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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021silhouette-top1")
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	.800
2	convnext_xxlarge_clip	.794
3	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.756
4	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.731
5	eva02_large_in22k	.731
6	vit_large_patch14_clip_224:laion2b_ft_in1k	.725
7	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.719
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.719
9	vit_large_patch14_clip_224:openai_ft_in1k	.719
10	dinov2_vitl14_lc	.719
11	vit_large_patch14_clip_336:laion2b_ft_in1k	.713
12	dinov2_vitg14_lc	.713
13	dinov2_vitl14_lc_res336	.713
14	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.706
15	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.700
16	vit_huge_clip336_laion	.700
17	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.694
18	vit_large_clip336_openai	.694
19	convnext_large_clip_laion2b_finelayers	.688
20	dinov2_vitg14_reg_lc	.688
21	convnext_base_fb_in22k_ft_in1k	.681
22	deit3_base_patch16_224_fb_in1k	.681
23	convnext_xlarge:fb_in22k_ft_in1k	.669
24	focalnet_tiny_lrf_in1k	.662
25	convnext_tiny:in12k_ft_in1k	.662
26	dinov2_vitl14_reg_lc	.662
27	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.656
28	vit_base_patch16_clip_224:openai_ft_in1k	.656
29	convnext_large:fb_in22k_ft_in1k	.656
30	resnext101_32x8d_wsl	.650
31	AdvProp_efficientnet-b6	.650
32	convnext_large_in22k_supervised	.650
33	resnext101_32x32d_wsl	.644
34	resnext101_32x16d_wsl	.637
35	convnext_base:clip_laiona_augreg_ft_in1k_384	.637
36	convnext_base_fb_in1k	.637
37	resnext101_32x48d_wsl	.631
38	convnext_tiny_imagenet_full_seed-0	.631
39	convnext_large_imagenet_full_seed-0	.631
40	vit_relpos_base_patch16_clsgap_224:sw_in1k	.631
41	AdvProp_efficientnet-b7	.625
42	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.625
43	imagenet_l2_10_0	.613
44	convnext_base_imagenet_full_seed-0	.613
45	regnety_032	.613
46	AdvProp_efficientnet-b4	.606
47	AT_efficientnet-b4	.606
48	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.606
49	pnasnet_large-tf	.600
50	resnet-50-robust	.600
51	effnetv2m_custom384	.600
52	imagenet_l2_3_0	.600
53	resnet50_robust_l2_eps3	.600
54	resnet50_robust_l2_eps1	.600
55	convnext_small_imagenet_full_seed-0	.600
56	deit_large_imagenet_full_seed-0	.600
57	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.600
58	voneresnet-50-robust	.600
59	fixres_resnext101_32x48d_wsl	.594
60	seresnext50_32x4d	.594
61	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.588
62	deit_base_imagenet_full_seed-0	.588
63	alexnet_linf_8_robust	.581
64	blt_vs	.581
65	deit3_base_patch16_224_fb_in22k_ft_in1k	.581
66	nasnet_large-tf	.575
67	vonegrcnn_62e_nobn	.575
68	resnet50_l2_3_robust	.575
69	cv_18_dagger_408_pretrained	.569
70	resnet50_ewconplus_wzc	.569
71	cvt_cvt-13-224-in1k_4_LucyV4	.569
72	resnet50_ewconplus_wzc_seed2_2__323	.569
73	resnet-152_v2-tf	.562
74	effnetb1_272x240	.562
75	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.562
76	convnext_tiny_sup	.562
77	vonegrcnn_52e_full	.562
78	resnet_152_v1	.562
79	resnet50-SIN_IN	.556
80	antialiased-rnext101_32x8d	.556
81	antialias-resnet152	.556
82	ReAlnet09	.556
83	vonegrcnn_52e_full	.550
84	BiT-S-R50x1	.550
85	BiT-S-R152x2	.550
86	resnet-50_v2-tf	.550
87	inception_v2-tf	.550
88	xception-keras	.550
89	AT_efficientnet-b2	.550
90	AdvProp_efficientnet-b2	.550
91	custom_model_cv_18_dagger_408	.550
92	cvt_cvt-21-384-in1k_4_LucyV4	.550
93	vonegrcnn_62e_nobn	.550
94	vonegrcnn_47e	.550
95	resnet-50-pytorch	.544
96	vonegrcnn_47e	.544
97	resnet-152_v1-tf	.544
98	resnet-101_v2-tf	.544
99	resnet50-sup	.544
100	cvt_cvt-21-224-in1k_4_LucyV4	.544
101	ReAlnet10	.544
102	resnet50_st_wzc_seed3_2_323	.544
103	resnet50_tutorial	.544
104	pnasnet_large_halfPrecision	.544
105	deit_small_imagenet_full_seed-0	.544
106	pnasnet_large	.544
107	resnet_50_v1	.544
108	convnext_base_fb_in1k_gram_aligned_dinov3	.544
109	efficientnet-b7	.537
110	AdvProp_efficientnet-b8	.537
111	AdvProp_efficientnet-b0	.537
112	alexnet_robust_correct	.537
113	resnet50_linf_8_robust	.537
114	resnet_101_v2	.537
115	ReAlnet03	.537
116	inception_v4-tf	.531
117	efficientnet_b0	.531
118	resnet-50_v1-tf	.531
119	densenet-169-keras	.525
120	antialiased-r50	.525
121	resnet50_st_wzc_2	.525
122	nasnet_large	.525
123	nasnet_mobile	.525
124	ReAlnet01	.525
125	ReAlnet04	.525
126	densenet-169	.525
127	resnet_50_v2	.525
128	resnet50_hvs_wzc	.525
129	inception_v1-tf	.519
130	BiT-S-R152x4	.519
131	BiT-S-R101x3	.519
132	voneresnet-50	.519
133	alexnet_l2_3_robust	.519
134	ReAlnet07	.519
135	ReAlnet08	.519
136	inception_v3_pytorch	.519
137	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.519
138	tv_efficientnet-b1	.519
139	grcnn_109	.519
140	resnet50_st_wzc_seed6_2_323	.519
141	resnet50_st_wzc_seed7_2_323	.519
142	densenet-201-keras	.512
143	resnet_50_v1_spiking	.512
144	ReAlnet06	.512
145	densenet-201	.512
146	resnet50_ewconplus_wzc_seed5_2__323	.512
147	resnet-152_v2_pytorch	.512
148	densenet_201_pytorch	.512
149	grcnn	.512
150	ReAlnet05	.512
151	mobilenet_v2_0.5_96	.506
152	BiT-S-R50x3	.506
153	resnet-101_v1-tf	.506
154	voneresnet-50-non_stochastic	.506
155	resnet50-SIN	.506
156	Res2Net50_26w_4s	.506
157	cvt_cvt-13-384-in1k_4_LucyV4	.506
158	ReAlnet02	.506
159	inception_v3-tf	.500
160	inception_v3	.500
161	resnet_SIN_IN_FT_IN	.494
162	mobilenet_v2_1.3_224-tf	.494
163	resnet50-SIN_IN_IN	.494
164	mobilenet_v2_0.35_96	.494
165	resnet_101_v1	.494
166	mobilenet_v2_0.75_96	.487
167	BiT-S-R101x1	.487
168	resnet101_imagenet_full	.487
169	inception_v4	.487
170	resnet_152_v2	.487
171	vgg-19-keras	.481
172	CLIP-RN50	.481
173	mobilenet_v2_0.35_128	.481
174	regnet_y_400mf	.481
175	AlexNet_SIN	.481
176	AlexNet_SIN_fov4	.481
177	AlexNet_SIN_fov12	.481
178	AlexNet_SIN_fov16	.481
179	mobilenet_v2_1.0_160-tf	.475
180	vgg-16-keras	.475
181	mobilenet_v2_1.0_96	.475
182	resnet-18	.475
183	mobilenet_v2_0.75_128	.475
184	convnext_femto_ols:d1_in1k	.475
185	mobilenet_v1_0.75_128	.469
186	resnet50-moclr8deg	.469
187	CORnet-S	.463
188	mobilenet_v1_0.75_224	.463
189	mobilenet_v1_1.0_192	.463
190	eicircuit_resnet18_bn_20260919	.463
191	resnet50_ewconplus_wzc_seed6_2_323	.463
192	mobilenet_v2_1.0_192-tf	.456
193	mobilenet_v2_0.75_224-tf	.456
194	mobilenet_v2_1_0_160	.456
195	mobilenet_v2_0.5_160	.450
196	mobilenet_v2_1.0_224-tf	.450
197	mobilenet_v1_1.0_128	.450
198	resnet-34	.450
199	mobilenet_v1_1.0_224	.450
200	resnet50	.450
201	vgg_19	.450
202	mobilenet_v2_1_0_192	.450
203	resnet152_imagenet_full	.450
204	resnet50-vitoimagevidnet8	.450
205	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.450
206	mobilenet_v2_0.5_128	.444
207	efficientnet_b0_imagenet_full	.444
208	mobilenet_v2_0.75_192-tf	.438
209	resnet50_imagenet_full	.438
210	vgg_16	.438
211	mobilenet_v2_0.5_224-tf	.431
212	alexnet	.431
213	resnet50_moco_v2	.431
214	alexnet_ks_torevert	.431
215	alexnet_fov4	.431
216	alexnet_fov12	.431
217	efficientnet_b2_imagenet_full	.431
218	efficientnet_b1_imagenet_full	.431
219	alexnet_fov16	.431
220	inception_v1	.431
221	mobilenet_v2_0.75_160-tf	.425
222	densenet-121-keras	.419
223	densenet-121	.419
224	mobilenet_v2_1.4_224-tf	.412
225	resnet34_imagenet_full	.412
226	resnet18_imagenet_full	.412
227	resnet50-VITO-8deg-cc	.406
228	mobilenet_v2_1.0_128-tf	.400
229	mobilenet_v1_0.5_192	.400
230	mobilenet_v1_1.0_160	.400
231	mobilenet_v2_0_75_160	.400
232	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.394
233	mobilenet_v1_0.75_160	.394
234	shufflenet_v2_x1_0	.394
235	mobilenet_v2_1_0_224	.394
236	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.394
237	mobilenet_v2_0_75_224	.388
238	mobilenet_v2_0_5_224	.381
239	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.375
240	0.5x_resnet-18_LC_w_sh_1_iter	.375
241	mobilenet_v2_0_75_192	.375
242	mobilenet_v2_1_3_224	.375
243	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.369
244	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.369
245	mobilenet_v1_0.5_224	.369
246	resnet_50_v1_spiking_l4a	.369
247	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.369
248	mobilenet_v2_0.5_192-tf	.362
249	mobilenet_v2_1_0_128	.362
250	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.356
251	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.356
252	pnasnet_large_pytorch	.350
253	mobilenet_v1_0.75_192	.344
254	mobilenet_v2_0.35_160	.344
255	mobilenet_v2_0_5_192	.344
256	ViT_L_32_imagenet1k	.338
257	mobilenet_v2_1-4_224_pytorch	.338
258	mobilenet_v2_1_4_224	.338
259	mobilenet_v1_0.5_160	.331
260	mobilenet_v1_0.5_128	.331
261	alexnet_random_linf8_perturb	.331
262	nasnet_mobile-tf	.325
263	alexnet_early_checkpoint	.325
264	convnext_small_imagenet_100_seed-0	.325
265	mobilenet_v2_0.35_224	.319
266	resnet50_eMMCR_Vanilla	.319
267	mobilenet_v1_0.25_160	.306
268	mobilenet_v2_0.35_192	.294
269	mobilenet_v1_0.25_128	.294
270	squeezenet1_1	.287
271	mobilenet_v1_0.25_192	.263
272	ViT_B_32_imagenet1k	.263
273	xception	.250
274	squeezenet1_0	.244
275	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.237
276	mobilenet_v1_0.25_224	.219
277	deit_small_imagenet_100_seed-0	.156
278	resnet50_imagenet_100_seed-0	.125
279	convnext_small_imagenet_10_seed-0	.125
280	resnet50-barlow	.119
281	resnet_50_v1_spiking_l4	.113
282	artResNet18_1	.113
283	resnet50_imagenet_10_seed-0	.106
284	deit_small_imagenet_1_seed-0	.087
285	deit_small_imagenet_10_seed-0	.081
286	resnet50_imagenet_1_seed-0	.081
287	alexnet_training_seed_09	.075
288	alexnet_training_seed_06	.075
289	resnet-50_untrained	.062
290	alexnet_training_seed_05	.062
291	mobilevit_small	.062
292	alexnet_training_seed_02	.062
293	alexnet_training_seed_08	.062
294	alexnet_training_seed_10	.062
295	convnext_small_imagenet_1_seed-0	.062
296	alexnet_training_seed_01	.062
297	alexnet_training_seed_01_fov4	.062
298	alexnet_training_seed_01_fov12	.062
299	alexnet_training_seed_01_fov16	.062
300	alexnet_training_seed_03	.062
301	alexnet_training_seed_04	.056
302	alexnet_training_seed_07	.056
303	resnet_50_v1_spiking_l3	.056
304	resnet50-simclr-vissl	.050
305	barlow-twins-resnet50	.044
306	resnet50-vicregl0p9	.037
307	resnet50-vicregl0p75	.006
308	ViT-B/32
309	resnet-18-LC_w_sh_100_iter_conv_init_m
310	BiT-M-R50x1
311	resnet-18-LC_w_sh_1_iter_conv_init_m
312	BiT-M-R152x2
313	yudixie_resnet50_cat_obj_class_all_latents_0_240908
314	BiT-M-R50x3
315	resnet-18-LC_w_sh_10_iter_m
316	resnet-18-LC_w_sh_100_iter_m
317	resnet-18-LC_w_sh_10_iter_conv_init_m
318	FrankRobWobv0
319	resnet-18-LC_w_sh_1_iter_m
320	0.5x_resnet-18_LC
321	CORnetZ_CIFAR10
322	resnet18-local_aggregation
323	r3m_resnet18
324	CORnet-Z
325	vggface
326	resnet18-instance_recognition
327	my-model
328	resnet18-deepcluster
329	0.5x_resnet-18_LC_w_sh_10_iter
330	r3m_resnet50
331	r3m_resnet34
332	resnet-18-LC_m
333	resnet18-supervised
334	r3m_resnet50_nocrop
335	resnet-18-LC_1st_conv_m
336	resnet-18-LC_conv_init_m
337	resnet18-contrastive_multiview
338	0.5x_resnet-18
339	0.5x_resnet-18_LC_w_sh_100_iter
340	RN50
341	RN50
342	resnet18-colorization
343	resnet18-relative_position
344	resnet18-autoencoder
345	resnet18-simclr
346	ViT_L_32
347	ViT_B_32
348	resnet18-depth_prediction
349	ViT_B_16
350	r3m_resnet34_nocrop
351	resnet18-contrastive_predictive
352	resnet-18-LC_untrained
353	resnet-18_untrained
354	resnet-50x2_untrained
355	dcgan
356	unet_entire
357	CORnetZ_CIFAR10_bs32_20_04
358	v1-pyr-nodown
359	pixels
360	dorinet_cornet_z
361	omnivore_swinT
362	openclip
363	vonealexnet_gaussian_noise_std4_fixed
364	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
365	yudixie_resnet50_object_class_0_240908
366	yudixie_resnet50_category_class_0_240908
367	yudixie_resnet50_distance_reg_0_240908
368	ConvLSTM
369	PredRNN
370	SimVP
371	briaai_rmbg_1_4
372	TAU
373	MIM
374	vit_base_patch14_reg4_dinov2-lvd142m
375	yolos_tiny
376	yudixie_resnet18_distance_translation_0_240719
377	compact_resnet50_V4
378	ReAlnet03_cornet
379	yudixie_resnet18_translation_reg_0_240719
380	yudixie_resnet18_distance_reg_0_240719
381	yudixie_resnet18_imagenet1kpret_0_240719
382	eBarlow_lmda_02_1000ep
383	yudixie_resnet50_distance_rotation_0_240908
384	yudixie_resnet50_distance_translation_0_240908
385	ReAlnet02_cornet
386	yudixie_resnet50_rotation_reg_0_240908
387	resnet18_ecoset_full
388	compact_resnet50_IT
389	resnet34_ecoset_full
390	ReAlnet01_cornet_fov4
391	ReAlnet01_cornet_fov12
392	resnet50_ecoset_full
393	ReAlnet04_cornet
394	resnet152_ecoset_full
395	compact_vgg19_V4
396	vit_base_patch16_dinov3-lvd1689m
397	yudixie_resnet18_translation_rotation_0_240719
398	vit_base_patch14_dinov2-lvd142m
399	yudixie_resnet18_distance_rotation_0_240719
400	resnet-18_test_m
401	yudixie_resnet50_translation_reg_0_240908
402	ReAlnet05_cornet
403	yudixie_resnet18_random_0_240719
404	yudixie_resnet18_distance_translation_rotation_0_240719
405	compact_vgg19_IT
406	SeLaVi-AVE
407	hmax
408	compact_resnet50_robust_V4
409	ReAlnet01_cornet_fov16
410	ReAlnet06_cornet
411	r3d_18
412	ReAlnet07_cornet
413	mc3_18
414	ReAlnet02_ALL
415	s3d
416	VOneCORnet-S
417	omnivore_swinB
418	resnet50-vicreg
419	ReAlnet08_cornet
420	mvit_v1_b
421	ReAlnet04_ALL
422	compact_cornets_V4
423	yudixie_resnet18_rotation_reg_0_240719
424	ReAlnet03_ALL
425	resnet18_imagenet21kP
426	compact_cornets_IT
427	ReAlnet09_cornet
428	omnivore_swinS
429	ReAlnet05_ALL
430	SeLaVi-Kinetics400
431	SeLaVi-VGG-Sound
432	ReAlnet06_ALL
433	SeLaVi-Kinetics-Sound
434	compact_resnet50_robust_IT
435	mvit_v2_s
436	ReAlnet10_cornet
437	r2plus1d_18
438	ReAlnet07_ALL
439	resnet101_ecoset_full
440	ReAlnet08_ALL
441	yudixie_resnet18_object_class_0_240719
442	yudixie_resnet18_category_class_0_240719
443	yudixie_resnet18_cat_obj_class_all_latents_0_240719
444	ReAlnet09_ALL
445	yudixie_resnet50_imagenet1kpret_0_240312
446	yudixie_resnet50_translation_rotation_0_240908
447	ReAlnet01_cornet
448	yudixie_resnet50_imagenet1kpret_0_240908
449	resnet50_primary_visual_cortex
450	yudixie_resnet50_random_0_240908
451	yudixie_resnet50_distance_translation_rotation_0_240908
452	ReAlnet10_ALL
453	ReAlnet01_ALL
454	resnet18_fair
455	pr1_stl10_disentangled_alpha04
456	predictive_coding_pc
457	yudixie_wbh_object_class_scaling_0415_run_0
458	yudixie_wbh_object_class_scaling_0415_run_3
459	yudixie_wbh_object_class_scaling_0415_imagenet
460	yudixie_wbh_object_class_scaling_0415_run_2
461	yudixie_wbh_object_class_scaling_0415_run_1
462	deepeisnn_ei_cifar10_vgg19
463	deepeisnn_ei_cifar10_resnet18
464	deepeisnn_ei_imagenet_resnet18
465	dinov2_vitl14_reg_lc_featread
466	gabor_filter_entropy_4x4
467	gabor_filter_mean_abs_4x4_v2
468	gabor_filter_entropy_4x4_v2
469	gabor_filter_entropy_4x4_v2_forcedCosine

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: Geirhos2021silhouette

Metric: top1