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

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