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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021uniformnoise-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.781
2	resnext101_32x32d_wsl	.754
3	resnext101_32x48d_wsl	.743
4	vit_base_patch16_clip_224:openai_ft_in1k	.734
5	resnext101_32x16d_wsl	.731
6	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.728
7	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.723
8	fixres_resnext101_32x48d_wsl	.720
9	vit_large_patch14_clip_224:laion2b_ft_in1k	.719
10	convnext_large:fb_in22k_ft_in1k	.686
11	vision_transformer_vit_large_patch16_224	.685
12	SWSL_resnext101_32x8d	.675
13	resnet-152_v2_pytorch	.674
14	resnet_152_v2	.670
15	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.661
16	resnet_101_v2	.647
17	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.647
18	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.645
19	vit_large_patch14_clip_224:openai_ft_in1k	.639
20	resnext101_32x8d_wsl	.637
21	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.635
22	CLIP_resnet50	.627
23	CLIP_resnet50_float32	.627
24	convnext_femto_ols:d1_in1k	.617
25	cvt_cvt-13-384-in1k_4_LucyV4	.614
26	vit_large_patch14_clip_336:laion2b_ft_in1k	.604
27	convnext_xlarge:fb_in22k_ft_in1k	.601
28	focalnet_tiny_lrf_in1k	.601
29	SWSL_resnet50	.599
30	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.591
31	convnext_tiny:in12k_ft_in1k	.590
32	cvt_cvt-13-224-in1k_4_LucyV4	.587
33	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.583
34	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.577
35	convnext_base:clip_laiona_augreg_ft_in1k_384	.576
36	vit_relpos_base_patch16_clsgap_224:sw_in1k	.576
37	CLIP_ViT-B_32	.572
38	nasnet_large	.555
39	CLIP-RN50	.537
40	ViT_L_32_imagenet1k	.524
41	convnext_tiny_imagenet_full_seed-0	.522
42	convnext_base_imagenet_full_seed-0	.520
43	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.520
44	cvt_cvt-21-384-in1k_4_LucyV4	.515
45	convnext_small_imagenet_full_seed-0	.509
46	cvt_cvt-21-224-in1k_4_LucyV4	.493
47	pnasnet_large	.493
48	ViT_B_16_imagenet1k	.476
49	effnetv2m_custom384	.475
50	pnasnet_large-tf	.473
51	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.462
52	resnet_50_v2	.458
53	ViT_B_32_imagenet1k	.455
54	convnext_large_imagenet_full_seed-0	.443
55	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.441
56	nasnet_large-tf	.418
57	inception_v4	.404
58	AdvProp_efficientnet-b6	.401
59	resnet-152_v2-tf	.386
60	resnet50-VITO-8deg-cc	.381
61	inception_v3	.377
62	cv_18_dagger_408_pretrained	.354
63	inception_v3-tf	.348
64	xception-keras	.344
65	resnet101	.340
66	tv_efficientnet-b1	.338
67	inception_v4-tf	.330
68	deit_large_imagenet_full_seed-0	.328
69	xception	.322
70	AdvProp_efficientnet-b4	.309
71	AT_efficientnet-b4	.309
72	antialias-resnet152	.309
73	densenet-201	.289
74	densenet_201_pytorch	.289
75	effnetb1_272x240	.285
76	resnet-50_v2-tf	.284
77	resnet50_ewconplus_wzc	.282
78	antialiased-rnext101_32x8d	.279
79	densenet-201-keras	.272
80	resnet50-moclr8deg	.266
81	BiT-S-R50x3	.265
82	AdvProp_efficientnet-b7	.260
83	deit_base_imagenet_full_seed-0	.257
84	deit_small_imagenet_full_seed-0	.257
85	efficientnet-b7	.254
86	resnet_101_v1	.253
87	AT_efficientnet-b2	.250
88	AdvProp_efficientnet-b2	.250
89	efficientnet_b0	.249
90	resnet50_random_l2_perturb	.245
91	resnet50	.244
92	Res2Net50_26w_4s	.243
93	resnet50_byol	.231
94	resnet_152_v1	.231
95	antialiased-r50	.224
96	resnet_50_v1_spiking	.209
97	resnet50_moco_v2	.209
98	convnext_small_imagenet_100_seed-0	.202
99	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.196
100	BiT-S-R101x3	.194
101	resnet-50-pytorch	.194
102	resnet50-sup	.194
103	resnet50_tutorial	.194
104	resnet_50_v1	.194
105	BiT-S-R152x2	.188
106	convnext_tiny_sup	.188
107	vonegrcnn_62e_nobn	.183
108	grcnn	.182
109	AdvProp_efficientnet-b8	.180
110	nasnet_mobile	.179
111	vonegrcnn_47e	.174
112	densenet-121-keras	.173
113	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.172
114	BiT-S-R152x4	.172
115	densenet-121	.166
116	resnet50-SIN_IN_IN	.162
117	resnet_SIN_IN_FT_IN	.162
118	vonegrcnn_62e_nobn	.157
119	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.157
120	grcnn_109	.156
121	densenet-169	.153
122	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.149
123	vonegrcnn_52e_full	.144
124	densenet-169-keras	.143
125	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.137
126	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.136
127	BiT-S-R101x1	.134
128	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.133
129	voneresnet-50-non_stochastic	.132
130	resnet101_imagenet_full	.131
131	alexnet_training_seed_05	.130
132	alexnet_training_seed_01	.130
133	resnet152_imagenet_full	.129
134	vonegrcnn_47e	.127
135	resnet50-simclr-vissl	.127
136	vonegrcnn_52e_full	.125
137	resnet50_random_linf8_perturb	.122
138	BiT-S-R50x1	.121
139	voneresnet-50-robust	.121
140	mobilenet_v2_1_0_224	.121
141	resnet50-vitoimagevidnet8	.119
142	blt_vs	.119
143	resnet50-SIN_IN	.116
144	inception_v2-tf	.112
145	imagenet_l2_10_0	.105
146	resnet50_imagenet_full	.105
147	resnet50_simclr	.103
148	alexnet_robust_correct	.099
149	alexnet_l2_3_robust	.098
150	custom_model_cv_18_dagger_408	.097
151	vgg-19-keras	.091
152	mobilenet_v1_0.25_128	.090
153	resnet50_imagenet_10_seed-0	.089
154	deit_small_imagenet_10_seed-0	.089
155	deit_small_imagenet_1_seed-0	.089
156	resnet34_imagenet_full	.088
157	alexnet_training_seed_02	.088
158	alexnet_training_seed_04	.088
159	alexnet_training_seed_08	.088
160	alexnet_training_seed_10	.088
161	resnet50-meshes-lt-100-original-pretrained	.088
162	vgg_19	.088
163	inception_v1	.080
164	voneresnet-50	.079
165	konkle_alexnetgn_ipcl_ref01_primary_model	.079
166	nasnet_mobile-tf	.078
167	mobilenet_v2_0.5_160	.078
168	mobilenet_v1_0.5_128	.078
169	alexnet_training_seed_07	.077
170	mobilenet_v2_1_3_224	.076
171	mobilenet_v2_0.5_128	.075
172	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.075
173	mobilenet_v2_1_0_192	.075
174	deit_small_imagenet_100_seed-0	.073
175	AdvProp_efficientnet-b0	.073
176	ReAlnet04	.071
177	mobilenet_v2_0.35_160	.069
178	mobilenet_v2_0.35_192	.068
179	ReAlnet08	.068
180	inception_v1-tf	.067
181	mobilenet_v1_0.5_160	.067
182	ReAlnet03	.067
183	ReAlnet09	.064
184	mobilenet_v2_0.35_128	.062
185	resnet50_eMMCR_Vanilla	.061
186	mobilenet_v1_0.75_128	.061
187	squeezenet1_0	.059
188	mobilenet_v1_0.5_224	.059
189	mobilenet_v2_0.75_96	.058
190	convnext_small_imagenet_1_seed-0	.058
191	resnet50-vicregl0p9	.057
192	resnet50-barlow	.057
193	mobilenet_v2_0.5_192-tf	.057
194	mobilenet_v2_1-4_224_pytorch	.057
195	mobilenet_v2_1_4_224	.057
196	ReAlnet05	.056
197	mobilenet_v2_0.5_224-tf	.056
198	resnet50_imagenet_1_seed-0	.054
199	mobilenet_v1_0.25_192	.054
200	resnet50_linf_4_robust	.050
201	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.049
202	mobilenet_v1_0.25_160	.049
203	ReAlnet01	.049
204	ReAlnet06	.049
205	mobilenet_v2_0.35_224	.048
206	mobilenet_v2_0.75_192-tf	.048
207	ReAlnet07	.048
208	mobilenet_v2_1_0_160	.048
209	mobilenet_v2_0.75_224-tf	.047
210	alexnet_random_l2_3_perturb	.047
211	resnet-34	.047
212	mobilenet_v1_0.5_192	.046
213	mobilenet_v2_1.0_224-tf	.046
214	mobilenet_v1_0.75_160	.046
215	resnet50_l2_3_robust	.046
216	resnet_50_v1_spiking_l4	.046
217	resnet-152_v1-tf	.045
218	vgg-16-keras	.045
219	resnet50_imagenet_100_seed-0	.045
220	resnet50_FractalDB	.044
221	mobilenet_v1_0.75_192	.044
222	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.043
223	alexnet_linf_8_robust	.043
224	alexnet_random_linf8_perturb	.041
225	resnet-101_v1-tf	.041
226	alexnet_early_checkpoint	.040
227	vgg_16	.039
228	resnet-18	.039
229	alexnet-baseline	.038
230	alexnet	.038
231	alexnet	.038
232	alexnet	.038
233	alexnet	.038
234	alexnet	.038
235	alexnet	.038
236	alexnet	.038
237	alexnet	.038
238	alexnet	.038
239	alexnet_ks_torevert	.038
240	resnet-50-robust	.038
241	imagenet_l2_3_0	.038
242	resnet50_robust_l2_eps3	.038
243	resnet18_imagenet_full	.038
244	mobilenet_v2_0.75_160-tf	.038
245	efficientnet_b1_imagenet_full	.036
246	mobilenet_v2_0.75_128	.036
247	mobilenet_v2_1.0_128-tf	.036
248	texture_shape_resnet50_trained_on_SIN	.036
249	mobilenet_v1_1.0_160	.036
250	efficientnet_b0_imagenet_full	.035
251	shufflenet_v2_x1_0	.034
252	resnet_50_v1_spiking_l4a	.034
253	alexnet	.034
254	resnet50_robust_l2_eps1	.033
255	mobilenet_v1_0.25_224	.033
256	alexnet_reduced_aliasing_early_checkpoint	.031
257	squeezenet1_1	.031
258	mobilenet_v1_1.0_192	.030
259	mobilenet_v2_1.0_96	.028
260	resnet50-meshes-lt-100-original-scratch	.028
261	alexnet_training_seed_09	.027
262	regnet_y_400mf	.027
263	resnet50_linf_8_robust	.026
264	alexnet_training_seed_03	.024
265	mobilenet_v2_0.5_96	.024
266	resnet50-SIN	.023
267	mobilenet_v2_1_0_128	.022
268	mobilenet_v1_1.0_224	.022
269	convnext_small_imagenet_10_seed-0	.022
270	resnet50-vicregl0p75	.022
271	resnet-50_untrained	.021
272	0.5x_resnet-18_LC_w_sh_1_iter	.021
273	mobilenet_v2_1.0_160-tf	.020
274	mobilenet_v1_0.75_224	.017
275	mobilenet_v1_1.0_128	.016
276	mobilenet_v2_0_75_224	.016
277	mobilenet_v2_0.35_96	.013
278	ReAlnet02	.011
279	cornet_s	.011
280	ReAlnet10	.011
281	mobilenet_v2_1.3_224-tf	.010
282	vonealexnet_gaussian_noise_std4_fixed	.010
283	resnet_50_v1_spiking_l3	.009
284	mobilenet_v2_1.4_224-tf	.008
285	barlow-twins-resnet50	.006
286	mobilenet_v2_0_5_192	.006
287	efficientnet_b2_imagenet_full	.006
288	CORnet-S	.005
289	mobilenet_v2_0_75_160	.005
290	alexnet_training_seed_06	.005
291	texture_shape_alexnet_trained_on_SIN	.005
292	artResNet18_1	.004
293	mobilenet_v2_1.0_192-tf	.004
294	resnet50-vicreg	.003
295	mobilenet_v2_0_75_192	.003
296	AlexNet_SIN	.002
297	mobilenet_v2_0_5_224	.001
298	resnet-50_v1-tf	.000
299	resnet-18-LC_w_sh_100_iter_conv_init_m
300	ViT-B/32
301	BiT-M-R50x3
302	BiT-M-R50x1
303	resnet-18-LC_w_sh_1_iter_conv_init_m
304	resnet-18-LC_w_sh_1_iter_m
305	BiT-M-R152x2
306	resnet-18-LC_w_sh_10_iter_m
307	ReAlnet09_ALL
308	resnet-18-LC_w_sh_100_iter_m
309	resnet-18-LC_w_sh_10_iter_conv_init_m
310	FrankRobWobv0
311	0.5x_resnet-18_LC
312	CORnetZ_CIFAR10
313	resnet18-local_aggregation
314	r3m_resnet18
315	CORnet-Z
316	resnet18-deepcluster
317	vggface
318	my-model
319	resnet18-instance_recognition
320	0.5x_resnet-18_LC_w_sh_10_iter
321	r3m_resnet50
322	r3m_resnet34
323	resnet-18-LC_m
324	r3m_resnet50_nocrop
325	resnet18-supervised
326	resnet-18-LC_1st_conv_m
327	resnet-18-LC_conv_init_m
328	resnet18-contrastive_multiview
329	0.5x_resnet-18
330	RN50
331	0.5x_resnet-18_LC_w_sh_100_iter
332	RN50
333	resnet18-colorization
334	resnet18-relative_position
335	resnet18-autoencoder
336	resnet18-simclr
337	ViT_L_32
338	ViT_B_32
339	resnet18-depth_prediction
340	ViT_B_16
341	r3m_resnet34_nocrop
342	resnet18-contrastive_predictive
343	resnet-18-LC_untrained
344	resnet-18_untrained
345	resnet-50x2_untrained
346	dcgan
347	unet_entire
348	v1-pyr-nodown
349	CORnetZ_CIFAR10_bs32_20_04
350	pixels
351	dorinet_cornet_z
352	omnivore_swinT
353	my-model
354	resnet50
355	openclip
356	resnet50-cifar
357	resnet50_pretrained_with_retinal_waves
358	resnet50_ImageNet
359	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
360	ReAlnet08_ALL
361	yudixie_resnet50_random_0_240908
362	yudixie_resnet50_imagenet1kpret_0_240908
363	yudixie_resnet50_imagenet1kpret_0_240312
364	yudixie_resnet50_cat_obj_class_all_latents_0_240908
365	yudixie_resnet50_object_class_0_240908
366	yudixie_resnet50_category_class_0_240908
367	resnet18_imagenet21kP
368	yudixie_resnet50_distance_reg_0_240908
369	ConvLSTM
370	PredRNN
371	SimVP
372	TAU
373	MIM
374	hmax
375	briaai_rmbg_1_4
376	pnasnet_large_pytorch
377	inception_v3_pytorch
378	ReAlnet01_cornet
379	yolos_tiny
380	yudixie_resnet18_translation_reg_0_240719
381	ReAlnet02_cornet
382	yudixie_resnet18_distance_reg_0_240719
383	yudixie_resnet18_rotation_reg_0_240719
384	yudixie_resnet18_imagenet1kpret_0_240719
385	compact_resnet50_V4
386	compact_resnet50_IT
387	resnet18_ecoset_full
388	ReAlnet03_cornet
389	resnet34_ecoset_full
390	VOneCORnet-S
391	resnet50_ecoset_full
392	resnet101_ecoset_full
393	yudixie_resnet50_distance_translation_0_240908
394	compact_vgg19_V4
395	yudixie_resnet18_distance_translation_0_240719
396	yudixie_resnet18_translation_rotation_0_240719
397	ReAlnet04_cornet
398	omnivore_swinB
399	yudixie_resnet18_distance_rotation_0_240719
400	yudixie_resnet18_category_class_0_240719
401	yudixie_resnet50_rotation_reg_0_240908
402	ReAlnet05_cornet
403	yudixie_resnet18_random_0_240719
404	resnet-18_test_m
405	yudixie_resnet50_distance_rotation_0_240908
406	yudixie_resnet18_cat_obj_class_all_latents_0_240719
407	SeLaVi-AVE
408	yudixie_resnet18_distance_translation_rotation_0_240719
409	yudixie_resnet18_object_class_0_240719
410	compact_resnet50_robust_V4
411	compact_vgg19_IT
412	ReAlnet06_cornet
413	yudixie_resnet50_translation_reg_0_240908
414	r3d_18
415	ReAlnet07_cornet
416	mc3_18
417	ReAlnet02_ALL
418	s3d
419	ReAlnet08_cornet
420	mvit_v1_b
421	ReAlnet04_ALL
422	compact_cornets_V4
423	ReAlnet03_ALL
424	compact_cornets_IT
425	ReAlnet09_cornet
426	SeLaVi-Kinetics400
427	ReAlnet05_ALL
428	SeLaVi-VGG-Sound
429	ReAlnet06_ALL
430	omnivore_swinS
431	r2plus1d_18
432	compact_resnet50_robust_IT
433	ReAlnet07_ALL
434	mvit_v2_s
435	ReAlnet10_cornet
436	SeLaVi-Kinetics-Sound
437	resnet152_ecoset_full
438	yudixie_resnet50_distance_translation_rotation_0_240908
439	yudixie_resnet50_translation_rotation_0_240908
440	resnet50_primary_visual_cortex
441	ReAlnet10_ALL
442	ReAlnet01_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

0.43.

Note that scores are relative to this ceiling.

Data: Geirhos2021uniformnoise

Metric: error_consistency