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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	resnet50_ewconplus_wzc	.766
97	resnet_101_v1	.766
98	resnet101_imagenet_full	.757
99	resnet_SIN_IN_FT_IN	.755
100	resnet50-SIN_IN_IN	.755
101	resnet50-VITO-8deg-cc	.754
102	grcnn	.752
103	efficientnet_b0	.743
104	resnet-152_v1-tf	.738
105	vonegrcnn_47e	.736
106	resnet50_tutorial	.732
107	resnet_50_v1	.732
108	resnet-50-pytorch	.732
109	resnet50-sup	.732
110	vonegrcnn_47e	.730
111	resnet50-moclr8deg	.730
112	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.730
113	antialiased-r50	.729
114	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.727
115	AdvProp_efficientnet-b8	.725
116	efficientnet_b2_imagenet_full	.720
117	vonegrcnn_62e_nobn	.720
118	resnet-50_v1-tf	.720
119	resnet50_imagenet_full	.718
120	resnet152_imagenet_full	.716
121	resnet50	.711
122	vonegrcnn_52e_full	.709
123	resnet-101_v1-tf	.709
124	vonegrcnn_62e_nobn	.707
125	CORnet-S	.707
126	xception	.704
127	resnet-34	.698
128	resnet_50_v1_spiking	.698
129	CLIP-RN50	.696
130	resnet50_moco_v2	.696
131	resnet50_robust_l2_eps1	.696
132	voneresnet-50-non_stochastic	.695
133	vgg-19-keras	.691
134	resnet34_imagenet_full	.691
135	efficientnet_b1_imagenet_full	.691
136	vonegrcnn_52e_full	.686
137	inception_v1	.684
138	mobilenet_v2_1.4_224-tf	.682
139	mobilenet_v2_1.3_224-tf	.679
140	voneresnet-50-robust	.679
141	mobilenet_v2_1_3_224	.679
142	ReAlnet06	.673
143	AlexNet_SIN	.671
144	AlexNet_SIN_fov4	.671
145	AlexNet_SIN_fov12	.671
146	AlexNet_SIN_fov16	.671
147	nasnet_mobile	.670
148	mobilenet_v2_1.0_224-tf	.664
149	vgg-16-keras	.661
150	efficientnet_b0_imagenet_full	.657
151	pnasnet_large_pytorch	.657
152	voneresnet-50	.652
153	vgg_16	.652
154	vgg_19	.648
155	ReAlnet04	.648
156	ReAlnet01	.645
157	mobilenet_v2_0.75_192-tf	.641
158	mobilenet_v2_1_0_224	.639
159	mobilenet_v2_1.0_192-tf	.637
160	mobilenet_v1_1.0_192	.637
161	regnet_y_400mf	.637
162	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.636
163	resnet18_imagenet_full	.632
164	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.632
165	resnet-18	.630
166	mobilenet_v2_1_0_192	.630
167	ReAlnet09	.627
168	mobilenet_v2_0.75_224-tf	.627
169	mobilenet_v2_0_75_192	.625
170	blt_vs	.625
171	ReAlnet03	.625
172	custom_model_cv_18_dagger_408	.623
173	ReAlnet08	.621
174	ReAlnet05	.621
175	mobilenet_v2_1.0_160-tf	.620
176	mobilenet_v1_1.0_224	.616
177	ReAlnet07	.613
178	ReAlnet10	.611
179	ReAlnet02	.609
180	nasnet_mobile-tf	.609
181	resnet-50-robust	.609
182	imagenet_l2_3_0	.609
183	resnet50_robust_l2_eps3	.609
184	mobilenet_v2_0_75_224	.605
185	mobilenet_v2_1_0_160	.604
186	shufflenet_v2_x1_0	.598
187	resnet50_eMMCR_Vanilla	.598
188	mobilenet_v2_1-4_224_pytorch	.596
189	mobilenet_v2_1_4_224	.596
190	mobilenet_v1_0.75_224	.596
191	mobilenet_v2_1.0_128-tf	.586
192	mobilenet_v1_1.0_160	.571
193	mobilenet_v2_1_0_128	.570
194	resnet50_l2_3_robust	.570
195	mobilenet_v2_0_5_224	.559
196	resnet_50_v1_spiking_l4a	.554
197	convnext_small_imagenet_100_seed-0	.554
198	mobilenet_v2_0_75_160	.552
199	mobilenet_v2_0.5_224-tf	.548
200	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.541
201	mobilenet_v1_0.75_192	.539
202	mobilenet_v1_0.75_160	.539
203	mobilenet_v1_1.0_128	.537
204	mobilenet_v2_0.75_160-tf	.537
205	resnet50_linf_8_robust	.536
206	mobilenet_v2_0_5_192	.532
207	mobilenet_v2_0.75_128	.532
208	alexnet_random_linf8_perturb	.532
209	alexnet	.532
210	mobilenet_v1_0.5_224	.530
211	mobilenet_v2_0.5_160	.523
212	mobilenet_v2_0.5_192-tf	.521
213	alexnet-baseline	.505
214	alexnet	.505
215	alexnet	.505
216	alexnet	.505
217	alexnet	.505
218	alexnet	.505
219	alexnet	.505
220	alexnet	.505
221	alexnet	.505
222	alexnet_ks_torevert	.505
223	alexnet_fov4	.505
224	alexnet_fov12	.505
225	alexnet_fov16	.505
226	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.504
227	squeezenet1_1	.498
228	mobilenet_v1_0.5_160	.498
229	alexnet_early_checkpoint	.498
230	mobilenet_v2_0.35_192	.496
231	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.495
232	mobilenet_v1_0.5_192	.495
233	mobilenet_v2_1.0_96	.491
234	mobilenet_v1_0.75_128	.484
235	mobilenet_v2_0.5_128	.482
236	mobilenet_v2_0.35_224	.461
237	squeezenet1_0	.457
238	mobilenet_v1_0.5_128	.457
239	mobilenet_v2_0.35_160	.454
240	mobilenet_v2_0.75_96	.445
241	imagenet_l2_10_0	.425
242	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.412
243	0.5x_resnet-18_LC_w_sh_1_iter	.409
244	mobilenet_v2_0.35_128	.396
245	mobilenet_v2_0.5_96	.366
246	mobilenet_v1_0.25_224	.366
247	alexnet_robust_correct	.362
248	mobilenet_v2_0.35_96	.354
249	mobilenet_v1_0.25_160	.339
250	alexnet_l2_3_robust	.327
251	mobilenet_v1_0.25_192	.316
252	alexnet_linf_8_robust	.296
253	mobilenet_v1_0.25_128	.284
254	deit_small_imagenet_100_seed-0	.282
255	resnet_50_v1_spiking_l4	.279
256	resnet50_imagenet_100_seed-0	.266
257	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
258	resnet50-vicregl0p9	.077
259	convnext_small_imagenet_10_seed-0	.075
260	resnet50_imagenet_10_seed-0	.073
261	resnet_50_v1_spiking_l3	.073
262	deit_small_imagenet_10_seed-0	.066
263	resnet50_imagenet_1_seed-0	.066
264	alexnet_training_seed_09	.064
265	convnext_small_imagenet_1_seed-0	.064
266	resnet50-vicregl0p75	.064
267	resnet-50_untrained	.062
268	resnet50-meshes-lt-100-original-pretrained	.062
269	alexnet_training_seed_05	.062
270	mobilevit_small	.062
271	alexnet_training_seed_02	.062
272	alexnet_training_seed_04	.062
273	alexnet_training_seed_08	.062
274	alexnet_training_seed_10	.062
275	alexnet_training_seed_01	.062
276	deit_small_imagenet_1_seed-0	.062
277	alexnet_training_seed_01_fov4	.062
278	alexnet_training_seed_01_fov12	.062
279	alexnet_training_seed_01_fov16	.062
280	alexnet_training_seed_03	.062
281	alexnet_training_seed_06	.059
282	alexnet_training_seed_07	.057
283	resnet50-meshes-lt-100-original-scratch	.057
284	resnet50-barlow	.052
285	artResNet18_1	.048
286	barlow-twins-resnet50	.048
287	resnet50-simclr-vissl	.039
288	ViT-B/32
289	resnet-18-LC_w_sh_100_iter_conv_init_m
290	BiT-M-R50x1
291	resnet-18-LC_w_sh_1_iter_conv_init_m
292	BiT-M-R152x2
293	resnet-18-LC_w_sh_1_iter_m
294	BiT-M-R50x3
295	resnet-18-LC_w_sh_10_iter_m
296	resnet-18-LC_w_sh_100_iter_m
297	resnet-18-LC_w_sh_10_iter_conv_init_m
298	FrankRobWobv0
299	0.5x_resnet-18_LC
300	CORnetZ_CIFAR10
301	resnet18-local_aggregation
302	r3m_resnet18
303	resnet18-deepcluster
304	CORnet-Z
305	vggface
306	resnet18-instance_recognition
307	my-model
308	0.5x_resnet-18_LC_w_sh_10_iter
309	r3m_resnet50
310	r3m_resnet34
311	resnet-18-LC_m
312	resnet18-supervised
313	r3m_resnet50_nocrop
314	resnet-18-LC_1st_conv_m
315	resnet-18-LC_conv_init_m
316	resnet18-contrastive_multiview
317	0.5x_resnet-18
318	0.5x_resnet-18_LC_w_sh_100_iter
319	RN50
320	RN50
321	resnet18-colorization
322	resnet18-relative_position
323	resnet18-autoencoder
324	resnet18-simclr
325	ViT_L_32
326	ViT_B_32
327	resnet18-depth_prediction
328	ViT_B_16
329	r3m_resnet34_nocrop
330	resnet18-contrastive_predictive
331	resnet-18-LC_untrained
332	resnet-18_untrained
333	resnet-50x2_untrained
334	dcgan
335	unet_entire
336	CORnetZ_CIFAR10_bs32_20_04
337	v1-pyr-nodown
338	pixels
339	dorinet_cornet_z
340	omnivore_swinT
341	resnet50
342	openclip
343	vonealexnet_gaussian_noise_std4_fixed
344	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
345	ReAlnet08_ALL
346	yudixie_resnet50_imagenet1kpret_0_240312
347	yudixie_resnet50_category_class_0_240908
348	yudixie_resnet50_distance_reg_0_240908
349	resnet18_imagenet21kP
350	ConvLSTM
351	PredRNN
352	SimVP
353	TAU
354	yolos_tiny
355	MIM
356	hmax
357	ReAlnet01_cornet
358	yudixie_resnet18_distance_translation_0_240719
359	briaai_rmbg_1_4
360	ReAlnet03_cornet
361	yudixie_resnet18_distance_reg_0_240719
362	yudixie_resnet18_rotation_reg_0_240719
363	yudixie_resnet18_imagenet1kpret_0_240719
364	compact_resnet50_V4
365	ReAlnet02_cornet
366	yudixie_resnet50_rotation_reg_0_240908
367	yudixie_resnet50_distance_rotation_0_240908
368	yudixie_resnet50_distance_translation_0_240908
369	compact_resnet50_IT
370	resnet18_ecoset_full
371	VOneCORnet-S
372	ReAlnet01_cornet_fov4
373	ReAlnet04_cornet
374	resnet50_ecoset_full
375	resnet101_ecoset_full
376	ReAlnet01_cornet_fov12
377	resnet152_ecoset_full
378	compact_vgg19_V4
379	yudixie_resnet18_translation_rotation_0_240719
380	yudixie_resnet18_distance_rotation_0_240719
381	resnet-18_test_m
382	yudixie_resnet50_translation_reg_0_240908
383	yudixie_resnet18_category_class_0_240719
384	ReAlnet05_cornet
385	yudixie_resnet18_random_0_240719
386	yudixie_resnet18_cat_obj_class_all_latents_0_240719
387	yudixie_resnet18_translation_reg_0_240719
388	compact_vgg19_IT
389	yudixie_resnet18_distance_translation_rotation_0_240719
390	yudixie_resnet18_object_class_0_240719
391	SeLaVi-AVE
392	ReAlnet06_cornet
393	compact_resnet50_robust_V4
394	r3d_18
395	ReAlnet01_cornet_fov16
396	yudixie_resnet50_translation_rotation_0_240908
397	ReAlnet01_ALL
398	ReAlnet07_cornet
399	mc3_18
400	ReAlnet02_ALL
401	s3d
402	omnivore_swinB
403	ReAlnet08_cornet
404	resnet50-vicreg
405	mvit_v1_b
406	ReAlnet04_ALL
407	compact_cornets_V4
408	ReAlnet03_ALL
409	compact_cornets_IT
410	ReAlnet09_cornet
411	omnivore_swinS
412	ReAlnet05_ALL
413	SeLaVi-Kinetics400
414	SeLaVi-VGG-Sound
415	ReAlnet06_ALL
416	SeLaVi-Kinetics-Sound
417	compact_resnet50_robust_IT
418	yudixie_resnet50_distance_translation_rotation_0_240908
419	yudixie_resnet50_random_0_240908
420	r2plus1d_18
421	mvit_v2_s
422	yudixie_resnet50_imagenet1kpret_0_240908
423	ReAlnet07_ALL
424	resnet34_ecoset_full
425	yudixie_resnet50_cat_obj_class_all_latents_0_240908
426	ReAlnet09_ALL
427	yudixie_resnet50_object_class_0_240908
428	ReAlnet10_cornet
429	resnet50_primary_visual_cortex
430	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