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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	dinov2_vitl14_reg_lc	.681
2	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.646
3	convnext_large_clip_laion2b_finelayers	.640
4	convnext_xxlarge_clip	.631
5	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.614
6	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.606
7	vit_huge_clip336_laion	.590
8	resnet-152_v2_pytorch	.589
9	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.581
10	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.571
11	dinov2_vitg14_reg_lc	.571
12	fixres_resnext101_32x48d_wsl	.568
13	dinov2_vitl14_lc	.567
14	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.565
15	resnet_152_v2	.561
16	dinov2_vitg14_lc	.560
17	vit_large_patch14_clip_336:laion2b_ft_in1k	.559
18	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.546
19	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.539
20	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.528
21	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.527
22	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.516
23	vit_large_clip336_openai	.511
24	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.506
25	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.500
26	effnetv2m_custom384	.489
27	vit_large_patch14_clip_224:openai_ft_in1k	.488
28	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.478
29	convnext_base:clip_laiona_augreg_ft_in1k_384	.476
30	dinov2_vitl14_lc_res336	.475
31	convnext_large_in22k_supervised	.473
32	convnext_large:fb_in22k_ft_in1k	.467
33	vit_large_patch14_clip_224:laion2b_ft_in1k	.457
34	convnext_base_fb_in22k_ft_in1k	.434
35	pnasnet_large_halfPrecision	.431
36	pnasnet_large	.431
37	vit_base_patch16_clip_224:openai_ft_in1k	.429
38	custom_model_cv_18_dagger_408	.425
39	convnext_xlarge:fb_in22k_ft_in1k	.417
40	resnet_101_v2	.416
41	resnet_50_v2	.413
42	resnext101_32x48d_wsl	.405
43	CLIP_resnet50	.404
44	CLIP_resnet50_float32	.404
45	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.404
46	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.400
47	eva02_large_in22k	.396
48	resnext101_32x16d_wsl	.391
49	resnext101_32x32d_wsl	.385
50	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.380
51	SWSL_resnet50	.370
52	SWSL_resnext101_32x8d	.368
53	CLIP-RN50	.367
54	pnasnet_large-tf	.357
55	effnetb1_272x240	.356
56	resnext101_32x8d_wsl	.351
57	nasnet_large	.344
58	cvt_cvt-13-384-in1k_4_LucyV4	.343
59	cvt_cvt-21-384-in1k_4_LucyV4	.338
60	convnext_tiny:in12k_ft_in1k	.334
61	nasnet_large-tf	.325
62	vision_transformer_vit_large_patch16_224	.308
63	antialias-resnet152	.282
64	inception_v3	.273
65	CLIP_ViT-B_32	.273
66	focalnet_tiny_lrf_in1k	.273
67	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.268
68	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.268
69	inception_v4	.266
70	inception_v3-tf	.264
71	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.261
72	resnet152_imagenet_full	.259
73	convnext_femto_ols:d1_in1k	.255
74	resnet101	.255
75	resnet50-VITO-8deg-cc	.254
76	resnet101_imagenet_full	.250
77	vit_relpos_base_patch16_clsgap_224:sw_in1k	.249
78	resnet-152_v2-tf	.239
79	convnext_large_imagenet_full_seed-0	.236
80	resnet50_st_wzc_seed6_2_323	.236
81	resnet_152_v1	.236
82	resnet50_st_wzc_seed3_2_323	.232
83	deit3_base_patch16_224_fb_in1k	.228
84	cv_18_dagger_408_pretrained	.226
85	xception-keras	.225
86	antialiased-rnext101_32x8d	.225
87	convnext_tiny_imagenet_full_seed-0	.224
88	resnet50_random_l2_perturb	.223
89	tv_efficientnet-b1	.221
90	cvt_cvt-13-224-in1k_4_LucyV4	.218
91	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.213
92	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.212
93	convnext_base_imagenet_full_seed-0	.206
94	resnet50	.206
95	inception_v4-tf	.204
96	resnet50_imagenet_full	.204
97	deit_base_imagenet_full_seed-0	.201
98	convnext_small_imagenet_full_seed-0	.197
99	deit_large_imagenet_full_seed-0	.196
100	resnet_101_v1	.195
101	antialiased-r50	.195
102	grcnn_109	.194
103	resnet50_ewconplus_wzc_seed5_2__323	.193
104	resnet50-moclr8deg	.190
105	deit_small_imagenet_full_seed-0	.190
106	resnet50_ewconplus_wzc_seed2_2__323	.183
107	ViT_B_16_imagenet1k	.182
108	cvt_cvt-21-224-in1k_4_LucyV4	.181
109	densenet-201-keras	.181
110	xception	.179
111	Res2Net50_26w_4s	.177
112	pnasnet_large_pytorch	.175
113	convnext_base_fb_in1k	.174
114	densenet-201	.172
115	densenet_201_pytorch	.172
116	resnet_50_v1_spiking	.172
117	efficientnet_b2_imagenet_full	.167
118	vgg_19	.166
119	efficientnet_b0	.165
120	densenet-121-keras	.164
121	convnext_tiny_sup	.161
122	convnext_base_fb_in1k_gram_aligned_dinov3	.161
123	ViT_L_32_imagenet1k	.161
124	grcnn	.161
125	resnet34_imagenet_full	.160
126	resnet50_ewconplus_wzc	.160
127	resnet-50_v2-tf	.156
128	densenet-121	.155
129	resnet50_st_wzc_2	.155
130	resnet50_tutorial	.154
131	resnet_50_v1	.154
132	resnet-50-pytorch	.154
133	resnet50-sup	.154
134	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.153
135	mobilenet_v2_1.4_224-tf	.153
136	resnet50_random_linf8_perturb	.152
137	ViT_B_32_imagenet1k	.152
138	AdvProp_efficientnet-b6	.150
139	resnet50-SIN_IN_IN	.150
140	resnet_SIN_IN_FT_IN	.150
141	BiT-S-R50x3	.149
142	inception_v2-tf	.149
143	resnet-101_v1-tf	.149
144	resnet50_byol	.144
145	efficientnet-b7	.143
146	nasnet_mobile	.143
147	mobilenet_v2_1_0_224	.140
148	AT_efficientnet-b2	.137
149	AdvProp_efficientnet-b2	.137
150	alexnet_fov4	.136
151	resnet50-vitoimagevidnet8	.135
152	cornet_s	.133
153	BiT-S-R152x2	.132
154	densenet-169-keras	.131
155	voneresnet-50-non_stochastic	.130
156	BiT-S-R101x3	.130
157	mobilenet_v1_0.75_224	.130
158	mobilenet_v2_1.0_224-tf	.130
159	resnet18_imagenet_full	.130
160	efficientnet_b0_imagenet_full	.129
161	mobilenet_v2_1_3_224	.124
162	ReAlnet04	.124
163	AdvProp_efficientnet-b7	.123
164	inception_v1	.123
165	nasnet_mobile-tf	.121
166	ReAlnet03	.120
167	resnet50-SIN_IN	.119
168	resnet50_hvs_wzc	.118
169	resnet-50_v1-tf	.118
170	convnext_small_imagenet_100_seed-0	.117
171	ReAlnet10	.116
172	resnet_50_v1_spiking_l4a	.116
173	vgg_16	.116
174	AdvProp_efficientnet-b4	.115
175	AT_efficientnet-b4	.115
176	eBarlow_lmda_02_1000ep	.115
177	ReAlnet06	.115
178	ReAlnet01	.114
179	ReAlnet02	.114
180	ReAlnet09	.114
181	resnet-152_v1-tf	.114
182	densenet-169	.112
183	mobilenet_v2_1.3_224-tf	.109
184	resnet50_moco_v2	.105
185	BiT-S-R152x4	.105
186	efficientnet_b1_imagenet_full	.101
187	AlexNet_SIN_fov4	.101
188	mobilenet_v1_1.0_192	.100
189	inception_v1-tf	.098
190	ReAlnet05	.098
191	mobilenet_v2_1.0_160-tf	.096
192	vonegrcnn_47e	.096
193	ReAlnet07	.095
194	mobilenet_v2_1_0_192	.094
195	CORnet-S	.094
196	mobilenet_v1_1.0_224	.092
197	ReAlnet08	.092
198	mobilenet_v2_1_0_160	.091
199	mobilenet_v2_0_5_224	.091
200	resnet-34	.090
201	mobilenet_v2_0_75_192	.089
202	BiT-S-R50x1	.089
203	mobilenet_v2_0_75_160	.088
204	mobilenet_v2_1-4_224_pytorch	.087
205	mobilenet_v2_1_4_224	.087
206	resnet50_linf_4_robust	.085
207	squeezenet1_1	.085
208	BiT-S-R101x1	.085
209	vonegrcnn_62e_nobn	.085
210	regnet_y_400mf	.085
211	vgg-16-keras	.084
212	vonegrcnn_47e	.081
213	resnet50_simclr	.081
214	mobilenet_v2_0_75_224	.080
215	vonegrcnn_52e_full	.080
216	resnet-18	.079
217	vonegrcnn_52e_full	.079
218	mobilenet_v2_0_5_192	.079
219	AdvProp_efficientnet-b0	.078
220	vonegrcnn_62e_nobn	.075
221	shufflenet_v2_x1_0	.074
222	resnet50_l2_3_robust	.073
223	mobilenet_v1_0.5_192	.073
224	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.073
225	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.071
226	0.5x_resnet-18_LC_w_sh_1_iter	.070
227	mobilenet_v2_0.75_160-tf	.070
228	mobilenet_v2_1.0_192-tf	.069
229	texture_shape_alexnet_trained_on_SIN	.068
230	mobilenet_v2_0.75_224-tf	.067
231	mobilenet_v2_0.75_192-tf	.064
232	texture_shape_resnet50_trained_on_SIN	.064
233	mobilenet_v2_0.35_192	.064
234	mobilenet_v2_1_0_128	.063
235	mobilenet_v2_0.75_96	.063
236	resnet50_eMMCR_Vanilla	.063
237	mobilenet_v1_0.25_224	.062
238	squeezenet1_0	.062
239	resnet50_robust_l2_eps1	.062
240	resnet50_linf_8_robust	.061
241	mobilenet_v2_0.5_192-tf	.061
242	mobilenet_v1_1.0_128	.061
243	AdvProp_efficientnet-b8	.061
244	mobilenet_v1_0.25_192	.060
245	mobilenet_v1_0.75_160	.060
246	mobilenet_v2_0.35_224	.059
247	blt_vs	.059
248	konkle_alexnetgn_ipcl_ref01_primary_model	.059
249	mobilenet_v1_0.5_224	.058
250	resnet50_imagenet_100_seed-0	.058
251	alexnet_random_linf8_perturb	.058
252	resnet-50-robust	.058
253	imagenet_l2_3_0	.058
254	resnet50_robust_l2_eps3	.058
255	voneresnet-50	.057
256	vgg-19-keras	.057
257	alexnet_reduced_aliasing_early_checkpoint	.056
258	mobilenet_v2_0.5_224-tf	.055
259	mobilenet_v2_0.75_128	.055
260	mobilenet_v2_0.35_96	.055
261	resnet50-SIN	.055
262	convnext_small_imagenet_1_seed-0	.054
263	mobilenet_v2_1.0_128-tf	.054
264	mobilenet_v1_0.75_128	.053
265	AlexNet_SIN	.052
266	mobilenet_v2_0.35_160	.052
267	mobilenet_v1_0.75_192	.052
268	voneresnet-50-robust	.052
269	mobilenet_v1_1.0_160	.050
270	alexnet_random_l2_3_perturb	.050
271	mobilenet_v2_0.5_96	.050
272	mobilenet_v2_0.5_160	.049
273	alexnet_ks_torevert	.049
274	alexnet	.049
275	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.049
276	mobilenet_v2_0.35_128	.048
277	mobilenet_v1_0.5_128	.046
278	alexnet_fov12	.045
279	vonealexnet_gaussian_noise_std4_fixed	.045
280	alexnet_early_checkpoint	.044
281	alexnet_l2_3_robust	.044
282	resnet_50_v1_spiking_l4	.042
283	AlexNet_SIN_fov12	.042
284	mobilenet_v2_1.0_96	.042
285	alexnet_fov16	.041
286	mobilenet_v2_0.5_128	.038
287	alexnet_training_seed_05	.037
288	alexnet_training_seed_01	.037
289	alexnet_training_seed_01_fov4	.037
290	alexnet_training_seed_01_fov12	.037
291	alexnet_training_seed_01_fov16	.037
292	mobilenet_v1_0.25_160	.035
293	imagenet_l2_10_0	.035
294	mobilenet_v1_0.5_160	.033
295	resnet50-vicregl0p75	.032
296	AlexNet_SIN_fov16	.031
297	alexnet_linf_8_robust	.029
298	alexnet_robust_correct	.023
299	resnet-50_untrained	.021
300	alexnet_training_seed_06	.018
301	deit_small_imagenet_100_seed-0	.018
302	alexnet_training_seed_03	.016
303	resnet50-vicreg	.015
304	resnet50_FractalDB	.012
305	artResNet18_1	.011
306	alexnet_training_seed_07	.011
307	resnet50-vicregl0p9	.009
308	alexnet_training_seed_02	.009
309	alexnet_training_seed_04	.009
310	alexnet_training_seed_08	.009
311	alexnet_training_seed_10	.009
312	mobilenet_v1_0.25_128	.008
313	deit_small_imagenet_10_seed-0	.008
314	deit_small_imagenet_1_seed-0	.008
315	convnext_small_imagenet_10_seed-0	.007
316	resnet50_imagenet_10_seed-0	.007
317	mobilevit_small	.007
318	resnet50_imagenet_1_seed-0	.006
319	resnet50-barlow	.004
320	resnet50-simclr-vissl	.003
321	barlow-twins-resnet50	.002
322	resnet_50_v1_spiking_l3	.001
323	alexnet_training_seed_09	.001
324	resnet-18-LC_w_sh_100_iter_conv_init_m
325	ViT-B/32
326	BiT-M-R50x3
327	BiT-M-R50x1
328	BiT-M-R152x2
329	resnet-18-LC_w_sh_1_iter_conv_init_m
330	resnet-18-LC_w_sh_10_iter_m
331	resnet-18-LC_w_sh_100_iter_m
332	resnet-18-LC_w_sh_10_iter_conv_init_m
333	FrankRobWobv0
334	resnet-18-LC_w_sh_1_iter_m
335	0.5x_resnet-18_LC
336	CORnetZ_CIFAR10
337	resnet18-local_aggregation
338	resnet18-deepcluster
339	r3m_resnet18
340	CORnet-Z
341	vggface
342	my-model
343	resnet18-instance_recognition
344	0.5x_resnet-18_LC_w_sh_10_iter
345	r3m_resnet50
346	r3m_resnet34
347	resnet-18-LC_m
348	r3m_resnet50_nocrop
349	resnet18-supervised
350	resnet-18-LC_1st_conv_m
351	resnet-18-LC_conv_init_m
352	resnet18-contrastive_multiview
353	0.5x_resnet-18
354	resnet18-colorization
355	RN50
356	0.5x_resnet-18_LC_w_sh_100_iter
357	RN50
358	resnet18-relative_position
359	resnet18-autoencoder
360	resnet18-simclr
361	ViT_L_32
362	ViT_B_32
363	resnet18-depth_prediction
364	ViT_B_16
365	r3m_resnet34_nocrop
366	resnet18-contrastive_predictive
367	resnet-18-LC_untrained
368	resnet-18_untrained
369	resnet-50x2_untrained
370	dcgan
371	unet_entire
372	v1-pyr-nodown
373	CORnetZ_CIFAR10_bs32_20_04
374	omnivore_swinT
375	pixels
376	dorinet_cornet_z
377	my-model
378	openclip
379	resnet50-cifar
380	resnet50_pretrained_with_retinal_waves
381	resnet50_ImageNet
382	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
383	ReAlnet07_ALL
384	yudixie_resnet50_imagenet1kpret_0_240312
385	yudixie_resnet50_imagenet1kpret_0_240908
386	yudixie_resnet50_random_0_240908
387	yudixie_resnet50_cat_obj_class_all_latents_0_240908
388	yudixie_resnet50_category_class_0_240908
389	resnet18_imagenet21kP
390	ConvLSTM
391	PredRNN
392	yudixie_resnet50_distance_reg_0_240908
393	SimVP
394	TAU
395	MIM
396	hmax
397	inception_v3_pytorch
398	yolos_tiny
399	ReAlnet01_cornet
400	yudixie_resnet18_translation_reg_0_240719
401	yudixie_resnet18_distance_reg_0_240719
402	compact_resnet50_V4
403	yudixie_resnet18_rotation_reg_0_240719
404	briaai_rmbg_1_4
405	vit_base_patch14_dinov2-lvd142m
406	ReAlnet03_cornet
407	vit_base_patch16_dinov3-lvd1689m
408	resnet18_ecoset_full
409	ReAlnet01_cornet_fov4
410	resnet34_ecoset_full
411	compact_resnet50_IT
412	resnet50_ecoset_full
413	compact_resnet50_robust_V4
414	resnet101_ecoset_full
415	aughost_fair_resnet18
416	yudixie_resnet50_distance_translation_0_240908
417	yudixie_resnet50_rotation_reg_0_240908
418	resnet152_ecoset_full
419	ReAlnet01_cornet_fov12
420	yudixie_resnet18_distance_translation_0_240719
421	yudixie_resnet18_translation_rotation_0_240719
422	yudixie_resnet18_distance_rotation_0_240719
423	yudixie_resnet18_category_class_0_240719
424	ReAlnet05_cornet
425	compact_vgg19_V4
426	yudixie_resnet18_cat_obj_class_all_latents_0_240719
427	resnet-18_test_m
428	yudixie_resnet18_distance_translation_rotation_0_240719
429	yudixie_resnet18_object_class_0_240719
430	yudixie_resnet18_imagenet1kpret_0_240719
431	SeLaVi-AVE
432	ReAlnet06_cornet
433	r3d_18
434	ReAlnet01_cornet_fov16
435	compact_vgg19_IT
436	yudixie_resnet50_translation_reg_0_240908
437	yudixie_resnet50_translation_rotation_0_240908
438	ReAlnet01_ALL
439	mc3_18
440	ReAlnet07_cornet
441	s3d
442	mvit_v1_b
443	ReAlnet08_cornet
444	ReAlnet03_ALL
445	VOneCORnet-S
446	compact_cornets_V4
447	omnivore_swinB
448	ReAlnet04_ALL
449	SeLaVi-Kinetics400
450	ReAlnet09_cornet
451	SeLaVi-VGG-Sound
452	compact_cornets_IT
453	ReAlnet05_ALL
454	omnivore_swinS
455	ReAlnet06_ALL
456	mvit_v2_s
457	SeLaVi-Kinetics-Sound
458	compact_resnet50_robust_IT
459	ReAlnet10_cornet
460	r2plus1d_18
461	ReAlnet08_ALL
462	yudixie_resnet18_random_0_240719
463	yudixie_resnet50_distance_rotation_0_240908
464	ReAlnet02_cornet
465	yudixie_resnet50_distance_translation_rotation_0_240908
466	ReAlnet09_ALL
467	ReAlnet04_cornet
468	resnet50_primary_visual_cortex
469	yudixie_resnet50_object_class_0_240908
470	ReAlnet10_ALL
471	ReAlnet02_ALL
472	vit_base_patch14_reg4_dinov2-lvd142m
473	resnet18_fair
474	pr1_stl10_disentangled_alpha04
475	predictive_coding_pc
476	yudixie_wbh_object_class_scaling_0415_run_0
477	yudixie_wbh_object_class_scaling_0415_run_3
478	yudixie_wbh_object_class_scaling_0415_imagenet
479	yudixie_wbh_object_class_scaling_0415_run_2
480	yudixie_wbh_object_class_scaling_0415_run_1
481	deepeisnn_ei_cifar10_vgg19
482	deepeisnn_ei_cifar10_resnet18
483	deepeisnn_ei_imagenet_resnet18
484	dinov2_vitl14_reg_lc_featread

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