:38:09Z

dla60

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: '85f3944'

Training

cd CoModels/cv/classification/dla60
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 16
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 8
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: dla60
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/dla60/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.001
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 6.25e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/dla60
bash  infer.sh

训练过程（1 epoch）

训练日志：

推理结果

INFO * Acc@1 76.824 Acc@5 93.188 INFO Accuracy of the network on the 3125 test images: 76.8%

iwkkk · 2023-11-20T10:40:47Z

dla60x

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 4bf5419

Training

cd CoModels/cv/classification/dla60x
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 16
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 8
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: dla60x
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/dla60x/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.001
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 6.25e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/dla60x
bash  infer.sh

训练过程（1 epoch）

训练日志：

推理结果

INFO * Acc@1 78.070 Acc@5 93.958 INFO Accuracy of the network on the 3125 test images: 78.1%

iwkkk · 2023-11-20T10:42:41Z

dla60x_c

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 0703522

Training

cd CoModels/cv/classification/dla60x_c
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 16
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 8
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: dla60x_c
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/dla60x_c/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.001
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 6.25e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/dla60x_c
bash  infer.sh

训练过程（1 epoch）

训练日志：

推理结果

INFO * Acc@1 67.549 Acc@5 88.213 INFO Accuracy of the network on the 3125 test images: 67.5%

iwkkk · 2023-11-20T10:44:48Z

dla102

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 3a5f0b1

Training

cd CoModels/cv/classification/dla102
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 16
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 8
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: dla102
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/dla102/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.001
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 6.25e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/dla102
bash  infer.sh

训练过程（1 epoch）

训练日志：

推理结果

INFO * Acc@1 77.711 Acc@5 93.788 INFO Accuracy of the network on the 3125 test images: 77.7%

iwkkk · 2023-11-20T10:47:49Z

dla102x

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 26922fb

Training

cd CoModels/cv/classification/dla102x
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 16
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 8
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: dla102x
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/dla102x/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.001
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 6.25e-07
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/dla102x
bash  infer.sh

训练过程（2 epochs）

训练日志：

推理结果

INFO * Acc@1 78.283 Acc@5 94.101 INFO Accuracy of the network on the 3125 test images: 78.3%

Drlifei · 2023-11-21T02:14:58Z

deit_large_patch16_LS_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: da87b13

Training

cd CoModels/cv/classification/deit_large_patch16_LS_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/deit_large_patch16_LS_224
bash  infer.sh

训练过程

训练日志：

推理结果

Drlifei · 2023-11-21T02:18:32Z

deit_small_patch16_LS_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 6115505

Training

cd CoModels/cv/classification/deit_small_patch16_LS_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/deit_small_patch16_LS_224
bash  infer.sh

训练过程

训练日志：

推理结果

Drlifei · 2023-11-21T02:21:18Z

deit_large_patch16_LS_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 334cfc1

Training

cd CoModels/cv/classification/deit_large_patch16_LS_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/deit_large_patch16_LS_384
bash  infer.sh

训练过程

训练日志：

推理结果

Drlifei · 2023-11-21T02:28:10Z

deit_small_patch16_LS_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 464fcd5

Training

cd CoModels/cv/classification/deit_small_patch16_LS_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/deit_small_patch16_LS_384
bash  infer.sh

训练过程

训练日志：

推理结果

akeeei · 2023-11-21T07:08:43Z

Fan-ViT-small

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 8021d78

Training

cd CoModels/cv/classification/fan_vit_small
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 64
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/fan_vit_small
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 82.238 Acc@5 96.037
INFO Accuracy of the network on the 196 test images: 82.2%
INFO Max accuracy: 82.24%
INFO Training time 2:23:30

推理结果

INFO  * Acc@1 82.471 Acc@5 96.216
INFO Accuracy of the network on the 1563 test images: 82.5%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 217.21194210108126

akeeei · 2023-11-21T07:09:23Z

Fan-ViT-tiny

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 0a5b6fb

Training

cd CoModels/cv/classification/fan_vit_tiny
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/fan_vit_tiny
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 79.013 Acc@5 94.487
INFO Accuracy of the network on the 391 test images: 79.0%
INFO Max accuracy: 79.01%
INFO Training time 3:24:50

推理结果

INFO  * Acc@1 79.122 Acc@5 94.610
INFO Accuracy of the network on the 1563 test images: 79.1%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 357.83025676395147

akeeei · 2023-11-21T07:12:04Z

fan_hybrid_base_in22k_1k

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 94d67d4

Training

cd CoModels/cv/classification/fan_hybrid_base_in22k_1k
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/fan_hybrid_base_in22k_1k
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 85.136 Acc@5 97.407
INFO Accuracy of the network on the 391 test images: 85.1%
INFO Max accuracy: 85.14%
INFO Training time 1:44:25

推理结果

INFO  * Acc@1 85.483 Acc@5 97.516
INFO Accuracy of the network on the 1563 test images: 85.5%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 158.6908637349492

iwkkk · 2023-11-21T07:32:52Z

dla169

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: dea3f43
flowvision: 0.2.1
CoModels commit: 5a89a30

Training

cd CoModels/cv/classification/dla169
bash train.sh

训练所用超参数

AMP_OPT_LEVEL: ''
AUG:
  AUTO_AUGMENT: rand-m9-mstd0.5-inc1
  COLOR_JITTER: 0.4
  CUTMIX: 0.0
  CUTMIX_MINMAX: null
  MIXUP: 0.0
  MIXUP_MODE: batch
  MIXUP_PROB: 1.0
  MIXUP_SWITCH_PROB: 0.5
  RECOUNT: 1
  REMODE: pixel
  REPROB: 0.25
BASE:
- ''
DATA:
  BATCH_SIZE: 32
  CACHE_MODE: part
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224
  INTERPOLATION: bicubic
  NUM_CLASSES: 1000
  NUM_WORKERS: 8
  PIN_MEMORY: true
  SYNTHETIC_DATA: false
  ZIP_MODE: false
EVAL_MODE: true
LOCAL_RANK: 0
MODEL:
  ARCH: dla169
  CHECKPOINTS: null
  DROP_PATH_RATE: 0.1
  DROP_RATE: 0.0
  LABEL_SMOOTHING: 0.1
  NUM_CLASSES: 1000
  PRETRAINED: true
  RESUME: ''
OUTPUT: output/dla169/default
PRINT_FREQ: 50
SAVE_FREQ: 1
SEED: 42
TAG: default
TEST:
  CROP: true
  SEQUENTIAL: false
THROUGHPUT_MODE: false
TRAIN:
  ACCUMULATION_STEPS: 0
  AUTO_RESUME: false
  BASE_LR: 0.001
  CLIP_GRAD: 5.0
  EPOCHS: 90
  LR_SCHEDULER:
    DECAY_EPOCHS: 30
    DECAY_RATE: 0.1
    MILESTONES:
    - 150
    - 225
    NAME: step
  MIN_LR: 1.25e-06
  OPTIMIZER:
    BETAS:
    - 0.9
    - 0.999
    EPS: 1.0e-08
    MOMENTUM: 0.9
    NAME: sgd
  START_EPOCH: 0
  USE_CHECKPOINT: false
  WARMUP_EPOCHS: 0
  WARMUP_LR: 5.0e-07
  WEIGHT_DECAY: 0.0001

Inference

cd CoModels/cv/classification/dla169
bash  infer.sh

训练过程（1 epoch）

训练日志：

推理结果

INFO * Acc@1 78.530 Acc@5 94.282 INFO Accuracy of the network on the 1563 test images: 78.5%

akeeei · 2023-11-22T03:35:05Z

Fluid simulation-ldc

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 513599f

Training

cd CoModels/science/ldc
bash train.sh

Inference

cd CoModels/science/ldc
bash  infer.sh

训练过程

num_epoch: 30000, loss: 1.00961
sub losses:
0.04032142
0.06947027
0.04393552
0.1423571
0.7135256

推理结果

Load checkpoint
100% [..............................................................................] 19311 / 19311
Start infer

经过推断，将生成三个后缀为vtu的文件，分别表示LDC问题的水平流速、垂直流速和压力分布。可以使用Paraview等软件对结果进行可视化和后期处理。

akeeei · 2023-11-22T03:35:48Z

fan_hybrid_base_in22k_1k_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 7471d3e

Training

cd CoModels/cv/classification/fan_hybrid_base_in22k_1k
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 16
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 384

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/fan_hybrid_base_in22k_1k
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 85.876 Acc@5 97.701
INFO Accuracy of the network on the 1563 test images: 85.9%
INFO Max accuracy: 85.88%
INFO Training time 5:01:29

推理结果

INFO  * Acc@1 83.982 Acc@5 97.006
INFO Accuracy of the network on the 1563 test images: 84.0%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 105.20254379907955

akeeei · 2023-11-22T03:36:33Z

deit_base_patch16_LS_224

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 66017be

Training

cd CoModels/cv/classification/deit_base_patch16_LS_224
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 128
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-3
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/deit_base_patch16_LS_224
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 83.054 Acc@5 96.147
INFO Accuracy of the network on the 98 test images: 83.1%
INFO Max accuracy: 83.05%
INFO Training time 0:57:07

推理结果

INFO  * Acc@1 83.684 Acc@5 96.549
INFO Accuracy of the network on the 1563 test images: 83.7%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 266.17696222068827

akeeei · 2023-11-22T03:37:38Z

fan_large_16_p4_hybrid_in22k_1k_384

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 5ac06cd

Training

cd CoModels/cv/classification/fan_large_16_p4_hybrid_in22k_1k_384
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 8
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 384

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/fan_large_16_p4_hybrid_in22k_1k_384
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 86.908 Acc@5 98.005
INFO Accuracy of the network on the 1563 test images: 86.9%
INFO Max accuracy: 86.91%
INFO Training time 3:50:51

推理结果

INFO  * Acc@1 85.251 Acc@5 97.614
INFO Accuracy of the network on the 1563 test images: 85.3%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 242.6017645049282

akeeei · 2023-11-22T03:38:28Z

fan_large_16_p4_hybrid_in22k_1k

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: df4b987

Training

cd CoModels/cv/classification/fan_large_16_p4_hybrid_in22k_1k
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/fan_large_16_p4_hybrid_in22k_1k
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 86.171 Acc@5 97.823
INFO Accuracy of the network on the 391 test images: 86.2%
INFO Max accuracy: 86.17%
INFO Training time 1:16:32

推理结果

INFO  * Acc@1 86.382 Acc@5 97.933
INFO Accuracy of the network on the 1563 test images: 86.4%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 111.53712566903394

akeeei · 2023-11-22T08:07:01Z

Equation inversion-Lorenz system

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 1cdb54a

Training

cd CoModels/science/lorenz_system
bash train.sh

Inference

cd CoModels/science/lorenz_system
bash  infer.sh

训练过程

num_epoch: 20000, loss: 0.02206432
sub losses:
0.005381995
0.005250522
0.01032578
1.64854e-05
0.001089531
variables:
C1: 10.00202
C2: 14.99769
C3: 2.667474

推理结果

Load checkpoint
100% [..............................................................................] 64755 / 64755
Start infer
Evaluate variables:
C1: 10.00202
C2: 14.99769
C3: 2.667474

akeeei · 2023-11-23T02:17:59Z

poolformer_s24

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 6a6d75f

Training

cd CoModels/cv/classification/poolformer_s24
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 64
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/poolformer_s24
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 80.182 Acc@5 95.005
INFO Accuracy of the network on the 196 test images: 80.2%
INFO Max accuracy: 80.18%
INFO Training time 1:14:36

推理结果

INFO  * Acc@1 80.301 Acc@5 95.140
INFO Accuracy of the network on the 1563 test images: 80.3%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 322.20938907454337

akeeei · 2023-11-23T02:20:15Z

poolformer_s36

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: b5c776c

Training

cd CoModels/cv/classification/poolformer_s36
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 64
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/poolformer_s36
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 81.035 Acc@5 95.338
INFO Accuracy of the network on the 196 test images: 81.0%
INFO Max accuracy: 81.04%
INFO Training time 1:38:42

推理结果

INFO  * Acc@1 81.276 Acc@5 95.457
INFO Accuracy of the network on the 1563 test images: 81.3%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 288.32020883483756

akeeei · 2023-11-23T02:22:41Z

poolformer_m36

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: d62af08

Training

cd CoModels/cv/classification/poolformer_m36
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 64
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/poolformer_m36
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 81.721 Acc@5 95.468
INFO Accuracy of the network on the 196 test images: 81.7%
INFO Max accuracy: 81.72%
INFO Training time 1:02:32

推理结果

INFO  * Acc@1 82.090 Acc@5 95.710
INFO Accuracy of the network on the 1563 test images: 82.1%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 589.16099251004

akeeei · 2023-11-23T02:27:47Z

poolformer_m48

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: dea189a

Training

cd CoModels/cv/classification/poolformer_m48
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 64
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/poolformer_m48
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 82.165 Acc@5 95.722
INFO Accuracy of the network on the 391 test images: 82.2%
INFO Max accuracy: 82.16%
INFO Training time 1:13:00

推理结果

INFO  * Acc@1 82.441 Acc@5 95.892
INFO Accuracy of the network on the 1563 test images: 82.4%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 445.26869289873656

akeeei · 2023-11-24T02:26:21Z

pvt_medium

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: a06911a

Training

cd CoModels/cv/classification/pvt_medium
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/pvt_medium
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 81.364 Acc@5 95.580
INFO Accuracy of the network on the 391 test images: 81.4%
INFO Max accuracy: 81.36%
INFO Training time 1:48:35

推理结果

INFO  * Acc@1 81.208 Acc@5 95.650
INFO Accuracy of the network on the 1563 test images: 81.2%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 236.46548065860975

akeeei · 2023-11-24T02:28:15Z

pvt_tiny

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 269c2cb

Training

cd CoModels/cv/classification/pvt_tiny
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 64
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/pvt_tiny
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 81.704 Acc@5 95.849
INFO Accuracy of the network on the 391 test images: 81.7%
INFO Max accuracy: 81.70%
INFO Training time 1:23:07

推理结果

INFO  * Acc@1 75.109 Acc@5 92.428
INFO Accuracy of the network on the 1563 test images: 75.1%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 655.271585262991

akeeei · 2023-11-24T02:31:07Z

pvt_large

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: bce354e

Training

cd CoModels/cv/classification/pvt_large
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/pvt_large
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 75.196 Acc@5 92.426
INFO Accuracy of the network on the 391 test images: 75.2%
INFO Max accuracy: 75.20%
INFO Training time 0:36:00

推理结果

INFO  * Acc@1 81.689 Acc@5 95.841
INFO Accuracy of the network on the 1563 test images: 81.7%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 164.84514218123954

akeeei · 2023-11-24T02:34:58Z

deit_base_patch16_LS_224_in21k

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 9c3442d

Training

cd CoModels/cv/classification/deit_base_patch16_LS_224_in21k
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 5e-5
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/deit_base_patch16_LS_224_in21k
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 85.158 Acc@5 97.409
INFO Accuracy of the network on the 391 test images: 85.2%
INFO Max accuracy: 85.16%
INFO Training time 0:57:07

推理结果

INFO  * Acc@1 85.503 Acc@5 97.560
INFO Accuracy of the network on the 1563 test images: 85.5%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 570.4031648236078

akeeei · 2023-11-24T02:38:17Z

vit_base_patch16_224_miil

机器：[email protected]
数据集：imagenet
oneflow version: 0.9.1.dev20231017+cu117
oneflow git commit: 553c55e
flowvision: 0.2.1
CoModels commit: 0055013

Training

cd CoModels/cv/classification/vit_base_patch16_224_miil
bash train.sh

训练所用超参数

DATA:
  BATCH_SIZE: 32
  DATASET: imagenet
  DATA_PATH: /data/dataset/ImageNet/extract
  IMG_SIZE: 224

MODEL:
  PRETRAINED: True
  LABEL_SMOOTHING: 0.11

TRAIN:
  START_EPOCH: 0
  EPOCHS: 90
  WARMUP_EPOCHS: 30
  WEIGHT_DECAY: 0.3
  BASE_LR: 1e-4
  WARMUP_LR: 5e-7

  LR_SCHEDULER:
    NAME: step

  OPTIMIZER:
    NAME: sgd
    MOMENTUM: 0.9

Inference

cd CoModels/cv/classification/vit_base_patch16_224_miil
bash  infer.sh

训练过程

训练日志：

训练结果：

INFO  * Acc@1 75.447 Acc@5 92.909
INFO Accuracy of the network on the 391 test images: 75.4%
INFO Max accuracy: 75.45%
INFO Training time 1:37:03

推理结果

INFO  * Acc@1 33.865 Acc@5 53.165
INFO Accuracy of the network on the 1563 test images: 33.9%
INFO throughput averaged with 30 times
INFO batch_size 32 throughput 276.1159336251757

levi131 self-assigned this Nov 7, 2023

模型适配进度（拓展） #163

模型适配进度（拓展） #163

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