官方模型库

领域套件与扩展包

计算机视觉

图像分类（骨干类）

以下数据基于Ascend 910环境和ImageNet-1K数据集获得。

model	acc@1	mindcv recipe	vanilla mindspore
vgg11	71.86	config
vgg13	72.87	config
vgg16	74.61	config	link
vgg19	75.21	config	link
resnet18	70.21	config	link
resnet34	74.15	config	link
resnet50	76.69	config	link
resnet101	78.24	config	link
resnet152	78.72	config	link
resnetv2_50	76.90	config
resnetv2_101	78.48	config
dpn92	79.46	config
dpn98	79.94	config
dpn107	80.05	config
dpn131	80.07	config
densenet121	75.64	config
densenet161	79.09	config
densenet169	77.26	config
densenet201	78.14	config
seresnet18	71.81	config
seresnet34	75.36	config
seresnet50	78.31	config
seresnext26	77.18	config
seresnext50	78.71	config
skresnet18	73.09	config
skresnet34	76.71	config
skresnet50_32x4d	79.08	config
resnext50_32x4d	78.53	config
resnext101_32x4d	79.83	config
resnext101_64x4d	80.30	config
resnext152_64x4d	80.52	config
rexnet_x09	77.07	config
rexnet_x10	77.38	config
rexnet_x13	79.06	config
rexnet_x15	79.94	config
rexnet_x20	80.64	config
resnest50	80.81	config
resnest101	82.50	config
res2net50	79.35	config
res2net101	79.56	config
res2net50_v1b	80.32	config
res2net101_v1b	95.41	config
googlenet	72.68	config
inceptionv3	79.11	config	link
inceptionv4	80.88	config	link
mobilenet_v1_025	53.87	config
mobilenet_v1_050	65.94	config
mobilenet_v1_075	70.44	config
mobilenet_v1_100	72.95	config
mobilenet_v2_075	69.98	config
mobilenet_v2_100	72.27	config
mobilenet_v2_140	75.56	config
mobilenet_v3_small	68.10	config
mobilenet_v3_large	75.23	config	link
shufflenet_v1_g3_x0_5	57.05	config
shufflenet_v1_g3_x1_5	67.77	config	link
shufflenet_v2_x0_5	57.05	config
shufflenet_v2_x1_0	67.77	config	link
shufflenet_v2_x1_5	57.05	config
shufflenet_v2_x2_0	67.77	config
xception	79.01	config	link
ghostnet_50	66.03	config
ghostnet_100	73.78	config
ghostnet_130	75.50	config
nasnet_a_4x1056	73.65	config
mnasnet_0.5	68.07	config
mnasnet_0.75	71.81	config
mnasnet_1.0	74.28	config
mnasnet_1.4	76.01	config
efficientnet_b0	76.89	config	link
efficientnet_b1	78.95	config	link
efficientnet_b2	79.80		link
efficientnet_b3	80.50		link
efficientnet_v2	83.77		link
regnet_x_200mf	68.74	config
regnet_x_400mf	73.16	config
regnet_x_600mf	73.34	config
regnet_x_800mf	76.04	config
regnet_y_200mf	70.30	config
regnet_y_400mf	73.91	config
regnet_y_600mf	75.69	config
regnet_y_800mf	76.52	config
mixnet_s	75.52	config
mixnet_m	76.64	config
mixnet_l	78.73	config
hrnet_w32	80.64	config
hrnet_w48	81.19	config
bit_resnet50	76.81	config
bit_resnet50x3	80.63	config
bit_resnet101	77.93	config
repvgg_a0	72.19	config
repvgg_a1	74.19	config
repvgg_a2	76.63	config
repvgg_b0	74.99	config
repvgg_b1	78.81	config
repvgg_b2	79.29	config
repvgg_b3	80.46	config
repvgg_b1g2	78.03	config
repvgg_b1g4	77.64	config
repvgg_b2g4	78.80	config
repmlp_t224	76.71	config
convnext_tiny	81.91	config
convnext_small	83.40	config
convnext_base	83.32	config
vit_b_32_224	75.86	config	link
vit_l_16_224	76.34	config
vit_l_32_224	73.71	config
swintransformer_tiny	80.82	config	link
pvt_tiny	74.81	config
pvt_small	79.66	config
pvt_medium	81.82	config
pvt_large	81.75	config
pvt_v2_b0	71.50	config
pvt_v2_b1	78.91	config
pvt_v2_b2	81.99	config
pvt_v2_b3	82.84	config
pvt_v2_b4	83.14	config
pit_ti	72.96	config
pit_xs	78.41	config
pit_s	80.56	config
pit_b	81.87	config
coat_lite_tiny	77.35	config
coat_lite_mini	78.51	config
coat_tiny	79.67	config
convit_tiny	73.66	config
convit_tiny_plus	77.00	config
convit_small	81.63	config
convit_small_plus	81.80	config
convit_base	82.10	config
convit_base_plus	81.96	config
crossvit_9	73.56	config
crossvit_15	81.08	config
crossvit_18	81.93	config
mobilevit_xx_small	68.90	config
mobilevit_x_small	74.98	config
mobilevit_small	78.48	config
visformer_tiny	78.28	config
visformer_tiny_v2	78.82	config
visformer_small	81.76	config
visformer_small_v2	82.17	config
edgenext_xx_small	71.02	config
edgenext_x_small	75.14	config
edgenext_small	79.15	config
edgenext_base	82.24	config
poolformer_s12	77.33	config
xcit_tiny_12_p16	77.67	config

目标检测

以下数据基于Ascend 910环境和COCO2017数据集获得。

yolo

model	map	mindyolo recipe	vanilla mindspore
yolov8_n	37.2	config
yolov8_s	44.6	config
yolov8_m	50.5	config
yolov8_l	52.8	config
yolov8_x	53.7	config
yolov7_t	37.5	config
yolov7_l	50.8	config
yolov7_x	52.4	config
yolov5_n	27.3	config
yolov5_s	37.6	config	link
yolov5_m	44.9	config
yolov5_l	48.5	config
yolov5_x	50.5	config
yolov4_csp	45.4	config
yolov4_csp(silu)	45.8	config	link
yolov3_darknet53	45.5	config	link
yolox_n	24.1	config
yolox_t	33.3	config
yolox_s	40.7	config
yolox_m	46.7	config
yolox_l	49.2	config
yolox_x	51.6	config
yolox_darknet53	47.7	config

经典

model	map	mind_series recipe	vanilla mindspore
ssd_vgg16	23.2		link
ssd_mobilenetv1	22.0		link
ssd_mobilenetv2	29.1		link
ssd_resnet50	34.3		link
fasterrcnn	58	link	link
maskrcnn_mobilenetv1	coming soon		link
maskrcnn_resnet50	coming soon		link

语义分割

model	mind_series recipe	vanilla mindspore
ocrnet	link	link
deeplab v3		link
deeplab v3 plus		link
unet		link

OCR

文本检测

model	dataset	fscore	mindocr recipe	vanilla mindspore
dbnet_mobilenetv3	icdar2015	77.23	config	link
dbnet_resnet18	icdar2015	81.73	config	link
dbnet_resnet50	icdar2015	85.05	config	link
dbnet++_resnet50	icdar2015	86.74	config
psenet_resnet152	icdar2015	82.06	config	link
east_resnet50	icdar2015	84.87	config	link
fcenet_resnet50	icdar2015	84.12	config

文本识别

model	dataset	acc	mindocr recipe	vanilla mindspore
svtr_tiny	IC03,13,15,IIIT,etc	89.02	config
crnn_vgg7	IC03,13,15,IIIT,etc	82.03	config	link
crnn_resnet34_vd	IC03,13,15,IIIT,etc	84.45	config
rare_resnet34_vd	IC03,13,15,IIIT,etc	85.19	config	link

文本方向分类

model	dataset	acc	mindocr recipe
mobilenetv3	RCTW17,MTWI,LSVT	94.59	config

人脸

model	dataset	acc	mindface recipe	vanilla mindspore
arcface_mobilefacenet-0.45g	MS1MV2	98.70	config
arcface_r50	MS1MV2	99.76	config
arcface_r100	MS1MV2	99.38	config	link
arcface_vit_t	MS1MV2	99.71	config
arcface_vit_s	MS1MV2	99.76	config
arcface_vit_b	MS1MV2	99.81	config
arcface_vit_l	MS1MV2	99.75	config
retinaface_mobilenet_0.25	WiderFace	90.77/88.2/74.76	config	link
retinaface_r50	WiderFace	95.07/93.61/84.84	config	link

语言模型

model	mindformer recipe	vanilla mindspore
bert_base	config	link
t5_small	config
gpt2_small	config
gpt2_13b	config
gpt2_52b	config
pangu_alpha	config
glm_6b	config
glm_6b_lora	config
llama_7b	config
llama_13b	config
llama_65b	config
llama_7b_lora	config
bloom_560m	config
bloom_7.1b	config
bloom_65b	config
bloom_176b	config

强化学习

Algorithm	Discrete Action Space	Continuous Action Space	CPU	GPU	Ascend	Environment	Reward
DQN	✅	/	✅	✅	✅	CartPole-v0	195
PPO	/	✅	✅	✅	✅	HalfCheetah-v2	4800
AC	✅	/	✅	✅	✅	CartPole-v0	195
A2C	✅	/	✅	✅	✅	CartPole-v0	195
DDPG	/	✅	✅	✅	✅	HalfCheetah-v2	4800
QMIX	✅	/	✅	✅	✅	SMAC/Simple Spread	90%/-145
SAC	/	✅	✅	✅	✅	HalfCheetah-v2	4800
TD3	/	✅	✅	✅	✅	HalfCheetah-v2	4800
C51	✅	/	✅	✅	✅	CartPole-v0	195
A3C	✅	/	/	✅	✅	CartPole-v0	195
CQL	/	✅	✅	✅	✅	Hopper-v0	3500
MAPPO	✅	/	✅	✅	✅	Simple Spread	-145
GAIL	/	✅	✅	✅	✅	HalfCheetah-v2	4800
AWAC	/	✅	✅	✅	✅	Ant-v2	5000
Dreamer	/	✅	/	✅	✅	Walker-walk	900
IQL	/	✅	✅	✅	✅	Walker2d-v2	3000
MADDPG	✅	/	✅	✅	✅	simple_spread	-140
Double DQN	✅	/	✅	✅	✅	CartPole-v0	195
Policy Gradient	✅	/	✅	✅	✅	CartPole-v0	195
Dueling DQN	✅	/	✅	✅	✅	CartPole-v0	195

科学计算套件

领域	网络	MindSpore实现	Ascend	GPU
通用物理	auq_pinns	Link	✅	✅
通用物理	cpinns	Link	✅	✅
通用物理	deep_hpms	Link	✅	✅
通用物理	deep_ritz	Link	✅	✅
通用物理	deepbsde	Link		✅
通用物理	deeponet	Link		✅
通用物理	dgm	Link	✅	✅
通用物理	fbsnns	Link	✅	✅
通用物理	fpinns	Link	✅	✅
通用物理	gradient_pathologies_pinns	Link	✅	✅
通用物理	hp_vpinns	Link	✅	✅
通用物理	laaf	Link	✅	✅
通用物理	mgnet	Link	✅	✅
通用物理	multiscale_pinns	Link	✅	✅
通用物理	pfnn	Link		✅
通用物理	phygeonet	Link	✅	✅
通用物理	pi_deeponet	Link		✅
通用物理	pinns	Link		✅
通用物理	pinns_ntk	Link	✅	✅
通用物理	ppinns	Link	✅	✅
通用物理	xpinns	Link	✅	✅
哈密顿系统	sympnets	Link	✅	✅
弹性动力学	pinn_elastodynamics	Link	✅	✅
热力学	pinn_heattransfer	Link	✅	✅
气象学	enso	Link	✅	✅
地质学	inversion_net	Link	✅	✅
地质学	pinn_helmholtz	Link	✅	✅
海洋物理	ocean_model	Link		✅
海洋物理	pinns_swe	Link	✅	✅
电磁学	meta_auto_decoder	Link	✅	✅
电磁学	pinn_fwi	Link	✅	✅
电磁学	time_domain_maxwell	Link	✅	✅
电磁学	frequency_domain_maxwell	Link	✅	✅
电磁学	AD_FDTD	Link		✅
电磁学	SED_ANN	Link	✅	✅
电磁学	Metasurface_holograms	Link	✅	✅
电磁学	maxwell_net	Link	✅	✅
计算生物	MEGA-Fold	Link	✅	✅
计算生物	MEGA-EvoGen	Link	✅	✅
计算生物	MEGA-Assessment	Link	✅	✅
计算生物	ColabDesign	Link	✅	✅
计算生物	DeepFRI	Link	✅	✅
计算生物	FAAST	Link	✅	✅
计算生物	JT-VAE	Link	✅	✅
计算生物	MG-BERT	Link	✅	✅
计算生物	Multimer	Link	✅	✅
计算生物	ProteinMPNN	Link	✅	✅
计算生物	UFold	Link	✅	✅
计算生物	ESM-IF1	Link	✅	✅
计算生物	ESM2	Link	✅	✅
计算生物	Grover	Link	✅	✅
计算生物	Pafnucy	Link	✅	✅
计算流体	FNO1D	Link	✅	✅
计算流体	KNO1D	Link	✅	✅
计算流体	FNO2D	Link	✅	✅
计算流体	KNO2D	Link	✅	✅
计算流体	FNO3D	Link	✅	✅
计算流体	CAE-LSTM	Link	✅	✅
计算流体	eHDNN	Link	✅	✅
计算流体	HDNN	Link	✅	✅
计算流体	ViT	Link	✅	✅
计算流体	PeRCNN	Link	✅	✅
计算流体	Burgers1D	Link	✅	✅
计算流体	Cylinder Flow	Link	✅	✅
计算流体	PDE-Net	Link	✅	✅
计算流体	hfm	Link	✅	✅
计算流体	label_free_dnn_surrogate	Link	✅	✅
计算流体	nsf_nets	Link	✅	✅

大模型套件

Transformers

model	dataset	metric	score	mindformers config
bert_base_uncased	wiki	-	-	config
bert_tiny_uncased	wiki	-	-	config
qa_bert_base_uncased	SQuAD v1.1	EM / F1	80.74 / 88.33	config
tokcls_bert_base_chinese_cluener	CLUENER	Entity F1	0.7905	config
txtcls_bert_base_uncased_mnli	Mnli	Entity F1	84.80%	config
clip_vit_b_32	Cifar100	Accuracy	57.24%	config
clip_vit_b_16	Cifar100	Accuracy	61.41%	config
clip_vit_l_14	Cifar100	Accuracy	69.67%	config
clip_vit_l_14@336	Cifar100	Accuracy	68.19%	config
glm_6b	ADGEN	BLEU-4 / Rouge-1 / Rouge-2 / Rouge-l	8.42 / 31.75 / 7.98 / 25.28	config
gpt2	wikitext-2	-	-	config
gpt2_13b	wikitext-2	-	-	config
gpt2_52b	wikitext-2	-	-	config
llama_7b	alpac	-	-	config
llama_13b	alpac	-	-	config
mae_vit_base_p16	ImageNet-1K	-	-	config
vit_base_p16	ImageNet-1K	Accuracy	83.71%	config
pangualpha_2_6b	悟道数据集	-	-	config
pangualpha_13b	悟道数据集	-	-	config
swin_base_p4w7	ImageNet-1K	Accuracy	83.44%	config
t5_small	WMT16	-	-	config
t5_tiny	WMT16	-	-	config

推荐

model	dataset	auc	mindrec recipe	vanilla mindspore
Wide&Deep	Criteo	0.8	link	link
Deep&Cross Network (DCN)	Criteo	0.8	link	link