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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	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.756
3	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.731
4	vit_large_patch14_clip_224:laion2b_ft_in1k	.725
5	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.719
6	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.719
7	vit_large_patch14_clip_224:openai_ft_in1k	.719
8	vit_large_patch14_clip_336:laion2b_ft_in1k	.713
9	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.706
10	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.700
11	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.694
12	convnext_xlarge:fb_in22k_ft_in1k	.669
13	focalnet_tiny_lrf_in1k	.662
14	convnext_tiny:in12k_ft_in1k	.662
15	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.656
16	vit_base_patch16_clip_224:openai_ft_in1k	.656
17	convnext_large:fb_in22k_ft_in1k	.656
18	resnext101_32x8d_wsl	.650
19	AdvProp_efficientnet-b6	.650
20	resnext101_32x32d_wsl	.644
21	resnext101_32x16d_wsl	.637
22	convnext_base:clip_laiona_augreg_ft_in1k_384	.637
23	resnext101_32x48d_wsl	.631
24	convnext_tiny_imagenet_full_seed-0	.631
25	convnext_large_imagenet_full_seed-0	.631
26	vit_relpos_base_patch16_clsgap_224:sw_in1k	.631
27	AdvProp_efficientnet-b7	.625
28	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.625
29	imagenet_l2_10_0	.613
30	convnext_base_imagenet_full_seed-0	.613
31	AdvProp_efficientnet-b4	.606
32	AT_efficientnet-b4	.606
33	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.606
34	pnasnet_large-tf	.600
35	resnet-50-robust	.600
36	effnetv2m_custom384	.600
37	imagenet_l2_3_0	.600
38	resnet50_robust_l2_eps3	.600
39	resnet50_robust_l2_eps1	.600
40	convnext_small_imagenet_full_seed-0	.600
41	deit_large_imagenet_full_seed-0	.600
42	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.600
43	voneresnet-50-robust	.600
44	fixres_resnext101_32x48d_wsl	.594
45	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.588
46	deit_base_imagenet_full_seed-0	.588
47	alexnet_linf_8_robust	.581
48	blt_vs	.581
49	nasnet_large-tf	.575
50	vonegrcnn_62e_nobn	.575
51	resnet50_l2_3_robust	.575
52	cv_18_dagger_408_pretrained	.569
53	resnet50_ewconplus_wzc	.569
54	cvt_cvt-13-224-in1k_4_LucyV4	.569
55	resnet-152_v2-tf	.562
56	effnetb1_272x240	.562
57	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.562
58	convnext_tiny_sup	.562
59	vonegrcnn_52e_full	.562
60	resnet_152_v1	.562
61	resnet50-SIN_IN	.556
62	antialiased-rnext101_32x8d	.556
63	antialias-resnet152	.556
64	ReAlnet09	.556
65	vonegrcnn_52e_full	.550
66	BiT-S-R50x1	.550
67	BiT-S-R152x2	.550
68	resnet-50_v2-tf	.550
69	inception_v2-tf	.550
70	xception-keras	.550
71	AT_efficientnet-b2	.550
72	AdvProp_efficientnet-b2	.550
73	custom_model_cv_18_dagger_408	.550
74	cvt_cvt-21-384-in1k_4_LucyV4	.550
75	vonegrcnn_62e_nobn	.550
76	vonegrcnn_47e	.550
77	resnet-50-pytorch	.544
78	vonegrcnn_47e	.544
79	resnet-152_v1-tf	.544
80	resnet-101_v2-tf	.544
81	resnet50-sup	.544
82	cvt_cvt-21-224-in1k_4_LucyV4	.544
83	ReAlnet10	.544
84	resnet50_tutorial	.544
85	pnasnet_large_halfPrecision	.544
86	deit_small_imagenet_full_seed-0	.544
87	pnasnet_large	.544
88	resnet_50_v1	.544
89	efficientnet-b7	.537
90	AdvProp_efficientnet-b8	.537
91	AdvProp_efficientnet-b0	.537
92	alexnet_robust_correct	.537
93	resnet50_linf_8_robust	.537
94	resnet_101_v2	.537
95	ReAlnet03	.537
96	inception_v4-tf	.531
97	efficientnet_b0	.531
98	resnet-50_v1-tf	.531
99	densenet-169-keras	.525
100	antialiased-r50	.525
101	nasnet_large	.525
102	nasnet_mobile	.525
103	ReAlnet01	.525
104	ReAlnet04	.525
105	densenet-169	.525
106	resnet_50_v2	.525
107	inception_v1-tf	.519
108	BiT-S-R152x4	.519
109	BiT-S-R101x3	.519
110	voneresnet-50	.519
111	alexnet_l2_3_robust	.519
112	ReAlnet07	.519
113	ReAlnet08	.519
114	inception_v3_pytorch	.519
115	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.519
116	tv_efficientnet-b1	.519
117	grcnn_109	.519
118	densenet-201-keras	.512
119	resnet_50_v1_spiking	.512
120	ReAlnet06	.512
121	densenet-201	.512
122	resnet-152_v2_pytorch	.512
123	densenet_201_pytorch	.512
124	grcnn	.512
125	ReAlnet05	.512
126	mobilenet_v2_0.5_96	.506
127	BiT-S-R50x3	.506
128	resnet-101_v1-tf	.506
129	voneresnet-50-non_stochastic	.506
130	resnet50-SIN	.506
131	Res2Net50_26w_4s	.506
132	cvt_cvt-13-384-in1k_4_LucyV4	.506
133	ReAlnet02	.506
134	inception_v3-tf	.500
135	inception_v3	.500
136	resnet_SIN_IN_FT_IN	.494
137	mobilenet_v2_1.3_224-tf	.494
138	resnet50-SIN_IN_IN	.494
139	mobilenet_v2_0.35_96	.494
140	resnet_101_v1	.494
141	mobilenet_v2_0.75_96	.487
142	BiT-S-R101x1	.487
143	resnet101_imagenet_full	.487
144	inception_v4	.487
145	resnet_152_v2	.487
146	vgg-19-keras	.481
147	CLIP-RN50	.481
148	mobilenet_v2_0.35_128	.481
149	regnet_y_400mf	.481
150	AlexNet_SIN	.481
151	AlexNet_SIN_fov4	.481
152	AlexNet_SIN_fov12	.481
153	AlexNet_SIN_fov16	.481
154	mobilenet_v2_1.0_160-tf	.475
155	vgg-16-keras	.475
156	mobilenet_v2_1.0_96	.475
157	resnet-18	.475
158	mobilenet_v2_0.75_128	.475
159	convnext_femto_ols:d1_in1k	.475
160	mobilenet_v1_0.75_128	.469
161	resnet50-moclr8deg	.469
162	CORnet-S	.463
163	mobilenet_v1_0.75_224	.463
164	mobilenet_v1_1.0_192	.463
165	mobilenet_v2_1.0_192-tf	.456
166	mobilenet_v2_0.75_224-tf	.456
167	mobilenet_v2_1_0_160	.456
168	mobilenet_v2_0.5_160	.450
169	mobilenet_v2_1.0_224-tf	.450
170	mobilenet_v1_1.0_128	.450
171	resnet-34	.450
172	mobilenet_v1_1.0_224	.450
173	resnet50	.450
174	vgg_19	.450
175	mobilenet_v2_1_0_192	.450
176	resnet152_imagenet_full	.450
177	resnet50-vitoimagevidnet8	.450
178	mobilenet_v2_0.5_128	.444
179	efficientnet_b0_imagenet_full	.444
180	mobilenet_v2_0.75_192-tf	.438
181	resnet50_imagenet_full	.438
182	vgg_16	.438
183	mobilenet_v2_0.5_224-tf	.431
184	alexnet	.431
185	resnet50_moco_v2	.431
186	alexnet_ks_torevert	.431
187	alexnet_fov4	.431
188	alexnet_fov12	.431
189	efficientnet_b2_imagenet_full	.431
190	efficientnet_b1_imagenet_full	.431
191	alexnet_fov16	.431
192	inception_v1	.431
193	mobilenet_v2_0.75_160-tf	.425
194	densenet-121-keras	.419
195	densenet-121	.419
196	mobilenet_v2_1.4_224-tf	.412
197	resnet34_imagenet_full	.412
198	resnet18_imagenet_full	.412
199	resnet50-VITO-8deg-cc	.406
200	mobilenet_v2_1.0_128-tf	.400
201	mobilenet_v1_0.5_192	.400
202	mobilenet_v1_1.0_160	.400
203	mobilenet_v2_0_75_160	.400
204	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.394
205	mobilenet_v1_0.75_160	.394
206	shufflenet_v2_x1_0	.394
207	mobilenet_v2_1_0_224	.394
208	mobilenet_v2_0_75_224	.388
209	mobilenet_v2_0_5_224	.381
210	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.375
211	0.5x_resnet-18_LC_w_sh_1_iter	.375
212	mobilenet_v2_0_75_192	.375
213	mobilenet_v2_1_3_224	.375
214	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.369
215	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.369
216	mobilenet_v1_0.5_224	.369
217	resnet_50_v1_spiking_l4a	.369
218	mobilenet_v2_0.5_192-tf	.362
219	mobilenet_v2_1_0_128	.362
220	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.356
221	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.356
222	pnasnet_large_pytorch	.350
223	mobilenet_v1_0.75_192	.344
224	mobilenet_v2_0.35_160	.344
225	mobilenet_v2_0_5_192	.344
226	ViT_L_32_imagenet1k	.338
227	mobilenet_v2_1-4_224_pytorch	.338
228	mobilenet_v2_1_4_224	.338
229	mobilenet_v1_0.5_160	.331
230	mobilenet_v1_0.5_128	.331
231	alexnet_random_linf8_perturb	.331
232	nasnet_mobile-tf	.325
233	alexnet_early_checkpoint	.325
234	convnext_small_imagenet_100_seed-0	.325
235	mobilenet_v2_0.35_224	.319
236	resnet50_eMMCR_Vanilla	.319
237	mobilenet_v1_0.25_160	.306
238	mobilenet_v2_0.35_192	.294
239	mobilenet_v1_0.25_128	.294
240	squeezenet1_1	.287
241	mobilenet_v1_0.25_192	.263
242	ViT_B_32_imagenet1k	.263
243	xception	.250
244	squeezenet1_0	.244
245	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.237
246	mobilenet_v1_0.25_224	.219
247	deit_small_imagenet_100_seed-0	.156
248	resnet50_imagenet_100_seed-0	.125
249	convnext_small_imagenet_10_seed-0	.125
250	resnet50-barlow	.119
251	resnet_50_v1_spiking_l4	.113
252	artResNet18_1	.113
253	resnet50_imagenet_10_seed-0	.106
254	resnet50-meshes-lt-100-original-pretrained	.094
255	deit_small_imagenet_1_seed-0	.087
256	deit_small_imagenet_10_seed-0	.081
257	resnet50_imagenet_1_seed-0	.081
258	alexnet_training_seed_09	.075
259	alexnet_training_seed_06	.075
260	resnet-50_untrained	.062
261	resnet50-meshes-lt-100-original-scratch	.062
262	alexnet_training_seed_05	.062
263	mobilevit_small	.062
264	alexnet_training_seed_02	.062
265	alexnet_training_seed_08	.062
266	alexnet_training_seed_10	.062
267	convnext_small_imagenet_1_seed-0	.062
268	alexnet_training_seed_01	.062
269	alexnet_training_seed_01_fov4	.062
270	alexnet_training_seed_01_fov12	.062
271	alexnet_training_seed_01_fov16	.062
272	alexnet_training_seed_03	.062
273	alexnet_training_seed_04	.056
274	alexnet_training_seed_07	.056
275	resnet_50_v1_spiking_l3	.056
276	resnet50-simclr-vissl	.050
277	barlow-twins-resnet50	.044
278	resnet50-vicregl0p9	.037
279	resnet50-vicregl0p75	.006
280	ViT-B/32
281	resnet-18-LC_w_sh_100_iter_conv_init_m
282	BiT-M-R50x1
283	resnet-18-LC_w_sh_1_iter_conv_init_m
284	BiT-M-R152x2
285	yudixie_resnet50_cat_obj_class_all_latents_0_240908
286	BiT-M-R50x3
287	resnet-18-LC_w_sh_10_iter_m
288	resnet-18-LC_w_sh_100_iter_m
289	resnet-18-LC_w_sh_10_iter_conv_init_m
290	FrankRobWobv0
291	resnet-18-LC_w_sh_1_iter_m
292	0.5x_resnet-18_LC
293	CORnetZ_CIFAR10
294	resnet18-local_aggregation
295	r3m_resnet18
296	CORnet-Z
297	vggface
298	resnet18-instance_recognition
299	my-model
300	resnet18-deepcluster
301	0.5x_resnet-18_LC_w_sh_10_iter
302	r3m_resnet50
303	r3m_resnet34
304	resnet-18-LC_m
305	resnet18-supervised
306	r3m_resnet50_nocrop
307	resnet-18-LC_1st_conv_m
308	resnet-18-LC_conv_init_m
309	resnet18-contrastive_multiview
310	0.5x_resnet-18
311	0.5x_resnet-18_LC_w_sh_100_iter
312	RN50
313	RN50
314	resnet18-colorization
315	resnet18-relative_position
316	resnet18-autoencoder
317	resnet18-simclr
318	ViT_L_32
319	ViT_B_32
320	resnet18-depth_prediction
321	ViT_B_16
322	r3m_resnet34_nocrop
323	resnet18-contrastive_predictive
324	resnet-18-LC_untrained
325	resnet-18_untrained
326	resnet-50x2_untrained
327	dcgan
328	unet_entire
329	CORnetZ_CIFAR10_bs32_20_04
330	v1-pyr-nodown
331	pixels
332	dorinet_cornet_z
333	omnivore_swinT
334	resnet50
335	openclip
336	vonealexnet_gaussian_noise_std4_fixed
337	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
338	yudixie_resnet50_object_class_0_240908
339	yudixie_resnet50_category_class_0_240908
340	yudixie_resnet50_distance_reg_0_240908
341	ConvLSTM
342	PredRNN
343	SimVP
344	briaai_rmbg_1_4
345	TAU
346	MIM
347	vit_base_patch14_reg4_dinov2-lvd142m
348	yolos_tiny
349	yudixie_resnet18_distance_translation_0_240719
350	compact_resnet50_V4
351	ReAlnet03_cornet
352	yudixie_resnet18_translation_reg_0_240719
353	yudixie_resnet18_distance_reg_0_240719
354	yudixie_resnet18_imagenet1kpret_0_240719
355	eBarlow_lmda_02_1000ep
356	yudixie_resnet50_distance_rotation_0_240908
357	yudixie_resnet50_distance_translation_0_240908
358	ReAlnet02_cornet
359	yudixie_resnet50_rotation_reg_0_240908
360	resnet18_ecoset_full
361	compact_resnet50_IT
362	resnet34_ecoset_full
363	ReAlnet01_cornet_fov4
364	ReAlnet01_cornet_fov12
365	resnet50_ecoset_full
366	ReAlnet04_cornet
367	resnet152_ecoset_full
368	compact_vgg19_V4
369	vit_base_patch16_dinov3-lvd1689m
370	aughost_fair_resnet18
371	yudixie_resnet18_translation_rotation_0_240719
372	vit_base_patch14_dinov2-lvd142m
373	yudixie_resnet18_distance_rotation_0_240719
374	resnet-18_test_m
375	yudixie_resnet50_translation_reg_0_240908
376	ReAlnet05_cornet
377	yudixie_resnet18_random_0_240719
378	yudixie_resnet18_distance_translation_rotation_0_240719
379	compact_vgg19_IT
380	SeLaVi-AVE
381	hmax
382	compact_resnet50_robust_V4
383	ReAlnet01_cornet_fov16
384	ReAlnet06_cornet
385	r3d_18
386	ReAlnet07_cornet
387	mc3_18
388	ReAlnet02_ALL
389	s3d
390	VOneCORnet-S
391	omnivore_swinB
392	resnet50-vicreg
393	ReAlnet08_cornet
394	mvit_v1_b
395	ReAlnet04_ALL
396	compact_cornets_V4
397	yudixie_resnet18_rotation_reg_0_240719
398	ReAlnet03_ALL
399	resnet18_imagenet21kP
400	compact_cornets_IT
401	ReAlnet09_cornet
402	omnivore_swinS
403	ReAlnet05_ALL
404	SeLaVi-Kinetics400
405	SeLaVi-VGG-Sound
406	ReAlnet06_ALL
407	SeLaVi-Kinetics-Sound
408	compact_resnet50_robust_IT
409	mvit_v2_s
410	ReAlnet10_cornet
411	r2plus1d_18
412	ReAlnet07_ALL
413	resnet101_ecoset_full
414	ReAlnet08_ALL
415	yudixie_resnet18_object_class_0_240719
416	yudixie_resnet18_category_class_0_240719
417	yudixie_resnet18_cat_obj_class_all_latents_0_240719
418	ReAlnet09_ALL
419	yudixie_resnet50_imagenet1kpret_0_240312
420	yudixie_resnet50_translation_rotation_0_240908
421	ReAlnet01_cornet
422	yudixie_resnet50_imagenet1kpret_0_240908
423	resnet50_primary_visual_cortex
424	yudixie_resnet50_random_0_240908
425	yudixie_resnet50_distance_translation_rotation_0_240908
426	ReAlnet10_ALL
427	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

1.00.

Note that scores are relative to this ceiling.

Data: Geirhos2021silhouette

Metric: top1