Official Models

Computer Vision

Image Classification（backbone）

The following results are tested on Ascend 910 with ImageNet-1K.

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

Object Detection

The following results are tested on Ascend 910 with COCO2017.

Classic

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
fastrcnn	58		link
maskrcnn_mobilenetv1	coming soon		link
maskrcnn_resnet50	coming soon		link

Semantic Segmentation

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

OCR

model	dataset	fscore	mindocr recipe	vanilla mindspore
dbnet_mobilenetv3	icdar2015	77.28	config
dbnet_resnet18	icdar2015	83.71	config
dbnet_resnet50	icdar2015	84.99	config	link
dbnet_resnet50	msra-td500	85.03	config
dbnet++_resnet50	icdar2015	86.60	config
psenet_resnet152	icdar2015	82.06	config	link
east_resnet50	icdar2015	84.87	config	link
svtr_tiny	IC03,13,15,IIT,etc	89.02	config
crnn_vgg7	IC03,13,15,IIT,etc	82.03	config	link
crnn_resnet34_vd	IC03,13,15,IIT,etc	84.45	config
rare_resnet34_vd	IC03,13,15,IIT,etc	85.19	config

Face

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