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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	mehrer2020_alexnet_training_seed_05	.356
80	mobilenet_v2_0.5_128	.350
81	blt_vs	.350
82	mobilenet_v2_0_5_224	.350
83	mobilenet_v2_1_3_224	.350
84	mobilenet_v2_0.35_160	.344
85	imagenet_l2_10_0	.344
86	mobilenet_v2_0_75_224	.344
87	mobilenet_v2_0_75_192	.344
88	convnext_tiny_imagenet_full_seed-0	.344
89	inception_v4-tf	.338
90	mobilenet_v1_0.25_160	.338
91	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.338
92	mehrer2020_alexnet_training_seed_03	.338
93	mobilenet_v2_0.35_224	.331
94	mobilenet_v2_1.4_224-tf	.331
95	resnet152_imagenet_full	.331
96	convnext_femto_ols:d1_in1k	.331
97	mehrer2020_alexnet_training_seed_01	.331
98	mehrer2020_alexnet_training_seed_01_fov12	.331
99	mehrer2020_alexnet_training_seed_01_fov4	.331
100	mehrer2020_alexnet_training_seed_01_fov16	.331
101	seresnext50_32x4d	.331
102	BiT-S-R101x3	.325
103	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.325
104	densenet-169-keras	.325
105	mobilenet_v1_0.75_192	.325
106	resnet50_linf_8_robust	.325
107	efficientnet_b0_imagenet_full	.325
108	mobilenet_v2_1-4_224_pytorch	.325
109	resnet18_imagenet_full	.325
110	mobilenet_v2_1_4_224	.325
111	densenet-169	.325
112	mobilenet_v2_0.75_128	.319
113	mobilenet_v2_0.35_192	.319
114	convnext_tiny_sup	.319
115	antialiased-rnext101_32x8d	.319
116	BiT-S-R101x1	.312
117	mobilenet_v2_0.5_192-tf	.312
118	mobilenet_v1_0.5_128	.312
119	resnet50_ewconplus_wzc	.312
120	efficientnet_b1_imagenet_full	.312
121	cvt_cvt-13-384-in1k_4_LucyV4	.312
122	mehrer2020_alexnet_training_seed_04	.312
123	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.312
124	mobilenet_v2_1.0_192-tf	.306
125	mobilenet_v1_0.5_192	.306
126	mobilenet_v2_0.35_96	.306
127	resnet50_l2_3_robust	.306
128	inception_v4	.306
129	resnet50_st_wzc_seed7_2_323	.306
130	resnet50_ewconplus_wzc_seed5_2__323	.306
131	mobilenet_v2_1.0_160-tf	.300
132	mobilenet_v2_1.0_224-tf	.300
133	mobilenet_v2_0_5_192	.300
134	mobilenet_v2_0_75_160	.300
135	mobilenet_v2_1_0_192	.300
136	convnext_small_imagenet_100_seed-0	.300
137	mobilenet_v2_0.5_160	.294
138	alexnet	.294
139	mobilenet_v1_1.0_192	.294
140	mobilenet_v1_0.5_160	.294
141	alexnet_ks_torevert	.294
142	alexnet_fov4	.294
143	alexnet_fov12	.294
144	alexnet_fov16	.294
145	resnet101_imagenet_full	.294
146	cvt_cvt-13-224-in1k_4_LucyV4	.294
147	mobilenet_v2_1_0_224	.294
148	mehrer2020_alexnet_training_seed_02	.294
149	mobilenet_v2_1.0_128-tf	.287
150	mobilenet_v2_0.75_160-tf	.287
151	resnet_50_v2	.287
152	resnet-50-robust	.281
153	resnet-101_v2-tf	.281
154	resnet-152_v2-tf	.281
155	mobilenet_v2_0.35_128	.281
156	alexnet_l2_3_robust	.281
157	imagenet_l2_3_0	.281
158	resnet50_robust_l2_eps3	.281
159	resnet50-vitoimagevidnet8	.281
160	mobilenet_v1_0.5_224	.275
161	mobilenet_v1_1.0_160	.275
162	alexnet_early_checkpoint	.275
163	ReAlnet07	.275
164	inception_v3	.275
165	resnet50_ewconplus_wzc_seed2_2__323	.275
166	mobilenet_v2_0.75_96	.269
167	mobilenet_v1_1.0_128	.269
168	mobilenet_v1_1.0_224	.269
169	resnet50_st_wzc_2	.269
170	mobilenet_v2_1_0_160	.269
171	inception_v3_pytorch	.269
172	deit_large_imagenet_full_seed-0	.269
173	efficientnet_b2_imagenet_full	.269
174	nasnet_large-tf	.263
175	inception_v3-tf	.263
176	BiT-S-R50x3	.263
177	mobilenet_v1_0.75_224	.263
178	mobilenet_v1_0.25_224	.263
179	voneresnet-50-robust	.263
180	alexnet_random_linf8_perturb	.263
181	resnet34_imagenet_full	.263
182	resnet50_imagenet_full	.263
183	BiT-S-R152x2	.256
184	mobilenet_v1_0.25_192	.256
185	grcnn	.256
186	resnet50_hvs_wzc	.256
187	resnet50_st_wzc_seed6_2_323	.256
188	CORnet-S	.250
189	inception_v1-tf	.250
190	mobilenet_v2_1.0_96	.250
191	mobilenet_v1_0.75_128	.250
192	mobilenet_v1_0.75_160	.250
193	mobilenet_v1_0.25_128	.250
194	resnet-152_v2_pytorch	.250
195	pnasnet_large_pytorch	.250
196	nasnet_large	.250
197	resnet50_simsiam_linhead	.250
198	densenet-121-keras	.244
199	BiT-S-R50x1	.244
200	resnet-152_v1-tf	.244
201	resnet-50_v2-tf	.244
202	vgg-19-keras	.244
203	xception-keras	.244
204	cv_18_dagger_408_pretrained	.244
205	custom_model_cv_18_dagger_408	.244
206	cvt_cvt-21-384-in1k_4_LucyV4	.244
207	densenet-121	.244
208	resnet50_barlowtwins_linhead	.244
209	vgg-16-keras	.237
210	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.237
211	resnet_101_v2	.237
212	resnet_101_v1	.237
213	ReAlnet03	.237
214	pnasnet_large-tf	.231
215	resnet50_moco_v2	.231
216	resnet50-VITO-8deg-cc	.231
217	resnet50_byol_linhead	.231
218	nasnet_mobile-tf	.225
219	ReAlnet04	.225
220	deit_base_imagenet_full_seed-0	.225
221	nasnet_mobile	.225
222	resnet50_ewconplus_wzc_seed6_2_323	.225
223	mobilenet_v2_0.5_96	.219
224	resnet50-SIN_IN	.219
225	BiT-S-R152x4	.219
226	alexnet_robust_correct	.219
227	resnet50-moclr8deg	.219
228	ReAlnet08	.219
229	ReAlnet09	.219
230	deit_small_imagenet_full_seed-0	.219
231	inception_v1	.219
232	resnet50	.212
233	antialias-resnet152	.212
234	pnasnet_large_halfPrecision	.212
235	pnasnet_large	.212
236	ReAlnet02	.212
237	resnet50_robust_l2_eps1	.206
238	resnet_152_v1	.206
239	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.200
240	ViT_L_32_imagenet1k	.200
241	resnet-18	.200
242	ReAlnet10	.200
243	resnet50_vicreg_linhead	.200
244	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.194
245	alexnet_linf_8_robust	.194
246	regnet_y_400mf	.194
247	Res2Net50_26w_4s	.194
248	resnet50_eMMCR_Vanilla	.194
249	resnet_SIN_IN_FT_IN	.188
250	vonegrcnn_47e	.188
251	resnet50-SIN_IN_IN	.188
252	inception_v2-tf	.188
253	vgg_19	.188
254	xception	.188
255	vonegrcnn_62e_nobn	.181
256	ReAlnet06	.181
257	ReAlnet01	.181
258	vonegrcnn_47e	.181
259	eicircuit_resnet18_bn_20260919	.181
260	resnet-50_v1-tf	.175
261	vonegrcnn_52e_full	.169
262	resnet-101_v1-tf	.169
263	voneresnet-50	.169
264	vonegrcnn_62e_nobn	.169
265	vonegrcnn_52e_full	.169
266	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.163
267	0.5x_resnet-18_LC_w_sh_1_iter	.163
268	resnet-34	.163
269	deit_small_imagenet_100_seed-0	.156
270	ReAlnet05	.156
271	resnet50_simclr_linhead_01norm	.156
272	squeezenet1_0	.150
273	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.150
274	resnet_152_v2	.150
275	squeezenet1_1	.144
276	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.144
277	vgg_16	.144
278	resnet50-vicregl0p75	.144
279	ijepa_vith14_linhead	.144
280	resnet-50-pytorch	.138
281	resnet50-sup	.138
282	antialiased-r50	.138
283	resnet_50_v1_spiking	.138
284	resnet50_tutorial	.138
285	resnet_50_v1	.138
286	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.131
287	voneresnet-50-non_stochastic	.125
288	ViT_B_16_imagenet1k	.125
289	resnet50_imagenet_10_seed-0	.119
290	ViT_B_32_imagenet1k	.106
291	resnet_50_v1_spiking_l4	.106
292	resnet_50_v1_spiking_l4a	.106
293	shufflenet_v2_x1_0	.100
294	alexnet_training_seed_09	.094
295	resnet50-simclr-vissl	.087
296	barlow-twins-resnet50	.081
297	resnet50_imagenet_100_seed-0	.081
298	convnext_small_imagenet_10_seed-0	.069
299	alexnet_training_seed_04	.069
300	alexnet_training_seed_07	.069
301	alexnet_training_seed_01	.069
302	alexnet_training_seed_01_fov4	.069
303	alexnet_training_seed_01_fov12	.069
304	alexnet_training_seed_01_fov16	.069
305	resnet-50_untrained	.062
306	mobilevit_small	.062
307	alexnet_training_seed_02	.062
308	alexnet_training_seed_08	.062
309	alexnet_training_seed_10	.062
310	deit_small_imagenet_10_seed-0	.062
311	convnext_small_imagenet_1_seed-0	.062
312	resnet50_imagenet_1_seed-0	.062
313	deit_small_imagenet_1_seed-0	.062
314	alexnet_training_seed_03	.062
315	alexnet_training_seed_05	.056
316	alexnet_training_seed_06	.056
317	resnet_50_v1_spiking_l3	.050
318	artResNet18_1	.044
319	resnet50-barlow	.037
320	resnet50-vicregl0p9	.037
321	ViT-B/32
322	resnet-18-LC_w_sh_100_iter_conv_init_m
323	BiT-M-R50x1
324	resnet-18-LC_w_sh_1_iter_conv_init_m
325	BiT-M-R152x2
326	resnet-18-LC_w_sh_1_iter_m
327	resnet-18-LC_w_sh_10_iter_m
328	BiT-M-R50x3
329	resnet-18-LC_w_sh_100_iter_m
330	resnet-18-LC_w_sh_10_iter_conv_init_m
331	FrankRobWobv0
332	0.5x_resnet-18_LC
333	CORnetZ_CIFAR10
334	resnet18-local_aggregation
335	r3m_resnet18
336	resnet18-deepcluster
337	CORnet-Z
338	vggface
339	my-model
340	resnet18-instance_recognition
341	0.5x_resnet-18_LC_w_sh_10_iter
342	r3m_resnet50
343	r3m_resnet34
344	resnet-18-LC_m
345	resnet18-supervised
346	r3m_resnet50_nocrop
347	resnet-18-LC_1st_conv_m
348	resnet-18-LC_conv_init_m
349	resnet18-contrastive_multiview
350	0.5x_resnet-18
351	RN50
352	0.5x_resnet-18_LC_w_sh_100_iter
353	RN50
354	resnet18-colorization
355	resnet18-relative_position
356	resnet18-autoencoder
357	resnet18-simclr
358	ViT_L_32
359	ViT_B_32
360	resnet18-depth_prediction
361	ViT_B_16
362	r3m_resnet34_nocrop
363	resnet18-contrastive_predictive
364	resnet-18-LC_untrained
365	resnet-18_untrained
366	resnet-50x2_untrained
367	dcgan
368	unet_entire
369	CORnetZ_CIFAR10_bs32_20_04
370	v1-pyr-nodown
371	pixels
372	dorinet_cornet_z
373	omnivore_swinT
374	omnivore_swinS
375	openclip
376	vonealexnet_gaussian_noise_std4_fixed
377	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
378	yudixie_resnet50_category_class_0_240908
379	yudixie_resnet50_distance_reg_0_240908
380	ConvLSTM
381	PredRNN
382	SimVP
383	TAU
384	briaai_rmbg_1_4
385	MIM
386	yolos_tiny
387	ReAlnet01_cornet
388	compact_resnet50_V4
389	ReAlnet02_cornet
390	yudixie_resnet18_rotation_reg_0_240719
391	yudixie_resnet50_rotation_reg_0_240908
392	eBarlow_lmda_02_1000ep
393	vit_base_patch14_dinov2-lvd142m
394	vit_base_patch14_reg4_dinov2-lvd142m
395	yudixie_resnet50_distance_rotation_0_240908
396	compact_resnet50_IT
397	yudixie_resnet50_distance_translation_0_240908
398	ReAlnet01_cornet_fov4
399	resnet18_ecoset_full
400	VOneCORnet-S
401	resnet101_ecoset_full
402	ReAlnet01_cornet_fov12
403	compact_vgg19_V4
404	vit_base_patch16_dinov3-lvd1689m
405	resnet152_ecoset_full
406	ReAlnet03_cornet
407	yudixie_resnet18_distance_translation_0_240719
408	resnet-18_test_m
409	yudixie_resnet18_distance_rotation_0_240719
410	yudixie_resnet18_category_class_0_240719
411	yudixie_resnet50_translation_reg_0_240908
412	compact_resnet50_robust_V4
413	yudixie_resnet18_translation_reg_0_240719
414	yudixie_resnet18_distance_translation_rotation_0_240719
415	compact_vgg19_IT
416	SeLaVi-AVE
417	hmax
418	ReAlnet06_cornet
419	ReAlnet01_cornet_fov16
420	yudixie_resnet50_translation_rotation_0_240908
421	ReAlnet01_ALL
422	r3d_18
423	yudixie_resnet18_distance_reg_0_240719
424	ReAlnet02_ALL
425	mc3_18
426	s3d
427	omnivore_swinB
428	resnet50-vicreg
429	ReAlnet08_cornet
430	mvit_v1_b
431	compact_cornets_V4
432	ReAlnet03_ALL
433	ReAlnet04_ALL
434	resnet18_imagenet21kP
435	ReAlnet09_cornet
436	compact_cornets_IT
437	SeLaVi-Kinetics400
438	ReAlnet05_ALL
439	SeLaVi-VGG-Sound
440	SeLaVi-Kinetics-Sound
441	ReAlnet06_ALL
442	compact_resnet50_robust_IT
443	ReAlnet10_cornet
444	yudixie_resnet50_imagenet1kpret_0_240908
445	yudixie_resnet50_distance_translation_rotation_0_240908
446	ReAlnet07_ALL
447	r2plus1d_18
448	mvit_v2_s
449	resnet50_ecoset_full
450	ReAlnet08_ALL
451	yudixie_resnet18_object_class_0_240719
452	yudixie_resnet50_cat_obj_class_all_latents_0_240908
453	yudixie_resnet18_cat_obj_class_all_latents_0_240719
454	yudixie_resnet18_random_0_240719
455	ReAlnet09_ALL
456	yudixie_resnet18_translation_rotation_0_240719
457	yudixie_resnet50_imagenet1kpret_0_240312
458	yudixie_resnet18_imagenet1kpret_0_240719
459	resnet50_primary_visual_cortex
460	yudixie_resnet50_object_class_0_240908
461	ReAlnet10_ALL
462	yudixie_resnet50_random_0_240908
463	ReAlnet04_cornet
464	ReAlnet05_cornet
465	ReAlnet07_cornet
466	resnet34_ecoset_full
467	resnet18_fair
468	pr1_stl10_disentangled_alpha04
469	predictive_coding_pc
470	yudixie_wbh_object_class_scaling_0415_run_0
471	yudixie_wbh_object_class_scaling_0415_run_3
472	yudixie_wbh_object_class_scaling_0415_imagenet
473	yudixie_wbh_object_class_scaling_0415_run_2
474	yudixie_wbh_object_class_scaling_0415_run_1
475	deepeisnn_ei_cifar10_vgg19
476	deepeisnn_ei_cifar10_resnet18
477	deepeisnn_ei_imagenet_resnet18
478	dinov2_vitl14_reg_lc_featread
479	gabor_filter_entropy_4x4
480	gabor_filter_entropy_4x4_v2
481	gabor_filter_entropy_4x4_v2_forcedCosine
482	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