benchmark_results.md · MindSpore Lab/mindcv-1

Model	Context	Top-1 (%)	Top-5 (%)	Params(M)	Recipe	Download
bit_resnet50	D910x8-G	76.81	93.17	25.55	yaml	weights
bit_resnet50x3	D910x8-G	80.63	95.12	217.31	yaml	weights
bit_resnet101	D910x8-G	77.93	93.75	44.54	yaml	weights
coat_lite_tiny	D910x8-G	77.35	93.43	5.72	yaml	weights
coat_lite_mini	D910x8-G	78.51	93.84	11.01	yaml	weights
convit_tiny	D910x8-G	73.66	91.72	5.71	yaml	weights
convit_tiny_plus	D910x8-G	77.00	93.60	9.97	yaml	weights
convit_small	D910x8-G	81.63	95.59	27.78	yaml	weights
convit_small_plus	D910x8-G	81.80	95.42	48.98	yaml	weights
convit_base	D910x8-G	82.10	95.52	86.54	yaml	weights
convit_base_plus	D910x8-G	81.96	95.04	153.13	yaml	weights
ConvNeXt_tiny	D910x64-G	81.91	95.79	28.59	yaml	weights
ConvNeXt_small	D910x64-G	83.40	96.36	50.22	yaml	weights
ConvNeXt_base	D910x64-G	83.32	96.24	88.59	yaml	weights
crossvit_15	D910x8-G	81.08	95.33	27.27	yaml	weights
crossvit_18	D910x8-G	81.93	95.75	43.27	yaml	weights
densenet_121	D910x8-G	75.64	92.84	8.06	yaml	weights
densenet_161	D910x8-G	79.09	94.66	28.90	yaml	weights
densenet_169	D910x8-G	77.26	93.71	14.31	yaml	weights
densenet_201	D910x8-G	78.14	94.08	20.24	yaml	weights
dpn92	D910x8-G	79.46	94.49	37.79	yaml	weights
dpn98	D910x8-G	79.94	94.57	61.74	yaml	weights
dpn107	D910x8-G	80.05	94.74	87.13	yaml	weights
dpn131	D910x8-G	80.07	94.72	79.48	yaml	weights
edgenext_xx_small	D910x8-G	71.02	89.99	1.33	yaml	weights
edgenext_x_small	D910x8-G	75.14	92.50	2.34	yaml	weights
edgenext_small	D910x8-G	79.15	94.39	5.59	yaml	weights
edgenext_base	D910x8-G	82.24	95.94	18.51	yaml	weights
efficientnet_b0	D910x64-G	76.95	93.16	5.33	yaml	weights
GoogLeNet	D910x8-G	72.68	90.89	6.99	yaml	weights
hrnet_w32	D910x8-G	80.64	95.44	41.30	yaml	weights
hrnet_w48	D910x8-G	81.19	95.69	77.57	yaml	weights
Inception_v3	D910x8-G	79.11	94.40	27.20	yaml	weights
Inception_v4	D910x8-G	80.88	95.34	42.74	yaml	weights
MixNet_s	D910x8-G	75.52	92.52	4.17	yaml	weights
MixNet_m	D910x8-G	76.64	93.05	5.06	yaml	weights
MnasNet-B1-0_75	D910x8-G	71.81	90.53	3.20	yaml	weights
MnasNet-B1-1_0	D910x8-G	74.28	91.70	4.42	yaml	weights
MnasNet-B1-1_4	D910x8-G	76.01	92.83	7.16	yaml	weights
MobileNet_v1_025	D910x8-G	53.87	77.66	0.47	yaml	weights
MobileNet_v1_050	D910x8-G	65.94	86.51	1.34	yaml	weights
MobileNet_v1_075	D910x8-G	70.44	89.49	2.60	yaml	weights
MobileNet_v1_100	D910x8-G	72.95	91.01	4.25	yaml	weights
MobileNet_v2_075	D910x8-G	69.76	89.28	2.66	yaml	weights
MobileNet_v2_100	D910x8-G	72.02	90.92	3.54	yaml	weights
MobileNet_v2_140	D910x8-G	74.97	92.32	6.15	yaml	weights
MobileNetV3_small_100	D910x8-G	67.81	87.82	2.55	yaml	weights
MobileNetV3_large_100	D910x8-G	75.14	92.33	5.51	yaml	weights
nasnet_a_4x1056	D910x8-G	73.65	91.25	5.33	yaml	weights
PiT_xs	D910x8-G	78.41	94.06	10.61	yaml	weights
poolformer_s12	D910x8-G	77.33	93.34	11.92	yaml	weights
PVT_tiny	D910x8-G	74.81	92.18	13.23	yaml	weights
PVT_small	D910x8-G	79.66	94.71	24.49	yaml	weights
PVT_medium	D910x8-G	81.82	95.81	44.21	yaml	weights
PVT_large	D910x8-G	81.75	95.70	61.36	yaml	weights
PVTV2_b0	D910x8-G	71.50	90.60	3.67	yaml	weights
PVTV2_b1	D910x8-G	78.91	94.49	14.01	yaml	weights
PVTV2_b2	D910x8-G	81.99	95.74	25.35	yaml	weights
regnet_x_800mf	D910x8-G	76.04	92.97	7.26	yaml	weights
repmlp_t224	D910x8-G	76.68	93.30	38.30	yaml	weights
repvgg_a0	D910x8-G	72.19	90.75	9.13	yaml	weights
repvgg_a1	D910x8-G	74.19	91.89	14.12	yaml	weights
repvgg_a2	D910x8-G	76.63	93.42	28.25	yaml	weights
repvgg_b0	D910x8-G	74.99	92.40	15.85	yaml	weights
repvgg_b1	D910x8-G	78.81	94.37	57.48	yaml	weights
repvgg_b2	D910x64-G	79.29	94.66	89.11	yaml	weights
repvgg_b3	D910x64-G	80.46	95.34	123.19	yaml	weights
Res2Net50	D910x8-G	79.35	94.64	25.76	yaml	weights
Res2Net101	D910x8-G	79.56	94.70	45.33	yaml	weights
Res2Net50-v1b	D910x8-G	80.32	95.09	25.77	yaml	weights
Res2Net101-v1b	D910x8-G	81.26	95.41	45.35	yaml	weights
ResNeSt50	D910x8-G	80.81	95.16	27.55	yaml	weights
ResNet18	D910x8-G	70.31	89.62	11.70	yaml	weights
ResNet34	D910x8-G	74.15	91.98	21.81	yaml	weights
ResNet50	D910x8-G	76.69	93.50	25.61	yaml	weights
ResNet101	D910x8-G	78.24	94.09	44.65	yaml	weights
ResNet152	D910x8-G	78.72	94.45	60.34	yaml	weights
ResNetv2_50	D910x8-G	76.90	93.37	25.60	yaml	weights
ResNetv2_101	D910x8-G	78.48	94.23	44.55	yaml	weights
ResNeXt50_32x4d	D910x8-G	78.53	94.10	25.10	yaml	weights
ResNeXt101_32x4d	D910x8-G	79.83	94.80	44.32	yaml	weights
ResNeXt101_64x4d	D910x8-G	80.30	94.82	83.66	yaml	weights
ResNeXt152_64x4d	D910x8-G	80.52	95.00	115.27	yaml	weights
rexnet_x09	D910x8-G	77.07	93.41	4.13	yaml	weights
rexnet_x10	D910x8-G	77.38	93.60	4.84	yaml	weights
rexnet_x13	D910x8-G	79.06	94.28	7.61	yaml	weights
rexnet_x15	D910x8-G	79.94	94.74	9.79	yaml	weights
rexnet_x20	D910x8-G	80.64	94.99	16.45	yaml	weights
SEResNet18	D910x8-G	71.81	90.49	11.80	yaml	weights
SEResNet34	D910x8-G	75.38	92.50	21.98	yaml	weights
SEResNet50	D910x8-G	78.32	94.07	28.14	yaml	weights
SEResNeXt26_32x4d	D910x8-G	77.17	93.42	16.83	yaml	weights
SEResNeXt50_32x4d	D910x8-G	78.71	94.36	27.63	yaml	weights
shufflenet_v1_g3_x0_5	D910x8-G	57.05	79.73	0.73	yaml	weights
shufflenet_v1_g3_x1_0	D910x8-G	67.77	87.73	1.89	yaml	weights
shufflenet_v1_g3_x1_5	D910x8-G	71.53	90.17	3.48	yaml	weights
shufflenet_v1_g3_x2_0	D910x8-G	74.02	91.74	5.50	yaml	weights
shufflenet_v2_x0_5	D910x8-G	60.68	82.44	1.37	yaml	weights
shufflenet_v2_x1_0	D910x8-G	69.51	88.67	2.29	yaml	weights
shufflenet_v2_x1_5	D910x8-G	72.59	90.79	3.53	yaml	weights
shufflenet_v2_x2_0	D910x8-G	75.14	92.13	7.44	yaml	weights
skresnet18	D910x8-G	73.09	91.20	11.97	yaml	weights
skresnet34	D910x8-G	76.80	93.10	22.31	yaml	weights
skresnet50_32x4d	D910x8-G	79.08	94.60	37.31	yaml	weights
squeezenet_1.0	D910x8-G	59.01	81.01	1.25	yaml	weights
squeezenet_1.0	GPUx8-G	59.49	81.22	1.25	yaml	weights
squeezenet_1.1	D910x8-G	58.44	80.84	1.24	yaml	weights
squeezenet_1.1	GPUx8-G	58.99	80.99	1.24	yaml	weights
swin_tiny	D910x8-G	80.82	94.80	33.38	yaml	weights
vgg11	D910x8-G	72.00	90.50	132.86	yaml	weights
vgg13	D910x8-G	72.75	91.03	133.04	yaml	weights
vgg16	D910x8-G	74.53	92.05	138.35	yaml	weights
vgg19	D910x8-G	75.20	92.52	143.66	yaml	weights
visformer_tiny	D910x8-G	78.28	94.15	10.33	yaml	weights
visformer_tiny_v2	D910x8-G	78.82	94.41	9.38	yaml	weights
visformer_small	D910x8-G	81.73	95.88	40.25	yaml	weights
visformer_small_v2	D910x8-G	82.17	95.90	23.52	yaml	weights
vit_b_32_224	D910x8-G	75.86	92.08	87.46	yaml	weights
vit_l_16_224	D910x8-G	76.34	92.79	303.31	yaml	weights
vit_l_32_224	D910x8-G	73.71	90.92	305.52	yaml	weights
Xception	D910x8-G	79.01	94.25	22.91	yaml	weights
xcit_tiny_12_p16	D910x8-G	77.67	93.79	7.00	yaml	weights

Notes

Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode.
Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K.

MindSpore Lab / mindcv-1

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