Leaderboard Explore Compare Profile Community Blog FAQ Competition 2024 Report Issue

Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021cueconflict-error_consistency")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.943
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.938
3	vit_large_patch14_clip_224:laion2b_ft_in1k	.855
4	vit_large_patch14_clip_224:openai_ft_in1k	.840
5	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.804
6	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.784
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.780
8	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.752
9	vit_large_patch14_clip_336:laion2b_ft_in1k	.748
10	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.740
11	convnext_large:fb_in22k_ft_in1k	.698
12	convnext_base:clip_laiona_augreg_ft_in1k_384	.686
13	vision_transformer_vit_large_patch16_224	.656
14	convnext_xlarge:fb_in22k_ft_in1k	.651
15	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.633
16	fixres_resnext101_32x48d_wsl	.611
17	resnext101_32x32d_wsl	.596
18	resnext101_32x16d_wsl	.595
19	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.571
20	resnext101_32x48d_wsl	.560
21	vit_base_patch16_clip_224:openai_ft_in1k	.560
22	SWSL_resnext101_32x8d	.546
23	resnext101_32x8d_wsl	.540
24	CLIP_ViT-B_32	.482
25	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.477
26	resnet50-VITO-8deg-cc	.451
27	effnetv2m_custom384	.448
28	cvt_cvt-13-384-in1k_4_LucyV4	.441
29	resnet-152_v2-tf	.405
30	resnet_152_v2	.402
31	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.394
32	resnet_101_v2	.393
33	SWSL_resnet50	.390
34	resnet_50_v2	.383
35	vit_relpos_base_patch16_clsgap_224:sw_in1k	.380
36	ViT_B_16_imagenet1k	.376
37	cvt_cvt-21-384-in1k_4_LucyV4	.372
38	CLIP_resnet50	.371
39	CLIP_resnet50_float32	.371
40	pnasnet_large_halfPrecision	.357
41	effnetb1_272x240	.357
42	pnasnet_large	.355
43	efficientnet_b0	.346
44	inception_v3	.345
45	ViT_L_32_imagenet1k	.344
46	xception-keras	.343
47	nasnet_large	.332
48	cv_18_dagger_408_pretrained	.328
49	pnasnet_large-tf	.327
50	convnext_femto_ols:d1_in1k	.325
51	convnext_tiny_imagenet_full_seed-0	.321
52	CLIP-RN50	.317
53	pnasnet_large_pytorch	.316
54	resnet101	.316
55	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.316
56	focalnet_tiny_lrf_in1k	.313
57	inception_v3-tf	.311
58	ViT_B_32_imagenet1k	.309
59	resnet152_imagenet_full	.309
60	cvt_cvt-21-224-in1k_4_LucyV4	.309
61	inception_v3_pytorch	.300
62	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.299
63	convnext_tiny:in12k_ft_in1k	.297
64	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.294
65	resnet50_ewconplus_wzc	.293
66	xception	.292
67	resnet-50_v2-tf	.292
68	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.284
69	convnext_large_imagenet_full_seed-0	.284
70	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.282
71	convnext_base_imagenet_full_seed-0	.278
72	resnet50-SIN_IN	.272
73	resnet_101_v1	.271
74	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.271
75	nasnet_large-tf	.266
76	resnet-152_v2_pytorch	.260
77	resnet_152_v1	.255
78	inception_v4	.254
79	resnet50_byol	.254
80	AlexNet_SIN_fov4	.253
81	antialias-resnet152	.253
82	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.250
83	resnet50_imagenet_full	.244
84	resnet101_imagenet_full	.242
85	antialiased-rnext101_32x8d	.240
86	AdvProp_efficientnet-b7	.238
87	resnet_50_v1_spiking	.237
88	densenet-201	.236
89	densenet_201_pytorch	.236
90	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.235
91	custom_model_cv_18_dagger_408	.235
92	cvt_cvt-13-224-in1k_4_LucyV4	.233
93	resnet50	.233
94	densenet-201-keras	.233
95	resnet34_imagenet_full	.233
96	AT_efficientnet-b2	.232
97	AdvProp_efficientnet-b2	.232
98	resnet50-moclr8deg	.229
99	tv_efficientnet-b1	.229
100	efficientnet-b7	.228
101	resnet50-SIN_IN_IN	.228
102	resnet_SIN_IN_FT_IN	.228
103	resnet50_tutorial	.226
104	resnet_50_v1	.226
105	resnet-50-pytorch	.226
106	resnet50-sup	.226
107	resnet50_random_l2_perturb	.220
108	AdvProp_efficientnet-b6	.219
109	Res2Net50_26w_4s	.218
110	densenet-121-keras	.213
111	AdvProp_efficientnet-b4	.213
112	AT_efficientnet-b4	.213
113	densenet-121	.212
114	mobilenet_v2_0_5_224	.211
115	mobilenet_v2_1-4_224_pytorch	.210
116	mobilenet_v2_1_4_224	.210
117	inception_v4-tf	.210
118	alexnet_fov4	.208
119	grcnn_109	.208
120	convnext_small_imagenet_full_seed-0	.206
121	voneresnet-50-non_stochastic	.204
122	ReAlnet04	.204
123	texture_shape_resnet50_trained_on_SIN	.202
124	resnet-152_v1-tf	.196
125	eBarlow_lmda_02_1000ep	.195
126	resnet50_moco_v2	.191
127	cornet_s	.191
128	nasnet_mobile	.190
129	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.189
130	mobilenet_v2_1.4_224-tf	.189
131	resnet-101_v1-tf	.189
132	BiT-S-R152x4	.189
133	vgg_16	.188
134	ReAlnet03	.187
135	resnet18_imagenet_full	.186
136	ReAlnet09	.184
137	antialiased-r50	.182
138	konkle_alexnetgn_ipcl_ref12_supervised_ipcl_aug	.181
139	ReAlnet01	.181
140	deit_large_imagenet_full_seed-0	.181
141	resnet-50_v1-tf	.180
142	ReAlnet10	.179
143	grcnn	.179
144	resnet50_random_linf8_perturb	.177
145	BiT-S-R101x1	.177
146	blt_vs	.177
147	CORnet-S	.175
148	resnet50_linf_4_robust	.175
149	ReAlnet08	.173
150	resnet50_linf_8_robust	.173
151	BiT-S-R50x1	.173
152	mobilenet_v2_1_0_224	.171
153	inception_v1	.167
154	densenet-169	.166
155	ReAlnet06	.166
156	mobilenet_v1_0.75_224	.165
157	texture_shape_alexnet_trained_on_SIN	.164
158	mobilenet_v2_1_0_160	.164
159	BiT-S-R50x3	.163
160	resnet-18	.163
161	resnet50-vitoimagevidnet8	.162
162	voneresnet-50-robust	.162
163	resnet50_eMMCR_Vanilla	.160
164	inception_v2-tf	.160
165	deit_base_imagenet_full_seed-0	.160
166	AdvProp_efficientnet-b8	.159
167	ReAlnet05	.157
168	regnet_y_400mf	.157
169	mobilenet_v1_0.5_192	.157
170	vonealexnet_gaussian_noise_std4_fixed	.157
171	BiT-S-R152x2	.156
172	mobilenet_v1_1.0_224	.156
173	resnet50_l2_3_robust	.155
174	alexnet_reduced_aliasing_early_checkpoint	.155
175	inception_v1-tf	.154
176	alexnet_training_seed_03	.153
177	mobilenet_v2_1.0_224-tf	.153
178	ReAlnet07	.153
179	mobilenet_v2_1_3_224	.153
180	alexnet_random_linf8_perturb	.153
181	ReAlnet02	.153
182	mobilenet_v2_0.5_224-tf	.153
183	vonegrcnn_52e_full	.153
184	mobilenet_v2_1.3_224-tf	.152
185	resnet50_simclr	.152
186	densenet-169-keras	.149
187	mobilenet_v1_1.0_192	.147
188	vgg-16-keras	.146
189	0.5x_resnet-18_LC_w_sh_1_iter	.146
190	squeezenet1_1	.146
191	efficientnet_b1_imagenet_full	.145
192	vonegrcnn_47e	.144
193	resnet50_robust_l2_eps1	.144
194	resnet50-SIN	.144
195	efficientnet_b2_imagenet_full	.144
196	BiT-S-R101x3	.144
197	vgg_19	.142
198	alexnet_random_l2_3_perturb	.142
199	mobilenet_v2_0.5_96	.141
200	mobilenet_v1_1.0_128	.140
201	mobilenet_v2_1.0_160-tf	.137
202	vonegrcnn_62e_nobn	.137
203	mobilenet_v1_0.75_192	.136
204	resnet-34	.136
205	mobilenet_v2_1_0_128	.135
206	AdvProp_efficientnet-b0	.135
207	alexnet_early_checkpoint	.135
208	efficientnet_b0_imagenet_full	.134
209	mobilenet_v2_0.35_128	.134
210	convnext_small_imagenet_100_seed-0	.133
211	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.133
212	mobilenet_v2_0.35_96	.133
213	konkle_alexnetgn_ipcl_ref01_primary_model	.132
214	mobilenet_v2_1.0_128-tf	.132
215	mobilenet_v1_0.25_224	.132
216	mobilenet_v2_0.75_224-tf	.132
217	AlexNet_SIN	.132
218	vonegrcnn_52e_full	.132
219	resnet-50-robust	.131
220	imagenet_l2_3_0	.131
221	resnet50_robust_l2_eps3	.131
222	mobilenet_v2_1_0_192	.130
223	alexnet_ks_torevert	.128
224	alexnet	.128
225	mobilenet_v2_0.5_192-tf	.128
226	resnet50_imagenet_100_seed-0	.127
227	vgg-19-keras	.127
228	vonegrcnn_62e_nobn	.127
229	mobilenet_v2_0_75_224	.125
230	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.125
231	mobilenet_v2_1.0_192-tf	.125
232	shufflenet_v2_x1_0	.123
233	mobilenet_v1_0.75_128	.122
234	voneresnet-50	.121
235	vonegrcnn_47e	.121
236	mobilenet_v2_0.75_96	.119
237	mobilenet_v2_0.5_160	.117
238	mobilenet_v1_1.0_160	.114
239	mobilenet_v1_0.25_192	.112
240	convnext_tiny_sup	.112
241	alexnet_l2_3_robust	.110
242	squeezenet1_0	.110
243	mobilenet_v2_0.5_128	.109
244	mobilenet_v2_0_75_192	.107
245	mobilenet_v2_0_5_192	.106
246	AlexNet_SIN_fov12	.104
247	mobilenet_v2_0.35_160	.102
248	mobilenet_v2_0.35_192	.102
249	mobilenet_v1_0.5_128	.102
250	mobilenet_v2_0.75_160-tf	.101
251	nasnet_mobile-tf	.101
252	mobilenet_v2_0_75_160	.098
253	mobilenet_v1_0.25_160	.098
254	mobilenet_v1_0.75_160	.097
255	deit_small_imagenet_full_seed-0	.096
256	mobilenet_v2_0.75_128	.096
257	convnext_small_imagenet_1_seed-0	.096
258	mobilenet_v2_0.35_224	.093
259	convnext_small_imagenet_10_seed-0	.091
260	deit_small_imagenet_10_seed-0	.090
261	mobilenet_v2_0.75_192-tf	.090
262	alexnet_fov12	.088
263	deit_small_imagenet_1_seed-0	.087
264	AlexNet_SIN_fov16	.083
265	mobilenet_v1_0.5_224	.083
266	resnet_50_v1_spiking_l4a	.081
267	mobilevit_small	.079
268	mobilenet_v2_1.0_96	.078
269	alexnet_training_seed_02	.068
270	alexnet_training_seed_04	.068
271	alexnet_training_seed_08	.068
272	alexnet_training_seed_10	.068
273	alexnet_training_seed_09	.067
274	alexnet_training_seed_07	.066
275	resnet50-meshes-lt-100-original-pretrained	.066
276	alexnet_robust_correct	.063
277	resnet_50_v1_spiking_l4	.060
278	mobilenet_v1_0.25_128	.059
279	mobilenet_v1_0.5_160	.055
280	barlow-twins-resnet50	.053
281	resnet50-meshes-lt-100-original-scratch	.052
282	alexnet_training_seed_06	.050
283	resnet50-vicreg	.049
284	alexnet_fov16	.047
285	resnet50-barlow	.046
286	alexnet_linf_8_robust	.045
287	imagenet_l2_10_0	.034
288	resnet_50_v1_spiking_l3	.034
289	resnet50_FractalDB	.032
290	resnet50-vicregl0p9	.032
291	resnet-50_untrained	.031
292	deit_small_imagenet_100_seed-0	.028
293	artResNet18_1	.013
294	resnet50_imagenet_1_seed-0	.011
295	resnet50-simclr-vissl	.011
296	resnet50-vicregl0p75	.011
297	resnet50_imagenet_10_seed-0	.003
298	alexnet_training_seed_05	.003
299	alexnet_training_seed_01	.003
300	alexnet_training_seed_01_fov4	.003
301	alexnet_training_seed_01_fov12	.003
302	alexnet_training_seed_01_fov16	.003
303	resnet-18-LC_w_sh_100_iter_conv_init_m
304	ViT-B/32
305	BiT-M-R50x3
306	BiT-M-R50x1
307	BiT-M-R152x2
308	resnet-18-LC_w_sh_1_iter_conv_init_m
309	resnet-18-LC_w_sh_1_iter_m
310	resnet-18-LC_w_sh_10_iter_m
311	yudixie_resnet50_random_0_240908
312	resnet-18-LC_w_sh_100_iter_m
313	resnet-18-LC_w_sh_10_iter_conv_init_m
314	FrankRobWobv0
315	resnet18-local_aggregation
316	0.5x_resnet-18_LC
317	CORnetZ_CIFAR10
318	resnet18-deepcluster
319	r3m_resnet18
320	resnet18-colorization
321	CORnet-Z
322	resnet18-instance_recognition
323	my-model
324	vggface
325	0.5x_resnet-18_LC_w_sh_10_iter
326	r3m_resnet50
327	r3m_resnet34
328	resnet-18-LC_m
329	r3m_resnet50_nocrop
330	resnet18-supervised
331	resnet-18-LC_1st_conv_m
332	resnet-18-LC_conv_init_m
333	resnet18-contrastive_multiview
334	0.5x_resnet-18
335	RN50
336	0.5x_resnet-18_LC_w_sh_100_iter
337	RN50
338	resnet18-relative_position
339	resnet18-autoencoder
340	resnet18-simclr
341	ViT_L_32
342	ViT_L_16_imagenet1k
343	ViT_B_32
344	resnet18-depth_prediction
345	ViT_B_16
346	r3m_resnet34_nocrop
347	resnet18-contrastive_predictive
348	omnivore_swinT
349	resnet-18-LC_untrained
350	resnet-18_untrained
351	resnet-50x2_untrained
352	dcgan
353	v1-pyr-nodown
354	unet_entire
355	CORnetZ_CIFAR10_bs32_20_04
356	pixels
357	dorinet_cornet_z
358	my-model
359	resnet50
360	openclip
361	resnet50-cifar
362	resnet50_pretrained_with_retinal_waves
363	resnet50_ImageNet
364	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
365	yudixie_resnet50_imagenet1kpret_0_240908
366	yudixie_resnet50_imagenet1kpret_0_240312
367	yudixie_resnet50_cat_obj_class_all_latents_0_240908
368	yudixie_resnet50_object_class_0_240908
369	yudixie_resnet50_category_class_0_240908
370	resnet18_imagenet21kP
371	ConvLSTM
372	PredRNN
373	yudixie_resnet50_distance_reg_0_240908
374	SimVP
375	briaai_rmbg_1_4
376	TAU
377	MIM
378	hmax
379	ReAlnet01_cornet
380	yolos_tiny
381	yudixie_resnet18_translation_reg_0_240719
382	ReAlnet02_cornet
383	ReAlnet03_cornet
384	yudixie_resnet18_distance_reg_0_240719
385	yudixie_resnet18_rotation_reg_0_240719
386	yudixie_resnet18_imagenet1kpret_0_240719
387	compact_resnet50_V4
388	vit_base_patch14_dinov2-lvd142m
389	vit_base_patch16_dinov3-lvd1689m
390	vit_base_patch14_reg4_dinov2-lvd142m
391	resnet18_ecoset_full
392	resnet34_ecoset_full
393	ReAlnet01_cornet_fov4
394	resnet50_ecoset_full
395	compact_resnet50_robust_V4
396	resnet101_ecoset_full
397	yudixie_resnet50_distance_rotation_0_240908
398	yudixie_resnet50_rotation_reg_0_240908
399	ReAlnet01_cornet_fov12
400	resnet152_ecoset_full
401	yudixie_resnet18_distance_translation_0_240719
402	ReAlnet04_cornet
403	yudixie_resnet50_distance_translation_0_240908
404	yudixie_resnet18_translation_rotation_0_240719
405	yudixie_resnet18_distance_rotation_0_240719
406	compact_vgg19_V4
407	yudixie_resnet18_category_class_0_240719
408	ReAlnet05_cornet
409	yudixie_resnet18_cat_obj_class_all_latents_0_240719
410	resnet-18_test_m
411	yudixie_resnet18_distance_translation_rotation_0_240719
412	compact_resnet50_IT
413	SeLaVi-AVE
414	ReAlnet06_cornet
415	r3d_18
416	compact_vgg19_IT
417	ReAlnet01_cornet_fov16
418	ReAlnet01_ALL
419	mc3_18
420	ReAlnet07_cornet
421	yudixie_resnet50_translation_rotation_0_240908
422	s3d
423	mvit_v1_b
424	ReAlnet08_cornet
425	ReAlnet02_ALL
426	ReAlnet03_ALL
427	aughost_fair_resnet18
428	compact_cornets_V4
429	VOneCORnet-S
430	ReAlnet04_ALL
431	ReAlnet09_cornet
432	SeLaVi-Kinetics400
433	SeLaVi-VGG-Sound
434	omnivore_swinB
435	compact_cornets_IT
436	SeLaVi-Kinetics-Sound
437	ReAlnet06_ALL
438	mvit_v2_s
439	omnivore_swinS
440	r2plus1d_18
441	compact_resnet50_robust_IT
442	ReAlnet10_cornet
443	ReAlnet07_ALL
444	yudixie_resnet18_object_class_0_240719
445	ReAlnet05_ALL
446	ReAlnet08_ALL
447	yudixie_resnet18_random_0_240719
448	ReAlnet09_ALL
449	yudixie_resnet50_translation_reg_0_240908
450	resnet50_primary_visual_cortex
451	yudixie_resnet50_distance_translation_rotation_0_240908
452	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

0.33.

Note that scores are relative to this ceiling.

Data: Geirhos2021cueconflict

Metric: error_consistency