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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021edge-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	convnext_xxlarge_clip	.969
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.963
3	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.944
4	dinov2_vitg14_reg_lc	.938
5	eva02_large_in22k	.931
6	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.925
7	vit_large_patch14_clip_224:laion2b_ft_in1k	.925
8	vit_huge_clip336_laion	.919
9	dinov2_vitg14_lc	.912
10	vit_large_patch14_clip_336:laion2b_ft_in1k	.906
11	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.900
12	convnext_large_clip_laion2b_finelayers	.900
13	vit_large_patch14_clip_224:openai_ft_in1k	.894
14	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.894
15	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.894
16	vit_large_clip336_openai	.887
17	resnext101_32x32d_wsl	.881
18	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.881
19	convnext_base:clip_laiona_augreg_ft_in1k_384	.863
20	convnext_large:fb_in22k_ft_in1k	.844
21	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.825
22	dinov2_vitl14_lc_res336	.825
23	convnext_xlarge:fb_in22k_ft_in1k	.812
24	dinov2_vitl14_reg_lc	.806
25	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.800
26	convnext_base_fb_in22k_ft_in1k	.787
27	convnext_large_in22k_supervised	.787
28	resnext101_32x48d_wsl	.775
29	resnext101_32x8d_wsl	.769
30	resnext101_32x16d_wsl	.762
31	vit_base_patch16_clip_224:openai_ft_in1k	.756
32	fixres_resnext101_32x48d_wsl	.731
33	effnetv2m_custom384	.719
34	dinov2_vitl14_lc	.706
35	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.700
36	convnext_tiny:in12k_ft_in1k	.694
37	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.669
38	AdvProp_efficientnet-b7	.662
39	convnext_base_fb_in1k	.637
40	deit3_base_patch16_224_fb_in22k_ft_in1k	.625
41	AdvProp_efficientnet-b4	.600
42	AdvProp_efficientnet-b6	.600
43	AT_efficientnet-b4	.600
44	AlexNet_SIN	.581
45	AlexNet_SIN_fov4	.581
46	AlexNet_SIN_fov12	.581
47	AlexNet_SIN_fov16	.581
48	AdvProp_efficientnet-b8	.575
49	effnetb1_272x240	.569
50	convnext_base_fb_in1k_gram_aligned_dinov3	.531
51	tv_efficientnet-b1	.506
52	grcnn_109	.506
53	CLIP-RN50	.481
54	resnet50-SIN	.469
55	AT_efficientnet-b2	.469
56	AdvProp_efficientnet-b2	.469
57	vit_relpos_base_patch16_clsgap_224:sw_in1k	.438
58	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.438
59	deit3_base_patch16_224_fb_in1k	.438
60	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.425
61	convnext_large_imagenet_full_seed-0	.412
62	convnext_base_imagenet_full_seed-0	.400
63	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.400
64	convnext_small_imagenet_full_seed-0	.394
65	AdvProp_efficientnet-b0	.388
66	focalnet_tiny_lrf_in1k	.388
67	densenet-201-keras	.381
68	densenet-201	.381
69	densenet_201_pytorch	.381
70	regnety_032	.375
71	mobilenet_v2_0.5_224-tf	.369
72	mobilenet_v2_0.75_192-tf	.369
73	efficientnet_b0	.362
74	efficientnet-b7	.362
75	mobilenet_v2_1.3_224-tf	.362
76	mobilenet_v2_0.75_224-tf	.362
77	mobilenet_v2_1_0_128	.362
78	cvt_cvt-21-224-in1k_4_LucyV4	.356
79	mobilenet_v2_0.5_128	.350
80	blt_vs	.350
81	mobilenet_v2_0_5_224	.350
82	mobilenet_v2_1_3_224	.350
83	mobilenet_v2_0.35_160	.344
84	imagenet_l2_10_0	.344
85	mobilenet_v2_0_75_224	.344
86	mobilenet_v2_0_75_192	.344
87	convnext_tiny_imagenet_full_seed-0	.344
88	inception_v4-tf	.338
89	mobilenet_v1_0.25_160	.338
90	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.338
91	mobilenet_v2_0.35_224	.331
92	mobilenet_v2_1.4_224-tf	.331
93	resnet152_imagenet_full	.331
94	convnext_femto_ols:d1_in1k	.331
95	seresnext50_32x4d	.331
96	BiT-S-R101x3	.325
97	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.325
98	densenet-169-keras	.325
99	mobilenet_v1_0.75_192	.325
100	resnet50_linf_8_robust	.325
101	efficientnet_b0_imagenet_full	.325
102	mobilenet_v2_1-4_224_pytorch	.325
103	resnet18_imagenet_full	.325
104	mobilenet_v2_1_4_224	.325
105	densenet-169	.325
106	mobilenet_v2_0.75_128	.319
107	mobilenet_v2_0.35_192	.319
108	convnext_tiny_sup	.319
109	antialiased-rnext101_32x8d	.319
110	BiT-S-R101x1	.312
111	mobilenet_v2_0.5_192-tf	.312
112	mobilenet_v1_0.5_128	.312
113	resnet50_ewconplus_wzc	.312
114	efficientnet_b1_imagenet_full	.312
115	cvt_cvt-13-384-in1k_4_LucyV4	.312
116	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.312
117	mobilenet_v2_1.0_192-tf	.306
118	mobilenet_v1_0.5_192	.306
119	mobilenet_v2_0.35_96	.306
120	resnet50_l2_3_robust	.306
121	inception_v4	.306
122	resnet50_st_wzc_seed7_2_323	.306
123	resnet50_ewconplus_wzc_seed5_2__323	.306
124	mobilenet_v2_1.0_160-tf	.300
125	mobilenet_v2_1.0_224-tf	.300
126	mobilenet_v2_0_5_192	.300
127	mobilenet_v2_0_75_160	.300
128	mobilenet_v2_1_0_192	.300
129	convnext_small_imagenet_100_seed-0	.300
130	mobilenet_v2_0.5_160	.294
131	alexnet	.294
132	mobilenet_v1_1.0_192	.294
133	mobilenet_v1_0.5_160	.294
134	alexnet_ks_torevert	.294
135	alexnet_fov4	.294
136	alexnet_fov12	.294
137	alexnet_fov16	.294
138	resnet101_imagenet_full	.294
139	cvt_cvt-13-224-in1k_4_LucyV4	.294
140	mobilenet_v2_1_0_224	.294
141	mobilenet_v2_1.0_128-tf	.287
142	mobilenet_v2_0.75_160-tf	.287
143	resnet_50_v2	.287
144	resnet-50-robust	.281
145	resnet-101_v2-tf	.281
146	resnet-152_v2-tf	.281
147	mobilenet_v2_0.35_128	.281
148	alexnet_l2_3_robust	.281
149	imagenet_l2_3_0	.281
150	resnet50_robust_l2_eps3	.281
151	resnet50-vitoimagevidnet8	.281
152	mobilenet_v1_0.5_224	.275
153	mobilenet_v1_1.0_160	.275
154	alexnet_early_checkpoint	.275
155	ReAlnet07	.275
156	inception_v3	.275
157	resnet50_ewconplus_wzc_seed2_2__323	.275
158	mobilenet_v2_0.75_96	.269
159	mobilenet_v1_1.0_128	.269
160	mobilenet_v1_1.0_224	.269
161	resnet50_st_wzc_2	.269
162	mobilenet_v2_1_0_160	.269
163	inception_v3_pytorch	.269
164	deit_large_imagenet_full_seed-0	.269
165	efficientnet_b2_imagenet_full	.269
166	nasnet_large-tf	.263
167	inception_v3-tf	.263
168	BiT-S-R50x3	.263
169	mobilenet_v1_0.75_224	.263
170	mobilenet_v1_0.25_224	.263
171	voneresnet-50-robust	.263
172	alexnet_random_linf8_perturb	.263
173	resnet34_imagenet_full	.263
174	resnet50_imagenet_full	.263
175	BiT-S-R152x2	.256
176	mobilenet_v1_0.25_192	.256
177	grcnn	.256
178	resnet50_hvs_wzc	.256
179	resnet50_st_wzc_seed6_2_323	.256
180	CORnet-S	.250
181	inception_v1-tf	.250
182	mobilenet_v2_1.0_96	.250
183	mobilenet_v1_0.75_128	.250
184	mobilenet_v1_0.75_160	.250
185	mobilenet_v1_0.25_128	.250
186	resnet-152_v2_pytorch	.250
187	pnasnet_large_pytorch	.250
188	nasnet_large	.250
189	densenet-121-keras	.244
190	BiT-S-R50x1	.244
191	resnet-152_v1-tf	.244
192	resnet-50_v2-tf	.244
193	vgg-19-keras	.244
194	xception-keras	.244
195	cv_18_dagger_408_pretrained	.244
196	custom_model_cv_18_dagger_408	.244
197	cvt_cvt-21-384-in1k_4_LucyV4	.244
198	densenet-121	.244
199	vgg-16-keras	.237
200	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.237
201	resnet_101_v2	.237
202	resnet_101_v1	.237
203	ReAlnet03	.237
204	pnasnet_large-tf	.231
205	resnet50_moco_v2	.231
206	resnet50-VITO-8deg-cc	.231
207	nasnet_mobile-tf	.225
208	ReAlnet04	.225
209	deit_base_imagenet_full_seed-0	.225
210	nasnet_mobile	.225
211	resnet50_ewconplus_wzc_seed6_2_323	.225
212	mobilenet_v2_0.5_96	.219
213	resnet50-SIN_IN	.219
214	BiT-S-R152x4	.219
215	alexnet_robust_correct	.219
216	resnet50-moclr8deg	.219
217	ReAlnet08	.219
218	ReAlnet09	.219
219	deit_small_imagenet_full_seed-0	.219
220	inception_v1	.219
221	resnet50	.212
222	antialias-resnet152	.212
223	pnasnet_large_halfPrecision	.212
224	pnasnet_large	.212
225	ReAlnet02	.212
226	resnet50_robust_l2_eps1	.206
227	resnet_152_v1	.206
228	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.200
229	ViT_L_32_imagenet1k	.200
230	resnet-18	.200
231	ReAlnet10	.200
232	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.194
233	alexnet_linf_8_robust	.194
234	regnet_y_400mf	.194
235	Res2Net50_26w_4s	.194
236	resnet50_eMMCR_Vanilla	.194
237	resnet_SIN_IN_FT_IN	.188
238	vonegrcnn_47e	.188
239	resnet50-SIN_IN_IN	.188
240	inception_v2-tf	.188
241	vgg_19	.188
242	xception	.188
243	vonegrcnn_62e_nobn	.181
244	ReAlnet06	.181
245	ReAlnet01	.181
246	vonegrcnn_47e	.181
247	eicircuit_resnet18_bn_20260919	.181
248	resnet-50_v1-tf	.175
249	vonegrcnn_52e_full	.169
250	resnet-101_v1-tf	.169
251	voneresnet-50	.169
252	vonegrcnn_62e_nobn	.169
253	vonegrcnn_52e_full	.169
254	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
255	0.5x_resnet-18_LC_w_sh_1_iter	.163
256	resnet-34	.163
257	deit_small_imagenet_100_seed-0	.156
258	ReAlnet05	.156
259	squeezenet1_0	.150
260	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.150
261	resnet_152_v2	.150
262	squeezenet1_1	.144
263	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.144
264	vgg_16	.144
265	resnet50-vicregl0p75	.144
266	resnet-50-pytorch	.138
267	resnet50-sup	.138
268	antialiased-r50	.138
269	resnet_50_v1_spiking	.138
270	resnet50_tutorial	.138
271	resnet_50_v1	.138
272	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.131
273	voneresnet-50-non_stochastic	.125
274	ViT_B_16_imagenet1k	.125
275	resnet50_imagenet_10_seed-0	.119
276	ViT_B_32_imagenet1k	.106
277	resnet_50_v1_spiking_l4	.106
278	resnet_50_v1_spiking_l4a	.106
279	shufflenet_v2_x1_0	.100
280	alexnet_training_seed_09	.094
281	resnet50-simclr-vissl	.087
282	barlow-twins-resnet50	.081
283	resnet50_imagenet_100_seed-0	.081
284	convnext_small_imagenet_10_seed-0	.069
285	alexnet_training_seed_04	.069
286	alexnet_training_seed_07	.069
287	alexnet_training_seed_01	.069
288	alexnet_training_seed_01_fov4	.069
289	alexnet_training_seed_01_fov12	.069
290	alexnet_training_seed_01_fov16	.069
291	resnet-50_untrained	.062
292	mobilevit_small	.062
293	alexnet_training_seed_02	.062
294	alexnet_training_seed_08	.062
295	alexnet_training_seed_10	.062
296	deit_small_imagenet_10_seed-0	.062
297	convnext_small_imagenet_1_seed-0	.062
298	resnet50_imagenet_1_seed-0	.062
299	deit_small_imagenet_1_seed-0	.062
300	alexnet_training_seed_03	.062
301	alexnet_training_seed_05	.056
302	alexnet_training_seed_06	.056
303	resnet_50_v1_spiking_l3	.050
304	artResNet18_1	.044
305	resnet50-barlow	.037
306	resnet50-vicregl0p9	.037
307	ViT-B/32
308	resnet-18-LC_w_sh_100_iter_conv_init_m
309	BiT-M-R50x1
310	resnet-18-LC_w_sh_1_iter_conv_init_m
311	BiT-M-R152x2
312	resnet-18-LC_w_sh_1_iter_m
313	resnet-18-LC_w_sh_10_iter_m
314	BiT-M-R50x3
315	resnet-18-LC_w_sh_100_iter_m
316	resnet-18-LC_w_sh_10_iter_conv_init_m
317	FrankRobWobv0
318	0.5x_resnet-18_LC
319	CORnetZ_CIFAR10
320	resnet18-local_aggregation
321	r3m_resnet18
322	resnet18-deepcluster
323	CORnet-Z
324	vggface
325	my-model
326	resnet18-instance_recognition
327	0.5x_resnet-18_LC_w_sh_10_iter
328	r3m_resnet50
329	r3m_resnet34
330	resnet-18-LC_m
331	resnet18-supervised
332	r3m_resnet50_nocrop
333	resnet-18-LC_1st_conv_m
334	resnet-18-LC_conv_init_m
335	resnet18-contrastive_multiview
336	0.5x_resnet-18
337	RN50
338	0.5x_resnet-18_LC_w_sh_100_iter
339	RN50
340	resnet18-colorization
341	resnet18-relative_position
342	resnet18-autoencoder
343	resnet18-simclr
344	ViT_L_32
345	ViT_B_32
346	resnet18-depth_prediction
347	ViT_B_16
348	r3m_resnet34_nocrop
349	resnet18-contrastive_predictive
350	resnet-18-LC_untrained
351	resnet-18_untrained
352	resnet-50x2_untrained
353	dcgan
354	unet_entire
355	CORnetZ_CIFAR10_bs32_20_04
356	v1-pyr-nodown
357	pixels
358	dorinet_cornet_z
359	omnivore_swinT
360	omnivore_swinS
361	openclip
362	vonealexnet_gaussian_noise_std4_fixed
363	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
364	yudixie_resnet50_category_class_0_240908
365	yudixie_resnet50_distance_reg_0_240908
366	ConvLSTM
367	PredRNN
368	SimVP
369	TAU
370	briaai_rmbg_1_4
371	MIM
372	yolos_tiny
373	ReAlnet01_cornet
374	compact_resnet50_V4
375	ReAlnet02_cornet
376	yudixie_resnet18_rotation_reg_0_240719
377	yudixie_resnet50_rotation_reg_0_240908
378	eBarlow_lmda_02_1000ep
379	vit_base_patch14_dinov2-lvd142m
380	vit_base_patch14_reg4_dinov2-lvd142m
381	yudixie_resnet50_distance_rotation_0_240908
382	compact_resnet50_IT
383	yudixie_resnet50_distance_translation_0_240908
384	ReAlnet01_cornet_fov4
385	resnet18_ecoset_full
386	VOneCORnet-S
387	ReAlnet04_cornet
388	resnet101_ecoset_full
389	ReAlnet01_cornet_fov12
390	compact_vgg19_V4
391	vit_base_patch16_dinov3-lvd1689m
392	resnet152_ecoset_full
393	ReAlnet03_cornet
394	yudixie_resnet18_distance_translation_0_240719
395	resnet-18_test_m
396	yudixie_resnet18_distance_rotation_0_240719
397	ReAlnet05_cornet
398	yudixie_resnet18_category_class_0_240719
399	yudixie_resnet50_translation_reg_0_240908
400	compact_resnet50_robust_V4
401	yudixie_resnet18_translation_reg_0_240719
402	yudixie_resnet18_distance_translation_rotation_0_240719
403	compact_vgg19_IT
404	SeLaVi-AVE
405	hmax
406	ReAlnet06_cornet
407	ReAlnet01_cornet_fov16
408	yudixie_resnet50_translation_rotation_0_240908
409	ReAlnet01_ALL
410	r3d_18
411	yudixie_resnet18_distance_reg_0_240719
412	ReAlnet02_ALL
413	ReAlnet07_cornet
414	mc3_18
415	s3d
416	omnivore_swinB
417	resnet50-vicreg
418	ReAlnet08_cornet
419	mvit_v1_b
420	compact_cornets_V4
421	ReAlnet03_ALL
422	ReAlnet04_ALL
423	resnet18_imagenet21kP
424	ReAlnet09_cornet
425	compact_cornets_IT
426	SeLaVi-Kinetics400
427	ReAlnet05_ALL
428	SeLaVi-VGG-Sound
429	SeLaVi-Kinetics-Sound
430	ReAlnet06_ALL
431	compact_resnet50_robust_IT
432	ReAlnet10_cornet
433	yudixie_resnet50_imagenet1kpret_0_240908
434	yudixie_resnet50_distance_translation_rotation_0_240908
435	ReAlnet07_ALL
436	r2plus1d_18
437	mvit_v2_s
438	resnet34_ecoset_full
439	resnet50_ecoset_full
440	ReAlnet08_ALL
441	yudixie_resnet18_object_class_0_240719
442	yudixie_resnet50_cat_obj_class_all_latents_0_240908
443	yudixie_resnet18_cat_obj_class_all_latents_0_240719
444	yudixie_resnet18_random_0_240719
445	ReAlnet09_ALL
446	yudixie_resnet18_translation_rotation_0_240719
447	yudixie_resnet50_imagenet1kpret_0_240312
448	yudixie_resnet18_imagenet1kpret_0_240719
449	resnet50_primary_visual_cortex
450	yudixie_resnet50_object_class_0_240908
451	ReAlnet10_ALL
452	yudixie_resnet50_random_0_240908
453	resnet18_fair
454	pr1_stl10_disentangled_alpha04
455	predictive_coding_pc
456	yudixie_wbh_object_class_scaling_0415_run_0
457	yudixie_wbh_object_class_scaling_0415_run_3
458	yudixie_wbh_object_class_scaling_0415_imagenet
459	yudixie_wbh_object_class_scaling_0415_run_2
460	yudixie_wbh_object_class_scaling_0415_run_1
461	deepeisnn_ei_cifar10_vgg19
462	deepeisnn_ei_cifar10_resnet18
463	deepeisnn_ei_imagenet_resnet18
464	dinov2_vitl14_reg_lc_featread
465	gabor_filter_entropy_4x4
466	gabor_filter_entropy_4x4_v2
467	gabor_filter_entropy_4x4_v2_forcedCosine
468	gabor_filter_mean_abs_4x4_v2

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

1.00.

Note that scores are relative to this ceiling.

Data: Geirhos2021edge

Metric: top1