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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021edge-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_patch14_clip_224:laion2b_ft_in1k	1.2
2	vit_large_patch14_clip_336:laion2b_ft_in1k	1.2
3	vit_large_patch14_clip_336:openai_ft_in12k_in1k	1.2
4	convnext_base:clip_laiona_augreg_ft_in1k_384	1.0
5	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.995
6	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.979
7	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.899
8	CLIP_ViT-B_32	.854
9	vit_large_patch14_clip_224:openai_ft_in1k	.807
10	vit_base_patch16_clip_224:openai_ft_in1k	.753
11	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.744
12	SWSL_resnext101_32x8d	.728
13	resnext101_32x16d_wsl	.719
14	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.699
15	convnext_xlarge:fb_in22k_ft_in1k	.670
16	resnext101_32x32d_wsl	.645
17	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.635
18	resnext101_32x48d_wsl	.619
19	resnext101_32x8d_wsl	.618
20	CLIP-RN50	.608
21	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.543
22	vision_transformer_vit_large_patch16_224	.543
23	convnext_large:fb_in22k_ft_in1k	.531
24	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.519
25	CLIP_resnet50	.516
26	CLIP_resnet50_float32	.516
27	fixres_resnext101_32x48d_wsl	.502
28	SWSL_resnet50	.395
29	convnext_large_imagenet_full_seed-0	.285
30	resnet50-VITO-8deg-cc	.264
31	convnext_tiny_imagenet_full_seed-0	.264
32	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.252
33	cvt_cvt-21-384-in1k_4_LucyV4	.243
34	convnext_tiny:in12k_ft_in1k	.232
35	convnext_base_imagenet_full_seed-0	.219
36	AlexNet_SIN_fov4	.200
37	convnext_small_imagenet_full_seed-0	.198
38	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.195
39	xception-keras	.194
40	vit_relpos_base_patch16_clsgap_224:sw_in1k	.192
41	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.187
42	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.184
43	effnetv2m_custom384	.183
44	inception_v4	.182
45	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.182
46	inception_v3_pytorch	.175
47	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.173
48	cvt_cvt-21-224-in1k_4_LucyV4	.168
49	AT_efficientnet-b2	.163
50	AdvProp_efficientnet-b2	.163
51	cvt_cvt-13-384-in1k_4_LucyV4	.160
52	convnext_femto_ols:d1_in1k	.158
53	pnasnet_large	.153
54	cvt_cvt-13-224-in1k_4_LucyV4	.151
55	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.151
56	resnet_152_v2	.151
57	resnet_50_v2	.151
58	inception_v4-tf	.148
59	focalnet_tiny_lrf_in1k	.144
60	resnet-152_v2_pytorch	.144
61	inception_v3-tf	.140
62	resnet50_ewconplus_wzc	.139
63	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.139
64	AdvProp_efficientnet-b6	.139
65	efficientnet-b7	.138
66	resnet50_byol	.133
67	pnasnet_large_pytorch	.132
68	antialias-resnet152	.132
69	cv_18_dagger_408_pretrained	.131
70	blt_vs	.128
71	alexnet_fov4	.124
72	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.124
73	inception_v3	.124
74	custom_model_cv_18_dagger_408	.122
75	resnet101	.116
76	resnet-18	.116
77	nasnet_large	.115
78	nasnet_large-tf	.115
79	resnet50-SIN_IN	.115
80	resnet50_random_linf8_perturb	.114
81	convnext_small_imagenet_100_seed-0	.113
82	densenet-201-keras	.113
83	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.113
84	deit_large_imagenet_full_seed-0	.109
85	resnet_101_v2	.109
86	AdvProp_efficientnet-b4	.107
87	squeezenet1_1	.107
88	resnet-152_v2-tf	.107
89	AT_efficientnet-b4	.107
90	vgg_16	.107
91	resnet_101_v1	.107
92	resnet50_imagenet_100_seed-0	.106
93	efficientnet_b0	.106
94	pnasnet_large-tf	.106
95	mobilenet_v2_1_0_160	.106
96	densenet-201	.105
97	densenet_201_pytorch	.105
98	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.105
99	densenet-121	.104
100	densenet-121-keras	.104
101	mobilenet_v2_1_0_128	.101
102	convnext_tiny_sup	.100
103	resnet50-vitoimagevidnet8	.099
104	inception_v1	.099
105	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.099
106	resnet-50_v2-tf	.098
107	mobilenet_v1_1.0_224	.098
108	tv_efficientnet-b1	.098
109	densenet-169	.098
110	resnet50_random_l2_perturb	.098
111	ReAlnet02	.093
112	AdvProp_efficientnet-b7	.092
113	texture_shape_resnet50_trained_on_SIN	.092
114	resnet-101_v1-tf	.092
115	resnet-34	.092
116	cornet_s	.092
117	alexnet_l2_3_robust	.092
118	alexnet_linf_8_robust	.092
119	ReAlnet10	.092
120	mobilenet_v2_0_5_192	.092
121	nasnet_mobile	.092
122	efficientnet_b0_imagenet_full	.091
123	efficientnet_b2_imagenet_full	.091
124	ReAlnet08	.091
125	resnet18_imagenet_full	.091
126	mobilenet_v2_0_75_160	.091
127	ReAlnet01	.091
128	CORnet-S	.091
129	inception_v1-tf	.091
130	densenet-169-keras	.090
131	resnet50-moclr8deg	.090
132	inception_v2-tf	.085
133	konkle_alexnetgn_ipcl_ref01_primary_model	.085
134	ReAlnet07	.084
135	resnet50_imagenet_full	.084
136	mobilenet_v2_1_0_224	.084
137	vonegrcnn_52e_full	.084
138	BiT-S-R152x2	.084
139	voneresnet-50-non_stochastic	.084
140	mobilenet_v2_0.5_192-tf	.084
141	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
142	mobilenet_v2_0.35_160	.084
143	alexnet_robust_correct	.084
144	vgg_19	.084
145	antialiased-rnext101_32x8d	.084
146	ReAlnet05	.084
147	AlexNet_SIN_fov12	.084
148	resnet50_eMMCR_Vanilla	.084
149	ViT_B_16_imagenet1k	.084
150	ReAlnet06	.083
151	ReAlnet04	.083
152	ReAlnet03	.083
153	mobilenet_v1_0.25_160	.079
154	0.5x_resnet-18_LC_w_sh_1_iter	.078
155	antialiased-r50	.078
156	vonegrcnn_62e_nobn	.077
157	mobilenet_v2_0_75_224	.077
158	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.077
159	mobilenet_v1_1.0_192	.077
160	mobilenet_v2_0.35_192	.077
161	mobilenet_v1_0.5_128	.077
162	resnet50	.077
163	vonealexnet_gaussian_noise_std4_fixed	.077
164	vonegrcnn_47e	.076
165	alexnet	.076
166	deit_base_imagenet_full_seed-0	.076
167	xception	.076
168	mobilenet_v2_1.4_224-tf	.076
169	BiT-S-R50x1	.076
170	BiT-S-R152x4	.076
171	mobilenet_v2_0.5_128	.076
172	alexnet_early_checkpoint	.076
173	alexnet_reduced_aliasing_early_checkpoint	.076
174	Res2Net50_26w_4s	.076
175	mobilenet_v1_1.0_160	.075
176	ReAlnet09	.075
177	mobilenet_v2_1_3_224	.075
178	grcnn	.075
179	mobilenet_v1_0.75_128	.074
180	resnet101_imagenet_full	.074
181	mobilenet_v2_0.75_96	.074
182	effnetb1_272x240	.074
183	resnet34_imagenet_full	.070
184	mobilenet_v2_0_5_224	.070
185	vonegrcnn_52e_full	.070
186	mobilenet_v2_1.0_128-tf	.070
187	resnet50-SIN_IN_IN	.069
188	resnet_SIN_IN_FT_IN	.069
189	alexnet-baseline	.069
190	mobilenet_v2_1.0_224-tf	.069
191	AdvProp_efficientnet-b8	.069
192	alexnet	.069
193	alexnet	.069
194	alexnet	.069
195	alexnet	.069
196	alexnet	.069
197	alexnet	.069
198	alexnet	.069
199	alexnet	.069
200	alexnet	.069
201	shufflenet_v2_x1_0	.069
202	alexnet	.069
203	alexnet_ks_torevert	.069
204	resnet-50-pytorch	.068
205	BiT-S-R50x3	.068
206	resnet-152_v1-tf	.068
207	resnet50-sup	.068
208	mobilenet_v1_0.5_192	.068
209	ViT_L_32_imagenet1k	.068
210	resnet50_tutorial	.068
211	grcnn_109	.068
212	resnet_50_v1	.068
213	BiT-S-R101x3	.066
214	efficientnet_b1_imagenet_full	.064
215	squeezenet1_0	.063
216	mobilenet_v2_0.75_224-tf	.063
217	mobilenet_v1_0.25_224	.063
218	resnet50_moco_v2	.063
219	voneresnet-50	.062
220	vonegrcnn_62e_nobn	.062
221	resnet50-SIN	.062
222	ViT_B_32_imagenet1k	.062
223	vgg-16-keras	.062
224	mobilenet_v2_0.35_128	.062
225	regnet_y_400mf	.062
226	mobilenet_v2_1_0_192	.062
227	vonegrcnn_47e	.062
228	mobilenet_v1_0.5_224	.062
229	mobilenet_v1_0.75_160	.062
230	resnet152_imagenet_full	.062
231	mobilenet_v2_1-4_224_pytorch	.061
232	deit_small_imagenet_full_seed-0	.061
233	mobilenet_v2_1_4_224	.061
234	resnet_152_v1	.060
235	resnet50_imagenet_1_seed-0	.059
236	resnet-50_untrained	.059
237	alexnet_random_l2_3_perturb	.056
238	mobilenet_v2_0_75_192	.056
239	voneresnet-50-robust	.055
240	mobilenet_v2_0.75_192-tf	.055
241	texture_shape_alexnet_trained_on_SIN	.055
242	mobilenet_v2_0.5_224-tf	.055
243	mobilenet_v2_1.0_160-tf	.055
244	nasnet_mobile-tf	.055
245	BiT-S-R101x1	.055
246	mobilenet_v2_1.3_224-tf	.055
247	resnet-50-robust	.055
248	vgg-19-keras	.055
249	mobilenet_v2_1.0_96	.055
250	mobilenet_v1_0.75_192	.055
251	mobilenet_v1_1.0_128	.055
252	mobilenet_v2_0.75_128	.055
253	mobilenet_v2_0.35_96	.055
254	AdvProp_efficientnet-b0	.055
255	mobilenet_v2_0.5_96	.055
256	resnet50-meshes-lt-100-original-scratch	.055
257	resnet50_l2_3_robust	.055
258	resnet50_linf_4_robust	.055
259	resnet50_linf_8_robust	.055
260	resnet50_FractalDB	.055
261	imagenet_l2_3_0	.055
262	imagenet_l2_10_0	.055
263	resnet50_robust_l2_eps3	.055
264	resnet50_robust_l2_eps1	.055
265	AlexNet_SIN	.055
266	alexnet_fov12	.055
267	alexnet_fov16	.055
268	AlexNet_SIN_fov16	.055
269	mobilenet_v2_0.75_160-tf	.053
270	mobilenet_v1_0.25_192	.053
271	mobilenet_v2_0.35_224	.053
272	mobilenet_v2_0.5_160	.053
273	mobilevit_small	.050
274	resnet50_simclr	.046
275	mobilenet_v1_0.75_224	.046
276	resnet50-barlow	.042
277	resnet50-meshes-lt-100-original-pretrained	.042
278	convnext_small_imagenet_1_seed-0	.042
279	resnet-50_v1-tf	.039
280	resnet_50_v1_spiking_l4a	.039
281	alexnet_random_linf8_perturb	.038
282	resnet50-vicregl0p9	.037
283	alexnet_training_seed_03	.036
284	resnet_50_v1_spiking	.035
285	mobilenet_v2_1.0_192-tf	.034
286	alexnet_training_seed_09	.034
287	mobilenet_v1_0.25_128	.032
288	alexnet_training_seed_02	.032
289	alexnet_training_seed_08	.032
290	alexnet_training_seed_04	.032
291	alexnet_training_seed_10	.032
292	alexnet_training_seed_07	.032
293	mobilenet_v1_0.5_160	.032
294	alexnet_training_seed_05	.024
295	alexnet_training_seed_01	.024
296	alexnet_training_seed_01_fov4	.024
297	alexnet_training_seed_01_fov12	.024
298	alexnet_training_seed_01_fov16	.024
299	barlow-twins-resnet50	.024
300	resnet50-vicreg	.024
301	resnet_50_v1_spiking_l4	.023
302	deit_small_imagenet_10_seed-0	.021
303	resnet50_imagenet_10_seed-0	.021
304	convnext_small_imagenet_10_seed-0	.021
305	deit_small_imagenet_1_seed-0	.021
306	artResNet18_1	.021
307	resnet_50_v1_spiking_l3	.015
308	deit_small_imagenet_100_seed-0	.014
309	resnet50-simclr-vissl	.011
310	resnet50-vicregl0p75	.010
311	alexnet_training_seed_06	.007
312	resnet-18-LC_w_sh_100_iter_conv_init_m
313	ViT-B/32
314	BiT-M-R50x3
315	BiT-M-R50x1
316	BiT-M-R152x2
317	resnet-18-LC_w_sh_1_iter_conv_init_m
318	resnet-18-LC_w_sh_1_iter_m
319	resnet-18-LC_w_sh_10_iter_m
320	resnet-18-LC_w_sh_100_iter_m
321	resnet-18-LC_w_sh_10_iter_conv_init_m
322	FrankRobWobv0
323	resnet18-local_aggregation
324	0.5x_resnet-18_LC
325	CORnetZ_CIFAR10
326	resnet18-deepcluster
327	r3m_resnet18
328	CORnet-Z
329	resnet18-instance_recognition
330	my-model
331	vggface
332	resnet18-colorization
333	0.5x_resnet-18_LC_w_sh_10_iter
334	r3m_resnet50
335	r3m_resnet34
336	resnet-18-LC_m
337	r3m_resnet50_nocrop
338	resnet18-supervised
339	resnet-18-LC_1st_conv_m
340	resnet-18-LC_conv_init_m
341	resnet18-contrastive_multiview
342	0.5x_resnet-18
343	RN50
344	0.5x_resnet-18_LC_w_sh_100_iter
345	RN50
346	resnet18-relative_position
347	resnet18-autoencoder
348	resnet18-simclr
349	ViT_L_32
350	ViT_B_32
351	resnet18-depth_prediction
352	ViT_B_16
353	r3m_resnet34_nocrop
354	resnet18-contrastive_predictive
355	omnivore_swinT
356	resnet-18-LC_untrained
357	resnet-18_untrained
358	resnet-50x2_untrained
359	dcgan
360	v1-pyr-nodown
361	unet_entire
362	CORnetZ_CIFAR10_bs32_20_04
363	pixels
364	dorinet_cornet_z
365	my-model
366	resnet50
367	openclip
368	resnet50-cifar
369	resnet50_pretrained_with_retinal_waves
370	resnet50_ImageNet
371	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
372	yudixie_resnet50_random_0_240908
373	yudixie_resnet50_imagenet1kpret_0_240908
374	yudixie_resnet50_object_class_0_240908
375	yudixie_resnet50_category_class_0_240908
376	resnet18_imagenet21kP
377	yudixie_resnet50_distance_reg_0_240908
378	ConvLSTM
379	PredRNN
380	SimVP
381	TAU
382	MIM
383	ReAlnet01_cornet
384	briaai_rmbg_1_4
385	yudixie_resnet18_translation_reg_0_240719
386	yudixie_resnet18_distance_reg_0_240719
387	yudixie_resnet18_rotation_reg_0_240719
388	yolos_tiny
389	yudixie_resnet18_imagenet1kpret_0_240719
390	ReAlnet02_cornet
391	compact_resnet50_V4
392	ReAlnet03_cornet
393	compact_resnet50_IT
394	ReAlnet01_cornet_fov4
395	resnet101_ecoset_full
396	ReAlnet01_cornet_fov12
397	compact_resnet50_robust_V4
398	yudixie_resnet50_rotation_reg_0_240908
399	resnet152_ecoset_full
400	yudixie_resnet50_distance_rotation_0_240908
401	yudixie_resnet50_distance_translation_0_240908
402	yudixie_resnet18_translation_rotation_0_240719
403	yudixie_resnet18_distance_rotation_0_240719
404	compact_vgg19_V4
405	yudixie_resnet18_category_class_0_240719
406	ReAlnet05_cornet
407	resnet-18_test_m
408	SeLaVi-AVE
409	yudixie_resnet18_distance_translation_rotation_0_240719
410	yudixie_resnet18_object_class_0_240719
411	hmax
412	ReAlnet06_cornet
413	r3d_18
414	compact_vgg19_IT
415	ReAlnet01_cornet_fov16
416	yudixie_resnet50_translation_reg_0_240908
417	mc3_18
418	ReAlnet01_ALL
419	yudixie_resnet50_translation_rotation_0_240908
420	s3d
421	ReAlnet07_cornet
422	mvit_v1_b
423	ReAlnet02_ALL
424	ReAlnet08_cornet
425	ReAlnet03_ALL
426	compact_cornets_V4
427	VOneCORnet-S
428	ReAlnet04_ALL
429	omnivore_swinB
430	ReAlnet09_cornet
431	SeLaVi-VGG-Sound
432	compact_cornets_IT
433	ReAlnet05_ALL
434	SeLaVi-Kinetics-Sound
435	SeLaVi-Kinetics400
436	r2plus1d_18
437	ReAlnet06_ALL
438	omnivore_swinS
439	mvit_v2_s
440	compact_resnet50_robust_IT
441	ReAlnet10_cornet
442	resnet18_ecoset_full
443	resnet34_ecoset_full
444	ReAlnet07_ALL
445	resnet50_ecoset_full
446	yudixie_resnet18_cat_obj_class_all_latents_0_240719
447	yudixie_resnet18_random_0_240719
448	ReAlnet08_ALL
449	yudixie_resnet18_distance_translation_0_240719
450	yudixie_resnet50_imagenet1kpret_0_240312
451	ReAlnet04_cornet
452	yudixie_resnet50_distance_translation_rotation_0_240908
453	yudixie_resnet50_cat_obj_class_all_latents_0_240908
454	resnet50_primary_visual_cortex
455	ReAlnet09_ALL
456	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

0.32.

Note that scores are relative to this ceiling.

Data: Geirhos2021edge

Metric: error_consistency