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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021stylized-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	resnet50-SIN_IN	.910
2	resnet50-SIN	.910
3	AlexNet_SIN	.786
4	AlexNet_SIN_fov4	.786
5	AlexNet_SIN_fov12	.786
6	AlexNet_SIN_fov16	.786
7	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.748
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.700
9	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.695
10	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.657
11	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.655
12	AdvProp_efficientnet-b7	.654
13	AdvProp_efficientnet-b6	.650
14	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.649
15	vit_large_patch14_clip_224:laion2b_ft_in1k	.649
16	deit3_base_patch16_224_fb_in1k	.644
17	convnext_base_fb_in22k_ft_in1k	.641
18	vit_large_patch14_clip_224:openai_ft_in1k	.637
19	convnext_xlarge:fb_in22k_ft_in1k	.631
20	deit3_base_patch16_224_fb_in22k_ft_in1k	.627
21	convnext_large:fb_in22k_ft_in1k	.613
22	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.609
23	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.605
24	AdvProp_efficientnet-b4	.599
25	AT_efficientnet-b4	.599
26	vit_large_patch14_clip_336:laion2b_ft_in1k	.599
27	AdvProp_efficientnet-b8	.595
28	efficientnet-b7	.594
29	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.588
30	effnetv2m_custom384	.586
31	vit_base_patch16_clip_224:openai_ft_in1k	.574
32	deit_large_imagenet_full_seed-0	.571
33	vit_relpos_base_patch16_clsgap_224:sw_in1k	.571
34	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.568
35	AT_efficientnet-b2	.566
36	AdvProp_efficientnet-b2	.566
37	convnext_base:clip_laiona_augreg_ft_in1k_384	.561
38	convnext_tiny_sup	.559
39	convnext_base_fb_in1k	.559
40	convnext_small_imagenet_full_seed-0	.557
41	convnext_large_imagenet_full_seed-0	.556
42	focalnet_tiny_lrf_in1k	.551
43	resnext101_32x16d_wsl	.547
44	resnext101_32x32d_wsl	.546
45	deit_base_imagenet_full_seed-0	.546
46	convnext_base_imagenet_full_seed-0	.546
47	convnext_tiny:in12k_ft_in1k	.544
48	convnext_tiny_imagenet_full_seed-0	.544
49	resnext101_32x48d_wsl	.540
50	resnext101_32x8d_wsl	.521
51	AdvProp_efficientnet-b0	.521
52	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.515
53	effnetb1_272x240	.514
54	deit_small_imagenet_full_seed-0	.512
55	ViT_L_32_imagenet1k	.492
56	fixres_resnext101_32x48d_wsl	.492
57	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.485
58	cvt_cvt-13-224-in1k_4_LucyV4	.482
59	cvt_cvt-13-384-in1k_4_LucyV4	.481
60	ViT_B_32_imagenet1k	.476
61	efficientnet_b0	.475
62	resnet-101_v2-tf	.466
63	cvt_cvt-21-224-in1k_4_LucyV4	.466
64	nasnet_large-tf	.464
65	cv_18_dagger_408_pretrained	.464
66	resnet-152_v2-tf	.458
67	efficientnet_b2_imagenet_full	.455
68	resnet_SIN_IN_FT_IN	.450
69	resnet50-SIN_IN_IN	.450
70	inception_v3_pytorch	.450
71	voneresnet-50-robust	.448
72	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.445
73	inception_v3-tf	.444
74	resnet50_robust_l2_eps1	.441
75	antialiased-rnext101_32x8d	.440
76	antialias-resnet152	.440
77	nasnet_large	.438
78	pnasnet_large-tf	.434
79	resnet-152_v1-tf	.431
80	convnext_small_imagenet_100_seed-0	.430
81	resnet-50_v2-tf	.427
82	efficientnet_b0_imagenet_full	.427
83	densenet-169-keras	.426
84	regnet_y_400mf	.426
85	resnet50_ewconplus_wzc	.426
86	inception_v3	.425
87	resnet50_ewconplus_wzc_2_323	.424
88	densenet-169	.424
89	mobilenet_v2_1.0_192-tf	.422
90	mobilenet_v2_0.75_192-tf	.422
91	efficientnet_b1_imagenet_full	.421
92	blt_vs	.420
93	tv_efficientnet-b1	.420
94	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.420
95	densenet-201-keras	.419
96	resnet-34	.419
97	densenet-201	.419
98	resnet50_ewconplus_wzc_seed3_2_323	.419
99	resnet_101_v1	.417
100	mobilenet_v2_0.75_224-tf	.416
101	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.416
102	resnet_152_v1	.416
103	nasnet_mobile	.415
104	convnext_femto_ols:d1_in1k	.414
105	mobilenet_v2_0.75_160-tf	.412
106	mobilenet_v2_1.0_160-tf	.410
107	vonegrcnn_62e_nobn	.410
108	BiT-S-R101x3	.410
109	pnasnet_large_halfPrecision	.410
110	pnasnet_large	.410
111	resnet_101_v2	.410
112	BiT-S-R101x1	.409
113	resnet50_ewconplus_wzc_seed2_2__323	.409
114	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.409
115	vonegrcnn_52e_full	.407
116	resnet-50-robust	.407
117	imagenet_l2_3_0	.407
118	resnet50_robust_l2_eps3	.407
119	vonegrcnn_47e	.407
120	resnet50_st_wzc_seed4_2_323	.406
121	resnet50-vitoimagevidnet8	.406
122	mobilenet_v2_0.5_160	.404
123	xception-keras	.404
124	vonegrcnn_52e_full	.404
125	resnet50_ewconplus_noseed_2_323	.404
126	vonegrcnn_47e	.403
127	BiT-S-R50x1	.401
128	inception_v2-tf	.401
129	mobilenet_v2_1_0_192	.401
130	vonegrcnn_62e_nobn	.401
131	voneresnet-50	.400
132	mobilenet_v2_1_0_160	.400
133	resnet50_ewconplus_wzc_seed6_2_323	.400
134	inception_v4-tf	.399
135	resnet-101_v1-tf	.399
136	mobilenet_v2_0.5_192-tf	.399
137	resnet50_l2_3_robust	.399
138	convnext_base_fb_in1k_gram_aligned_dinov3	.399
139	mobilenet_v2_1.0_128-tf	.398
140	mobilenet_v2_0.75_128	.398
141	resnet50-VITO-8deg-cc	.398
142	mobilenet_v2_0_75_160	.398
143	grcnn_109	.398
144	inception_v1-tf	.395
145	mobilenet_v2_0.75_96	.395
146	voneresnet-50-non_stochastic	.395
147	BiT-S-R152x2	.393
148	grcnn	.393
149	resnet50_ewconplus_wzc_seed7_2_323	.391
150	mobilenet_v1_0.75_160	.390
151	resnet50_ewconplus_wzc_seed5_2__323	.389
152	resnet50_linf_8_robust	.388
153	shufflenet_v2_x1_0	.386
154	cvt_cvt-21-384-in1k_4_LucyV4	.386
155	inception_v4	.386
156	resnet50_st_wzc_seed6_2_323	.386
157	resnet50_st_wzc_seed3_2_323	.386
158	mobilenet_v2_1.0_224-tf	.385
159	mobilenet_v2_1_0_128	.385
160	BiT-S-R50x3	.384
161	mobilenet_v2_0_75_224	.384
162	mobilenet_v2_1.4_224-tf	.383
163	inception_v1	.383
164	densenet-121-keras	.381
165	mobilenet_v1_0.75_128	.381
166	densenet-121	.381
167	resnet50_st_wzc_seed7_2_323	.380
168	antialiased-r50	.379
169	resnet50_st_wzc_2	.379
170	mobilenet_v2_0_75_192	.379
171	resnet-50_v1-tf	.378
172	resnet-18	.378
173	resnet_50_v2	.378
174	resnet_50_v1_spiking	.375
175	resnet50_st_wzc_noseed_2_323	.375
176	mobilenet_v2_1_0_224	.374
177	mobilenet_v1_0.75_192	.372
178	resnet-50-pytorch	.371
179	vgg-19-keras	.371
180	resnet50-sup	.371
181	mobilenet_v2_1.0_96	.371
182	resnet50_tutorial	.371
183	resnet_50_v1	.371
184	mobilenet_v2_1.3_224-tf	.370
185	mobilenet_v2_0_5_192	.370
186	mobilenet_v1_1.0_128	.369
187	ReAlnet03	.367
188	resnet50_st_wzc_2_323	.367
189	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.367
190	CORnet-S	.366
191	custom_model_cv_18_dagger_408	.366
192	ReAlnet07	.366
193	ReAlnet09	.366
194	resnet50_st_wzc_seed5_2_323	.366
195	mobilenet_v2_0_5_224	.365
196	ReAlnet10	.364
197	mobilenet_v2_0.35_192	.361
198	mobilenet_v2_0.5_224-tf	.360
199	resnet50	.359
200	ReAlnet08	.359
201	ReAlnet05	.359
202	mobilenet_v1_1.0_160	.356
203	mobilenet_v2_1_3_224	.356
204	mobilenet_v1_0.5_128	.355
205	ReAlnet04	.355
206	nasnet_mobile-tf	.354
207	Res2Net50_26w_4s	.354
208	mobilenet_v2_0.35_224	.352
209	resnet34_imagenet_full	.352
210	ReAlnet06	.352
211	xception	.352
212	mobilenet_v2_0.5_128	.351
213	resnet50_imagenet_full	.351
214	ReAlnet01	.351
215	eBarlow_lmda_02_1000ep	.350
216	mobilenet_v1_1.0_192	.347
217	mobilenet_v2_0.5_96	.346
218	mobilenet_v1_0.75_224	.346
219	resnet50_moco_v2	.346
220	mobilenet_v1_0.5_160	.345
221	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.344
222	mobilenet_v2_0.35_160	.344
223	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.343
224	BiT-S-R152x4	.341
225	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.341
226	ReAlnet02	.341
227	CLIP-RN50	.340
228	imagenet_l2_10_0	.338
229	pnasnet_large_pytorch	.338
230	resnet152_imagenet_full	.335
231	mobilenet_v2_0.35_128	.331
232	resnet101_imagenet_full	.331
233	mobilenet_v1_0.5_192	.329
234	resnet50_eMMCR_Vanilla	.329
235	resnet_152_v2	.328
236	mobilenet_v1_0.5_224	.325
237	mobilenet_v1_1.0_224	.325
238	resnet50-moclr8deg	.321
239	mobilenet_v2_0.35_96	.315
240	resnet18_imagenet_full	.315
241	mobilenet_v2_1-4_224_pytorch	.312
242	mobilenet_v2_1_4_224	.312
243	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.311
244	resnet-152_v2_pytorch	.309
245	vgg-16-keras	.306
246	alexnet_l2_3_robust	.306
247	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.305
248	alexnet_random_linf8_perturb	.305
249	squeezenet1_1	.302
250	alexnet_early_checkpoint	.302
251	mobilenet_v1_0.25_224	.301
252	mobilenet_v1_0.25_192	.297
253	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.290
254	0.5x_resnet-18_LC_w_sh_1_iter	.289
255	mobilenet_v1_0.25_160	.287
256	alexnet_robust_correct	.282
257	alexnet	.279
258	alexnet_ks_torevert	.279
259	alexnet_fov12	.279
260	alexnet_fov4	.279
261	alexnet_fov16	.279
262	alexnet_linf_8_robust	.265
263	vgg_16	.265
264	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.263
265	vgg_19	.255
266	squeezenet1_0	.254
267	mobilenet_v1_0.25_128	.253
268	resnet50_imagenet_100_seed-0	.251
269	resnet_50_v1_spiking_l4a	.241
270	deit_small_imagenet_100_seed-0	.184
271	resnet_50_v1_spiking_l4	.160
272	resnet50_imagenet_10_seed-0	.087
273	resnet50-vicregl0p9	.084
274	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.083
275	convnext_small_imagenet_10_seed-0	.072
276	mobilevit_small	.070
277	alexnet_training_seed_06	.070
278	convnext_small_imagenet_1_seed-0	.068
279	deit_small_imagenet_10_seed-0	.066
280	barlow-twins-resnet50	.065
281	alexnet_training_seed_09	.065
282	deit_small_imagenet_1_seed-0	.065
283	alexnet_training_seed_07	.064
284	resnet50-meshes-lt-100-original-scratch	.062
285	alexnet_training_seed_05	.062
286	alexnet_training_seed_02	.062
287	alexnet_training_seed_04	.062
288	alexnet_training_seed_10	.062
289	alexnet_training_seed_08	.062
290	alexnet_training_seed_01	.062
291	alexnet_training_seed_01_fov4	.062
292	alexnet_training_seed_01_fov12	.062
293	alexnet_training_seed_01_fov16	.062
294	alexnet_training_seed_03	.062
295	resnet50-meshes-lt-100-original-pretrained	.061
296	artResNet18_1	.059
297	resnet-50_untrained	.058
298	resnet50-vicregl0p75	.058
299	resnet50_imagenet_1_seed-0	.055
300	resnet_50_v1_spiking_l3	.050
301	resnet50-simclr-vissl	.045
302	resnet50-barlow	.044
303	ViT-B/32
304	BiT-M-R50x1
305	resnet-18-LC_w_sh_100_iter_conv_init_m
306	resnet-18-LC_w_sh_1_iter_conv_init_m
307	BiT-M-R152x2
308	BiT-M-R50x3
309	resnet-18-LC_w_sh_10_iter_m
310	resnet-18_test_m
311	resnet-18-LC_w_sh_100_iter_m
312	resnet-18-LC_w_sh_10_iter_conv_init_m
313	FrankRobWobv0
314	resnet-18-LC_w_sh_1_iter_m
315	0.5x_resnet-18_LC
316	CORnetZ_CIFAR10
317	resnet18-local_aggregation
318	r3m_resnet18
319	CORnet-Z
320	vggface
321	my-model
322	resnet18-instance_recognition
323	resnet18-deepcluster
324	0.5x_resnet-18_LC_w_sh_10_iter
325	r3m_resnet50
326	r3m_resnet34
327	resnet-18-LC_m
328	resnet18-supervised
329	r3m_resnet50_nocrop
330	resnet-18-LC_1st_conv_m
331	resnet-18-LC_conv_init_m
332	resnet18-contrastive_multiview
333	0.5x_resnet-18
334	0.5x_resnet-18_LC_w_sh_100_iter
335	RN50
336	RN50
337	resnet18-colorization
338	resnet18-relative_position
339	ViT_L_32
340	resnet18-simclr
341	resnet18-autoencoder
342	ViT_B_32
343	resnet18-depth_prediction
344	ViT_B_16
345	r3m_resnet34_nocrop
346	resnet18-contrastive_predictive
347	resnet-18-LC_untrained
348	resnet-18_untrained
349	resnet-50x2_untrained
350	dcgan
351	unet_entire
352	CORnetZ_CIFAR10_bs32_20_04
353	pixels
354	dorinet_cornet_z
355	v1-pyr-nodown
356	omnivore_swinT
357	resnet50
358	openclip
359	vonealexnet_gaussian_noise_std4_fixed
360	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
361	yudixie_resnet50_category_class_0_240908
362	yudixie_resnet50_distance_reg_0_240908
363	resnet18_imagenet21kP
364	ConvLSTM
365	PredRNN
366	SimVP
367	TAU
368	briaai_rmbg_1_4
369	MIM
370	yolos_tiny
371	densenet_201_pytorch
372	yudixie_resnet18_distance_translation_0_240719
373	ReAlnet01_cornet
374	hmax
375	yudixie_resnet18_translation_rotation_0_240719
376	compact_resnet50_V4
377	ReAlnet02_cornet
378	yudixie_resnet18_translation_reg_0_240719
379	yudixie_resnet18_distance_reg_0_240719
380	yudixie_resnet18_rotation_reg_0_240719
381	yudixie_resnet18_imagenet1kpret_0_240719
382	yudixie_resnet50_distance_rotation_0_240908
383	yudixie_resnet50_rotation_reg_0_240908
384	yudixie_resnet50_distance_translation_0_240908
385	vit_base_patch14_reg4_dinov2-lvd142m
386	resnet18_ecoset_full
387	vit_base_patch14_dinov2-lvd142m
388	resnet34_ecoset_full
389	ReAlnet01_cornet_fov4
390	ReAlnet03_cornet
391	ReAlnet01_cornet_fov12
392	resnet50_ecoset_full
393	vit_base_patch16_dinov3-lvd1689m
394	compact_resnet50_IT
395	resnet101_ecoset_full
396	resnet152_ecoset_full
397	compact_vgg19_V4
398	yudixie_resnet18_distance_rotation_0_240719
399	ReAlnet05_cornet
400	yudixie_resnet18_category_class_0_240719
401	yudixie_resnet18_random_0_240719
402	aughost_fair_resnet18
403	SeLaVi-AVE
404	compact_vgg19_IT
405	compact_resnet50_robust_V4
406	ReAlnet06_cornet
407	ReAlnet01_cornet_fov16
408	r3d_18
409	yudixie_resnet50_translation_rotation_0_240908
410	ReAlnet07_cornet
411	mc3_18
412	ReAlnet02_ALL
413	s3d
414	VOneCORnet-S
415	ReAlnet08_cornet
416	omnivore_swinB
417	resnet50-vicreg
418	mvit_v1_b
419	ReAlnet03_ALL
420	compact_cornets_V4
421	ReAlnet09_cornet
422	compact_cornets_IT
423	omnivore_swinS
424	SeLaVi-Kinetics400
425	ReAlnet05_ALL
426	SeLaVi-VGG-Sound
427	ReAlnet06_ALL
428	ReAlnet10_cornet
429	yudixie_resnet50_distance_translation_rotation_0_240908
430	compact_resnet50_robust_IT
431	yudixie_resnet50_random_0_240908
432	ReAlnet07_ALL
433	SeLaVi-Kinetics-Sound
434	mvit_v2_s
435	r2plus1d_18
436	yudixie_resnet50_imagenet1kpret_0_240908
437	ReAlnet08_ALL
438	yudixie_resnet18_object_class_0_240719
439	yudixie_resnet18_cat_obj_class_all_latents_0_240719
440	ReAlnet09_ALL
441	yudixie_resnet18_distance_translation_rotation_0_240719
442	yudixie_resnet50_imagenet1kpret_0_240312
443	yudixie_resnet50_translation_reg_0_240908
444	ReAlnet04_cornet
445	yudixie_resnet50_object_class_0_240908
446	resnet50_primary_visual_cortex
447	yudixie_resnet50_cat_obj_class_all_latents_0_240908
448	ReAlnet10_ALL
449	ReAlnet01_ALL
450	ReAlnet04_ALL
451	resnet18_fair
452	pr1_stl10_disentangled_alpha04
453	predictive_coding_pc
454	yudixie_wbh_object_class_scaling_0415_run_0
455	yudixie_wbh_object_class_scaling_0415_run_3
456	yudixie_wbh_object_class_scaling_0415_imagenet
457	yudixie_wbh_object_class_scaling_0415_run_2
458	yudixie_wbh_object_class_scaling_0415_run_1
459	deepeisnn_ei_cifar10_vgg19
460	deepeisnn_ei_cifar10_resnet18
461	deepeisnn_ei_imagenet_resnet18

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: Geirhos2021stylized

Metric: top1