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

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