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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021rotation-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.646
2	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.614
3	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.606
4	resnet-152_v2_pytorch	.589
5	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.581
6	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.571
7	fixres_resnext101_32x48d_wsl	.568
8	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.565
9	resnet_152_v2	.561
10	vit_large_patch14_clip_336:laion2b_ft_in1k	.559
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.546
12	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.539
13	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.528
14	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.527
15	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.516
16	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.506
17	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.500
18	effnetv2m_custom384	.489
19	vit_large_patch14_clip_224:openai_ft_in1k	.488
20	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.478
21	convnext_base:clip_laiona_augreg_ft_in1k_384	.476
22	convnext_large:fb_in22k_ft_in1k	.467
23	vit_large_patch14_clip_224:laion2b_ft_in1k	.457
24	convnext_base_fb_in22k_ft_in1k	.434
25	pnasnet_large_halfPrecision	.431
26	pnasnet_large	.431
27	vit_base_patch16_clip_224:openai_ft_in1k	.429
28	custom_model_cv_18_dagger_408	.425
29	convnext_xlarge:fb_in22k_ft_in1k	.417
30	resnet_101_v2	.416
31	resnet_50_v2	.413
32	resnext101_32x48d_wsl	.405
33	CLIP_resnet50	.404
34	CLIP_resnet50_float32	.404
35	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.404
36	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.400
37	resnext101_32x16d_wsl	.391
38	resnext101_32x32d_wsl	.385
39	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.380
40	SWSL_resnet50	.370
41	SWSL_resnext101_32x8d	.368
42	CLIP-RN50	.367
43	pnasnet_large-tf	.357
44	effnetb1_272x240	.356
45	resnext101_32x8d_wsl	.351
46	nasnet_large	.344
47	cvt_cvt-13-384-in1k_4_LucyV4	.343
48	cvt_cvt-21-384-in1k_4_LucyV4	.338
49	convnext_tiny:in12k_ft_in1k	.334
50	nasnet_large-tf	.325
51	vision_transformer_vit_large_patch16_224	.308
52	antialias-resnet152	.282
53	inception_v3	.273
54	CLIP_ViT-B_32	.273
55	focalnet_tiny_lrf_in1k	.273
56	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.268
57	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.268
58	inception_v4	.266
59	inception_v3-tf	.264
60	resnet50_st_wzc_seed4_2_323	.262
61	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.261
62	resnet152_imagenet_full	.259
63	convnext_femto_ols:d1_in1k	.255
64	resnet101	.255
65	resnet50-VITO-8deg-cc	.254
66	resnet50_st_wzc_noseed_2_323	.252
67	resnet50_st_wzc_seed5_2_323	.250
68	resnet101_imagenet_full	.250
69	vit_relpos_base_patch16_clsgap_224:sw_in1k	.249
70	resnet-152_v2-tf	.239
71	convnext_large_imagenet_full_seed-0	.236
72	resnet50_st_wzc_seed6_2_323	.236
73	resnet_152_v1	.236
74	resnet50_st_wzc_seed3_2_323	.232
75	deit3_base_patch16_224_fb_in1k	.228
76	cv_18_dagger_408_pretrained	.226
77	xception-keras	.225
78	antialiased-rnext101_32x8d	.225
79	convnext_tiny_imagenet_full_seed-0	.224
80	resnet50_random_l2_perturb	.223
81	tv_efficientnet-b1	.221
82	cvt_cvt-13-224-in1k_4_LucyV4	.218
83	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.213
84	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.212
85	resnet50_ewconplus_wzc_2_323	.206
86	convnext_base_imagenet_full_seed-0	.206
87	resnet50	.206
88	inception_v4-tf	.204
89	resnet50_imagenet_full	.204
90	deit_base_imagenet_full_seed-0	.201
91	convnext_small_imagenet_full_seed-0	.197
92	deit_large_imagenet_full_seed-0	.196
93	resnet_101_v1	.195
94	antialiased-r50	.195
95	grcnn_109	.194
96	resnet50_ewconplus_wzc_seed5_2__323	.193
97	resnet50-moclr8deg	.190
98	deit_small_imagenet_full_seed-0	.190
99	resnet50_ewconplus_wzc_seed2_2__323	.183
100	ViT_B_16_imagenet1k	.182
101	cvt_cvt-21-224-in1k_4_LucyV4	.181
102	densenet-201-keras	.181
103	xception	.179
104	Res2Net50_26w_4s	.177
105	resnet50_ewconplus_noseed_2_323	.177
106	pnasnet_large_pytorch	.175
107	convnext_base_fb_in1k	.174
108	densenet-201	.172
109	densenet_201_pytorch	.172
110	resnet_50_v1_spiking	.172
111	resnet50_ewconplus_wzc_seed7_2_323	.172
112	efficientnet_b2_imagenet_full	.167
113	vgg_19	.166
114	efficientnet_b0	.165
115	densenet-121-keras	.164
116	convnext_tiny_sup	.161
117	convnext_base_fb_in1k_gram_aligned_dinov3	.161
118	ViT_L_32_imagenet1k	.161
119	grcnn	.161
120	resnet34_imagenet_full	.160
121	resnet50_ewconplus_wzc	.160
122	resnet-50_v2-tf	.156
123	densenet-121	.155
124	resnet50_st_wzc_2	.155
125	resnet50_tutorial	.154
126	resnet_50_v1	.154
127	resnet-50-pytorch	.154
128	resnet50-sup	.154
129	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.153
130	mobilenet_v2_1.4_224-tf	.153
131	resnet50_random_linf8_perturb	.152
132	ViT_B_32_imagenet1k	.152
133	AdvProp_efficientnet-b6	.150
134	resnet50-SIN_IN_IN	.150
135	resnet_SIN_IN_FT_IN	.150
136	BiT-S-R50x3	.149
137	inception_v2-tf	.149
138	resnet-101_v1-tf	.149
139	resnet50_byol	.144
140	efficientnet-b7	.143
141	nasnet_mobile	.143
142	resnet50_ewconplus_wzc_seed4_2_323	.142
143	mobilenet_v2_1_0_224	.140
144	AT_efficientnet-b2	.137
145	AdvProp_efficientnet-b2	.137
146	alexnet_fov4	.136
147	resnet50-vitoimagevidnet8	.135
148	cornet_s	.133
149	BiT-S-R152x2	.132
150	densenet-169-keras	.131
151	voneresnet-50-non_stochastic	.130
152	BiT-S-R101x3	.130
153	mobilenet_v1_0.75_224	.130
154	mobilenet_v2_1.0_224-tf	.130
155	resnet18_imagenet_full	.130
156	efficientnet_b0_imagenet_full	.129
157	mobilenet_v2_1_3_224	.124
158	ReAlnet04	.124
159	AdvProp_efficientnet-b7	.123
160	inception_v1	.123
161	nasnet_mobile-tf	.121
162	ReAlnet03	.120
163	resnet50-SIN_IN	.119
164	resnet-50_v1-tf	.118
165	convnext_small_imagenet_100_seed-0	.117
166	ReAlnet10	.116
167	resnet_50_v1_spiking_l4a	.116
168	vgg_16	.116
169	AdvProp_efficientnet-b4	.115
170	AT_efficientnet-b4	.115
171	eBarlow_lmda_02_1000ep	.115
172	ReAlnet06	.115
173	ReAlnet01	.114
174	ReAlnet02	.114
175	ReAlnet09	.114
176	resnet-152_v1-tf	.114
177	densenet-169	.112
178	mobilenet_v2_1.3_224-tf	.109
179	resnet50_moco_v2	.105
180	BiT-S-R152x4	.105
181	efficientnet_b1_imagenet_full	.101
182	AlexNet_SIN_fov4	.101
183	mobilenet_v1_1.0_192	.100
184	inception_v1-tf	.098
185	ReAlnet05	.098
186	mobilenet_v2_1.0_160-tf	.096
187	vonegrcnn_47e	.096
188	ReAlnet07	.095
189	mobilenet_v2_1_0_192	.094
190	CORnet-S	.094
191	mobilenet_v1_1.0_224	.092
192	ReAlnet08	.092
193	mobilenet_v2_1_0_160	.091
194	mobilenet_v2_0_5_224	.091
195	resnet-34	.090
196	mobilenet_v2_0_75_192	.089
197	BiT-S-R50x1	.089
198	mobilenet_v2_0_75_160	.088
199	mobilenet_v2_1-4_224_pytorch	.087
200	mobilenet_v2_1_4_224	.087
201	resnet50_linf_4_robust	.085
202	squeezenet1_1	.085
203	BiT-S-R101x1	.085
204	vonegrcnn_62e_nobn	.085
205	regnet_y_400mf	.085
206	vgg-16-keras	.084
207	vonegrcnn_47e	.081
208	resnet50_simclr	.081
209	mobilenet_v2_0_75_224	.080
210	vonegrcnn_52e_full	.080
211	resnet-18	.079
212	vonegrcnn_52e_full	.079
213	mobilenet_v2_0_5_192	.079
214	AdvProp_efficientnet-b0	.078
215	vonegrcnn_62e_nobn	.075
216	shufflenet_v2_x1_0	.074
217	resnet50_l2_3_robust	.073
218	mobilenet_v1_0.5_192	.073
219	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.073
220	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.071
221	0.5x_resnet-18_LC_w_sh_1_iter	.070
222	mobilenet_v2_0.75_160-tf	.070
223	mobilenet_v2_1.0_192-tf	.069
224	texture_shape_alexnet_trained_on_SIN	.068
225	mobilenet_v2_0.75_224-tf	.067
226	mobilenet_v2_0.75_192-tf	.064
227	texture_shape_resnet50_trained_on_SIN	.064
228	mobilenet_v2_0.35_192	.064
229	mobilenet_v2_1_0_128	.063
230	mobilenet_v2_0.75_96	.063
231	resnet50_eMMCR_Vanilla	.063
232	mobilenet_v1_0.25_224	.062
233	squeezenet1_0	.062
234	resnet50_robust_l2_eps1	.062
235	resnet50_linf_8_robust	.061
236	mobilenet_v2_0.5_192-tf	.061
237	mobilenet_v1_1.0_128	.061
238	AdvProp_efficientnet-b8	.061
239	mobilenet_v1_0.25_192	.060
240	mobilenet_v1_0.75_160	.060
241	mobilenet_v2_0.35_224	.059
242	blt_vs	.059
243	konkle_alexnetgn_ipcl_ref01_primary_model	.059
244	mobilenet_v1_0.5_224	.058
245	resnet50_imagenet_100_seed-0	.058
246	alexnet_random_linf8_perturb	.058
247	resnet-50-robust	.058
248	imagenet_l2_3_0	.058
249	resnet50_robust_l2_eps3	.058
250	voneresnet-50	.057
251	vgg-19-keras	.057
252	alexnet_reduced_aliasing_early_checkpoint	.056
253	mobilenet_v2_0.5_224-tf	.055
254	mobilenet_v2_0.75_128	.055
255	mobilenet_v2_0.35_96	.055
256	resnet50-SIN	.055
257	convnext_small_imagenet_1_seed-0	.054
258	mobilenet_v2_1.0_128-tf	.054
259	mobilenet_v1_0.75_128	.053
260	AlexNet_SIN	.052
261	mobilenet_v2_0.35_160	.052
262	mobilenet_v1_0.75_192	.052
263	voneresnet-50-robust	.052
264	mobilenet_v1_1.0_160	.050
265	alexnet_random_l2_3_perturb	.050
266	mobilenet_v2_0.5_96	.050
267	mobilenet_v2_0.5_160	.049
268	alexnet_ks_torevert	.049
269	alexnet	.049
270	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.049
271	mobilenet_v2_0.35_128	.048
272	mobilenet_v1_0.5_128	.046
273	alexnet_fov12	.045
274	vonealexnet_gaussian_noise_std4_fixed	.045
275	alexnet_early_checkpoint	.044
276	alexnet_l2_3_robust	.044
277	resnet_50_v1_spiking_l4	.042
278	AlexNet_SIN_fov12	.042
279	mobilenet_v2_1.0_96	.042
280	alexnet_fov16	.041
281	mobilenet_v2_0.5_128	.038
282	alexnet_training_seed_05	.037
283	alexnet_training_seed_01	.037
284	alexnet_training_seed_01_fov4	.037
285	alexnet_training_seed_01_fov12	.037
286	alexnet_training_seed_01_fov16	.037
287	mobilenet_v1_0.25_160	.035
288	imagenet_l2_10_0	.035
289	mobilenet_v1_0.5_160	.033
290	resnet50-vicregl0p75	.032
291	AlexNet_SIN_fov16	.031
292	alexnet_linf_8_robust	.029
293	alexnet_robust_correct	.023
294	resnet-50_untrained	.021
295	alexnet_training_seed_06	.018
296	deit_small_imagenet_100_seed-0	.018
297	resnet50-meshes-lt-100-original-pretrained	.018
298	alexnet_training_seed_03	.016
299	resnet50-vicreg	.015
300	resnet50_FractalDB	.012
301	artResNet18_1	.011
302	alexnet_training_seed_07	.011
303	resnet50-vicregl0p9	.009
304	alexnet_training_seed_02	.009
305	alexnet_training_seed_04	.009
306	alexnet_training_seed_08	.009
307	alexnet_training_seed_10	.009
308	resnet50-meshes-lt-100-original-scratch	.009
309	mobilenet_v1_0.25_128	.008
310	deit_small_imagenet_10_seed-0	.008
311	deit_small_imagenet_1_seed-0	.008
312	convnext_small_imagenet_10_seed-0	.007
313	resnet50_imagenet_10_seed-0	.007
314	mobilevit_small	.007
315	resnet50_imagenet_1_seed-0	.006
316	resnet50-barlow	.004
317	resnet50-simclr-vissl	.003
318	barlow-twins-resnet50	.002
319	resnet_50_v1_spiking_l3	.001
320	alexnet_training_seed_09	.001
321	resnet-18-LC_w_sh_100_iter_conv_init_m
322	ViT-B/32
323	BiT-M-R50x3
324	BiT-M-R50x1
325	BiT-M-R152x2
326	resnet-18-LC_w_sh_1_iter_conv_init_m
327	resnet-18-LC_w_sh_10_iter_m
328	resnet-18-LC_w_sh_100_iter_m
329	resnet-18-LC_w_sh_10_iter_conv_init_m
330	FrankRobWobv0
331	resnet-18-LC_w_sh_1_iter_m
332	0.5x_resnet-18_LC
333	CORnetZ_CIFAR10
334	resnet18-local_aggregation
335	resnet18-deepcluster
336	r3m_resnet18
337	CORnet-Z
338	vggface
339	my-model
340	resnet18-instance_recognition
341	0.5x_resnet-18_LC_w_sh_10_iter
342	r3m_resnet50
343	r3m_resnet34
344	resnet-18-LC_m
345	r3m_resnet50_nocrop
346	resnet18-supervised
347	resnet-18-LC_1st_conv_m
348	resnet-18-LC_conv_init_m
349	resnet18-contrastive_multiview
350	0.5x_resnet-18
351	resnet18-colorization
352	RN50
353	0.5x_resnet-18_LC_w_sh_100_iter
354	RN50
355	resnet18-relative_position
356	resnet18-autoencoder
357	resnet18-simclr
358	ViT_L_32
359	ViT_B_32
360	resnet18-depth_prediction
361	ViT_B_16
362	r3m_resnet34_nocrop
363	resnet18-contrastive_predictive
364	resnet-18-LC_untrained
365	resnet-18_untrained
366	resnet-50x2_untrained
367	dcgan
368	unet_entire
369	v1-pyr-nodown
370	CORnetZ_CIFAR10_bs32_20_04
371	omnivore_swinT
372	pixels
373	dorinet_cornet_z
374	my-model
375	resnet50
376	openclip
377	resnet50-cifar
378	resnet50_pretrained_with_retinal_waves
379	resnet50_ImageNet
380	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
381	ReAlnet07_ALL
382	yudixie_resnet50_imagenet1kpret_0_240312
383	yudixie_resnet50_imagenet1kpret_0_240908
384	yudixie_resnet50_random_0_240908
385	yudixie_resnet50_cat_obj_class_all_latents_0_240908
386	yudixie_resnet50_category_class_0_240908
387	resnet18_imagenet21kP
388	ConvLSTM
389	PredRNN
390	yudixie_resnet50_distance_reg_0_240908
391	SimVP
392	TAU
393	MIM
394	hmax
395	inception_v3_pytorch
396	yolos_tiny
397	ReAlnet01_cornet
398	yudixie_resnet18_translation_reg_0_240719
399	yudixie_resnet18_distance_reg_0_240719
400	compact_resnet50_V4
401	yudixie_resnet18_rotation_reg_0_240719
402	briaai_rmbg_1_4
403	vit_base_patch14_dinov2-lvd142m
404	ReAlnet03_cornet
405	vit_base_patch16_dinov3-lvd1689m
406	resnet18_ecoset_full
407	ReAlnet01_cornet_fov4
408	resnet34_ecoset_full
409	compact_resnet50_IT
410	resnet50_ecoset_full
411	compact_resnet50_robust_V4
412	resnet101_ecoset_full
413	aughost_fair_resnet18
414	yudixie_resnet50_distance_translation_0_240908
415	yudixie_resnet50_rotation_reg_0_240908
416	resnet152_ecoset_full
417	ReAlnet01_cornet_fov12
418	yudixie_resnet18_distance_translation_0_240719
419	yudixie_resnet18_translation_rotation_0_240719
420	yudixie_resnet18_distance_rotation_0_240719
421	yudixie_resnet18_category_class_0_240719
422	ReAlnet05_cornet
423	compact_vgg19_V4
424	yudixie_resnet18_cat_obj_class_all_latents_0_240719
425	resnet-18_test_m
426	yudixie_resnet18_distance_translation_rotation_0_240719
427	yudixie_resnet18_object_class_0_240719
428	yudixie_resnet18_imagenet1kpret_0_240719
429	SeLaVi-AVE
430	ReAlnet06_cornet
431	r3d_18
432	ReAlnet01_cornet_fov16
433	compact_vgg19_IT
434	yudixie_resnet50_translation_reg_0_240908
435	yudixie_resnet50_translation_rotation_0_240908
436	ReAlnet01_ALL
437	mc3_18
438	ReAlnet07_cornet
439	s3d
440	mvit_v1_b
441	ReAlnet08_cornet
442	ReAlnet03_ALL
443	VOneCORnet-S
444	compact_cornets_V4
445	omnivore_swinB
446	ReAlnet04_ALL
447	SeLaVi-Kinetics400
448	ReAlnet09_cornet
449	SeLaVi-VGG-Sound
450	compact_cornets_IT
451	ReAlnet05_ALL
452	omnivore_swinS
453	ReAlnet06_ALL
454	mvit_v2_s
455	SeLaVi-Kinetics-Sound
456	compact_resnet50_robust_IT
457	ReAlnet10_cornet
458	r2plus1d_18
459	ReAlnet08_ALL
460	yudixie_resnet18_random_0_240719
461	yudixie_resnet50_distance_rotation_0_240908
462	ReAlnet02_cornet
463	yudixie_resnet50_distance_translation_rotation_0_240908
464	ReAlnet09_ALL
465	ReAlnet04_cornet
466	resnet50_primary_visual_cortex
467	yudixie_resnet50_object_class_0_240908
468	ReAlnet10_ALL
469	ReAlnet02_ALL
470	vit_base_patch14_reg4_dinov2-lvd142m
471	resnet18_fair
472	pr1_stl10_disentangled_alpha04
473	predictive_coding_pc
474	yudixie_wbh_object_class_scaling_0415_run_0
475	yudixie_wbh_object_class_scaling_0415_run_3
476	yudixie_wbh_object_class_scaling_0415_imagenet
477	yudixie_wbh_object_class_scaling_0415_run_2
478	yudixie_wbh_object_class_scaling_0415_run_1
479	deepeisnn_ei_cifar10_vgg19
480	deepeisnn_ei_cifar10_resnet18
481	deepeisnn_ei_imagenet_resnet18

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

0.44.

Note that scores are relative to this ceiling.

Data: Geirhos2021rotation

Metric: error_consistency