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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021powerequalisation-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.979
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.970
3	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.968
4	vit_large_patch14_clip_224:openai_ft_in1k	.964
5	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.964
6	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.963
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.963
8	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.959
9	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.959
10	convnext_large:fb_in22k_ft_in1k	.948
11	vit_large_patch14_clip_336:laion2b_ft_in1k	.945
12	convnext_xlarge:fb_in22k_ft_in1k	.943
13	deit_large_imagenet_full_seed-0	.943
14	vit_large_patch14_clip_224:laion2b_ft_in1k	.939
15	convnext_large_imagenet_full_seed-0	.938
16	convnext_base:clip_laiona_augreg_ft_in1k_384	.936
17	convnext_small_imagenet_full_seed-0	.934
18	convnext_base_imagenet_full_seed-0	.934
19	deit_base_imagenet_full_seed-0	.929
20	convnext_tiny:in12k_ft_in1k	.927
21	effnetv2m_custom384	.923
22	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.914
23	resnext101_32x32d_wsl	.911
24	vit_relpos_base_patch16_clsgap_224:sw_in1k	.911
25	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.909
26	fixres_resnext101_32x48d_wsl	.907
27	cv_18_dagger_408_pretrained	.905
28	focalnet_tiny_lrf_in1k	.905
29	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.902
30	resnext101_32x16d_wsl	.902
31	resnext101_32x48d_wsl	.900
32	vit_base_patch16_clip_224:openai_ft_in1k	.896
33	resnext101_32x8d_wsl	.896
34	convnext_tiny_imagenet_full_seed-0	.893
35	AdvProp_efficientnet-b7	.891
36	AdvProp_efficientnet-b6	.875
37	convnext_tiny_sup	.875
38	cvt_cvt-13-224-in1k_4_LucyV4	.871
39	deit_small_imagenet_full_seed-0	.868
40	nasnet_large-tf	.864
41	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.863
42	resnet_101_v2	.854
43	resnet-152_v2_pytorch	.850
44	cvt_cvt-21-384-in1k_4_LucyV4	.850
45	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.845
46	cvt_cvt-21-224-in1k_4_LucyV4	.841
47	nasnet_large	.841
48	convnext_femto_ols:d1_in1k	.836
49	AdvProp_efficientnet-b4	.834
50	AT_efficientnet-b4	.834
51	densenet-201	.832
52	densenet_201_pytorch	.832
53	densenet-201-keras	.832
54	inception_v4-tf	.830
55	densenet-169	.825
56	densenet-169-keras	.823
57	cvt_cvt-13-384-in1k_4_LucyV4	.823
58	resnet_50_v2	.823
59	efficientnet-b7	.820
60	pnasnet_large-tf	.814
61	resnet-101_v2-tf	.812
62	pnasnet_large	.812
63	inception_v4	.807
64	inception_v3-tf	.807
65	resnet50-SIN	.807
66	inception_v2-tf	.804
67	inception_v3_pytorch	.804
68	tv_efficientnet-b1	.804
69	resnet_152_v2	.804
70	AdvProp_efficientnet-b0	.802
71	xception-keras	.800
72	resnet50-SIN_IN	.798
73	antialiased-rnext101_32x8d	.798
74	ViT_L_32_imagenet1k	.796
75	AT_efficientnet-b2	.796
76	AdvProp_efficientnet-b2	.796
77	effnetb1_272x240	.793
78	antialias-resnet152	.793
79	resnet_152_v1	.793
80	inception_v3	.791
81	BiT-S-R152x2	.787
82	BiT-S-R50x3	.786
83	resnet-152_v2-tf	.786
84	grcnn_109	.786
85	BiT-S-R152x4	.780
86	resnet-50_v2-tf	.780
87	Res2Net50_26w_4s	.780
88	BiT-S-R101x1	.777
89	densenet-121-keras	.775
90	BiT-S-R101x3	.775
91	BiT-S-R50x1	.773
92	densenet-121	.773
93	resnet50-vitoimagevidnet8	.770
94	inception_v1-tf	.766
95	ViT_B_32_imagenet1k	.766
96	resnet_101_v1	.766
97	resnet101_imagenet_full	.757
98	resnet_SIN_IN_FT_IN	.755
99	resnet50-SIN_IN_IN	.755
100	resnet50-VITO-8deg-cc	.754
101	grcnn	.752
102	efficientnet_b0	.743
103	resnet-152_v1-tf	.738
104	vonegrcnn_47e	.736
105	resnet50_tutorial	.732
106	resnet_50_v1	.732
107	resnet-50-pytorch	.732
108	resnet50-sup	.732
109	vonegrcnn_47e	.730
110	resnet50-moclr8deg	.730
111	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.730
112	antialiased-r50	.729
113	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.727
114	AdvProp_efficientnet-b8	.725
115	efficientnet_b2_imagenet_full	.720
116	vonegrcnn_62e_nobn	.720
117	resnet-50_v1-tf	.720
118	resnet50_imagenet_full	.718
119	resnet152_imagenet_full	.716
120	resnet50	.711
121	vonegrcnn_52e_full	.709
122	resnet-101_v1-tf	.709
123	vonegrcnn_62e_nobn	.707
124	CORnet-S	.707
125	xception	.704
126	resnet-34	.698
127	resnet_50_v1_spiking	.698
128	CLIP-RN50	.696
129	resnet50_moco_v2	.696
130	resnet50_robust_l2_eps1	.696
131	voneresnet-50-non_stochastic	.695
132	vgg-19-keras	.691
133	resnet34_imagenet_full	.691
134	efficientnet_b1_imagenet_full	.691
135	vonegrcnn_52e_full	.686
136	inception_v1	.684
137	mobilenet_v2_1.4_224-tf	.682
138	mobilenet_v2_1.3_224-tf	.679
139	voneresnet-50-robust	.679
140	mobilenet_v2_1_3_224	.679
141	ReAlnet06	.673
142	AlexNet_SIN	.671
143	nasnet_mobile	.670
144	mobilenet_v2_1.0_224-tf	.664
145	vgg-16-keras	.661
146	efficientnet_b0_imagenet_full	.657
147	pnasnet_large_pytorch	.657
148	voneresnet-50	.652
149	vgg_16	.652
150	vgg_19	.648
151	ReAlnet04	.648
152	ReAlnet01	.645
153	mobilenet_v2_0.75_192-tf	.641
154	mobilenet_v2_1_0_224	.639
155	mobilenet_v2_1.0_192-tf	.637
156	mobilenet_v1_1.0_192	.637
157	regnet_y_400mf	.637
158	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.636
159	resnet18_imagenet_full	.632
160	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.632
161	resnet-18	.630
162	mobilenet_v2_1_0_192	.630
163	ReAlnet09	.627
164	mobilenet_v2_0.75_224-tf	.627
165	mobilenet_v2_0_75_192	.625
166	blt_vs	.625
167	ReAlnet03	.625
168	custom_model_cv_18_dagger_408	.623
169	ReAlnet08	.621
170	ReAlnet05	.621
171	mobilenet_v2_1.0_160-tf	.620
172	mobilenet_v1_1.0_224	.616
173	ReAlnet07	.613
174	ReAlnet10	.611
175	ReAlnet02	.609
176	nasnet_mobile-tf	.609
177	resnet-50-robust	.609
178	imagenet_l2_3_0	.609
179	resnet50_robust_l2_eps3	.609
180	mobilenet_v2_0_75_224	.605
181	mobilenet_v2_1_0_160	.604
182	shufflenet_v2_x1_0	.598
183	resnet50_eMMCR_Vanilla	.598
184	mobilenet_v2_1-4_224_pytorch	.596
185	mobilenet_v2_1_4_224	.596
186	mobilenet_v1_0.75_224	.596
187	mobilenet_v2_1.0_128-tf	.586
188	mobilenet_v1_1.0_160	.571
189	mobilenet_v2_1_0_128	.570
190	resnet50_l2_3_robust	.570
191	mobilenet_v2_0_5_224	.559
192	resnet_50_v1_spiking_l4a	.554
193	convnext_small_imagenet_100_seed-0	.554
194	mobilenet_v2_0_75_160	.552
195	mobilenet_v2_0.5_224-tf	.548
196	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.541
197	mobilenet_v1_0.75_192	.539
198	mobilenet_v1_0.75_160	.539
199	mobilenet_v1_1.0_128	.537
200	mobilenet_v2_0.75_160-tf	.537
201	resnet50_linf_8_robust	.536
202	mobilenet_v2_0_5_192	.532
203	mobilenet_v2_0.75_128	.532
204	alexnet_random_linf8_perturb	.532
205	alexnet	.532
206	mobilenet_v1_0.5_224	.530
207	mobilenet_v2_0.5_160	.523
208	mobilenet_v2_0.5_192-tf	.521
209	alexnet-baseline	.505
210	alexnet	.505
211	alexnet	.505
212	alexnet	.505
213	alexnet	.505
214	alexnet	.505
215	alexnet	.505
216	alexnet	.505
217	alexnet_ks_torevert	.505
218	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.504
219	squeezenet1_1	.498
220	mobilenet_v1_0.5_160	.498
221	alexnet_early_checkpoint	.498
222	mobilenet_v2_0.35_192	.496
223	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.495
224	mobilenet_v1_0.5_192	.495
225	mobilenet_v2_1.0_96	.491
226	mobilenet_v1_0.75_128	.484
227	mobilenet_v2_0.5_128	.482
228	mobilenet_v2_0.35_224	.461
229	squeezenet1_0	.457
230	mobilenet_v1_0.5_128	.457
231	mobilenet_v2_0.35_160	.454
232	mobilenet_v2_0.75_96	.445
233	imagenet_l2_10_0	.425
234	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.412
235	0.5x_resnet-18_LC_w_sh_1_iter	.409
236	mobilenet_v2_0.35_128	.396
237	mobilenet_v2_0.5_96	.366
238	mobilenet_v1_0.25_224	.366
239	alexnet_robust_correct	.362
240	mobilenet_v2_0.35_96	.354
241	mobilenet_v1_0.25_160	.339
242	alexnet_l2_3_robust	.327
243	mobilenet_v1_0.25_192	.316
244	alexnet_linf_8_robust	.296
245	mobilenet_v1_0.25_128	.284
246	deit_small_imagenet_100_seed-0	.282
247	resnet_50_v1_spiking_l4	.279
248	resnet50_imagenet_100_seed-0	.266
249	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
250	resnet50-vicregl0p9	.077
251	convnext_small_imagenet_10_seed-0	.075
252	resnet50_imagenet_10_seed-0	.073
253	resnet_50_v1_spiking_l3	.073
254	deit_small_imagenet_10_seed-0	.066
255	resnet50_imagenet_1_seed-0	.066
256	alexnet_training_seed_09	.064
257	convnext_small_imagenet_1_seed-0	.064
258	resnet50-vicregl0p75	.064
259	resnet-50_untrained	.062
260	resnet50-meshes-lt-100-original-pretrained	.062
261	alexnet_training_seed_05	.062
262	mobilevit_small	.062
263	alexnet_training_seed_02	.062
264	alexnet_training_seed_04	.062
265	alexnet_training_seed_08	.062
266	alexnet_training_seed_10	.062
267	alexnet_training_seed_01	.062
268	deit_small_imagenet_1_seed-0	.062
269	alexnet_training_seed_03	.062
270	alexnet_training_seed_06	.059
271	alexnet_training_seed_07	.057
272	resnet50-meshes-lt-100-original-scratch	.057
273	resnet50-barlow	.052
274	artResNet18_1	.048
275	barlow-twins-resnet50	.048
276	resnet50-simclr-vissl	.039
277	ViT-B/32
278	resnet-18-LC_w_sh_100_iter_conv_init_m
279	BiT-M-R50x1
280	resnet-18-LC_w_sh_1_iter_conv_init_m
281	BiT-M-R152x2
282	resnet-18-LC_w_sh_1_iter_m
283	BiT-M-R50x3
284	resnet-18-LC_w_sh_10_iter_m
285	resnet-18-LC_w_sh_100_iter_m
286	resnet-18-LC_w_sh_10_iter_conv_init_m
287	FrankRobWobv0
288	0.5x_resnet-18_LC
289	CORnetZ_CIFAR10
290	resnet18-local_aggregation
291	r3m_resnet18
292	resnet18-deepcluster
293	CORnet-Z
294	vggface
295	resnet18-instance_recognition
296	my-model
297	0.5x_resnet-18_LC_w_sh_10_iter
298	r3m_resnet50
299	r3m_resnet34
300	resnet-18-LC_m
301	resnet18-supervised
302	r3m_resnet50_nocrop
303	resnet-18-LC_1st_conv_m
304	resnet-18-LC_conv_init_m
305	resnet18-contrastive_multiview
306	0.5x_resnet-18
307	0.5x_resnet-18_LC_w_sh_100_iter
308	RN50
309	RN50
310	resnet18-colorization
311	resnet18-relative_position
312	resnet18-autoencoder
313	resnet18-simclr
314	ViT_L_32
315	ViT_B_32
316	resnet18-depth_prediction
317	ViT_B_16
318	r3m_resnet34_nocrop
319	resnet18-contrastive_predictive
320	resnet-18-LC_untrained
321	resnet-18_untrained
322	resnet-50x2_untrained
323	dcgan
324	unet_entire
325	CORnetZ_CIFAR10_bs32_20_04
326	v1-pyr-nodown
327	pixels
328	dorinet_cornet_z
329	omnivore_swinT
330	resnet50
331	openclip
332	vonealexnet_gaussian_noise_std4_fixed
333	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
334	ReAlnet08_ALL
335	yudixie_resnet50_imagenet1kpret_0_240312
336	yudixie_resnet50_category_class_0_240908
337	yudixie_resnet50_distance_reg_0_240908
338	resnet18_imagenet21kP
339	ConvLSTM
340	PredRNN
341	SimVP
342	TAU
343	yolos_tiny
344	MIM
345	hmax
346	ReAlnet01_cornet
347	yudixie_resnet18_distance_translation_0_240719
348	briaai_rmbg_1_4
349	ReAlnet03_cornet
350	yudixie_resnet18_distance_reg_0_240719
351	yudixie_resnet18_rotation_reg_0_240719
352	yudixie_resnet18_imagenet1kpret_0_240719
353	ReAlnet02_cornet
354	yudixie_resnet50_rotation_reg_0_240908
355	yudixie_resnet50_distance_rotation_0_240908
356	yudixie_resnet50_distance_translation_0_240908
357	resnet18_ecoset_full
358	VOneCORnet-S
359	ReAlnet04_cornet
360	resnet50_ecoset_full
361	resnet101_ecoset_full
362	resnet152_ecoset_full
363	yudixie_resnet18_translation_rotation_0_240719
364	yudixie_resnet18_distance_rotation_0_240719
365	resnet-18_test_m
366	yudixie_resnet50_translation_reg_0_240908
367	yudixie_resnet18_category_class_0_240719
368	ReAlnet05_cornet
369	yudixie_resnet18_random_0_240719
370	yudixie_resnet18_cat_obj_class_all_latents_0_240719
371	yudixie_resnet18_translation_reg_0_240719
372	yudixie_resnet18_distance_translation_rotation_0_240719
373	yudixie_resnet18_object_class_0_240719
374	SeLaVi-AVE
375	ReAlnet06_cornet
376	r3d_18
377	yudixie_resnet50_translation_rotation_0_240908
378	ReAlnet01_ALL
379	ReAlnet07_cornet
380	mc3_18
381	ReAlnet02_ALL
382	s3d
383	omnivore_swinB
384	ReAlnet08_cornet
385	resnet50-vicreg
386	mvit_v1_b
387	ReAlnet04_ALL
388	ReAlnet03_ALL
389	ReAlnet09_cornet
390	omnivore_swinS
391	ReAlnet05_ALL
392	SeLaVi-Kinetics400
393	SeLaVi-VGG-Sound
394	ReAlnet06_ALL
395	SeLaVi-Kinetics-Sound
396	yudixie_resnet50_distance_translation_rotation_0_240908
397	yudixie_resnet50_random_0_240908
398	r2plus1d_18
399	mvit_v2_s
400	yudixie_resnet50_imagenet1kpret_0_240908
401	ReAlnet07_ALL
402	resnet34_ecoset_full
403	yudixie_resnet50_cat_obj_class_all_latents_0_240908
404	ReAlnet09_ALL
405	yudixie_resnet50_object_class_0_240908
406	ReAlnet10_cornet
407	resnet50_primary_visual_cortex
408	ReAlnet10_ALL

Benchmark bibtex

@article{geirhos2021partial,
              title={Partial success in closing the gap between human and machine vision},
              author={Geirhos, Robert and Narayanappa, Kantharaju and Mitzkus, Benjamin and Thieringer, Tizian and Bethge, Matthias and Wichmann, Felix A and Brendel, Wieland},
              journal={Advances in Neural Information Processing Systems},
              volume={34},
              year={2021},
              url={https://openreview.net/forum?id=QkljT4mrfs}
        }

Ceiling

1.00.

Note that scores are relative to this ceiling.

Data: Geirhos2021powerequalisation

Metric: top1