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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	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.748
5	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.700
6	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.695
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.657
8	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.655
9	AdvProp_efficientnet-b7	.654
10	AdvProp_efficientnet-b6	.650
11	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.649
12	vit_large_patch14_clip_224:laion2b_ft_in1k	.649
13	vit_large_patch14_clip_224:openai_ft_in1k	.637
14	convnext_xlarge:fb_in22k_ft_in1k	.631
15	convnext_large:fb_in22k_ft_in1k	.613
16	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.609
17	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.605
18	AdvProp_efficientnet-b4	.599
19	AT_efficientnet-b4	.599
20	vit_large_patch14_clip_336:laion2b_ft_in1k	.599
21	AdvProp_efficientnet-b8	.595
22	efficientnet-b7	.594
23	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.588
24	effnetv2m_custom384	.586
25	vit_base_patch16_clip_224:openai_ft_in1k	.574
26	deit_large_imagenet_full_seed-0	.571
27	vit_relpos_base_patch16_clsgap_224:sw_in1k	.571
28	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.568
29	AT_efficientnet-b2	.566
30	AdvProp_efficientnet-b2	.566
31	convnext_base:clip_laiona_augreg_ft_in1k_384	.561
32	convnext_tiny_sup	.559
33	convnext_small_imagenet_full_seed-0	.557
34	convnext_large_imagenet_full_seed-0	.556
35	focalnet_tiny_lrf_in1k	.551
36	resnext101_32x16d_wsl	.547
37	resnext101_32x32d_wsl	.546
38	deit_base_imagenet_full_seed-0	.546
39	convnext_base_imagenet_full_seed-0	.546
40	convnext_tiny:in12k_ft_in1k	.544
41	convnext_tiny_imagenet_full_seed-0	.544
42	resnext101_32x48d_wsl	.540
43	resnext101_32x8d_wsl	.521
44	AdvProp_efficientnet-b0	.521
45	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.515
46	effnetb1_272x240	.514
47	deit_small_imagenet_full_seed-0	.512
48	ViT_L_32_imagenet1k	.492
49	fixres_resnext101_32x48d_wsl	.492
50	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.485
51	cvt_cvt-13-224-in1k_4_LucyV4	.482
52	cvt_cvt-13-384-in1k_4_LucyV4	.481
53	ViT_B_32_imagenet1k	.476
54	efficientnet_b0	.475
55	resnet-101_v2-tf	.466
56	cvt_cvt-21-224-in1k_4_LucyV4	.466
57	nasnet_large-tf	.464
58	cv_18_dagger_408_pretrained	.464
59	resnet-152_v2-tf	.458
60	efficientnet_b2_imagenet_full	.455
61	resnet_SIN_IN_FT_IN	.450
62	resnet50-SIN_IN_IN	.450
63	inception_v3_pytorch	.450
64	voneresnet-50-robust	.448
65	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.445
66	inception_v3-tf	.444
67	resnet50_robust_l2_eps1	.441
68	antialiased-rnext101_32x8d	.440
69	antialias-resnet152	.440
70	nasnet_large	.438
71	pnasnet_large-tf	.434
72	resnet-152_v1-tf	.431
73	convnext_small_imagenet_100_seed-0	.430
74	resnet-50_v2-tf	.427
75	efficientnet_b0_imagenet_full	.427
76	densenet-169-keras	.426
77	regnet_y_400mf	.426
78	resnet50_ewconplus_wzc	.426
79	inception_v3	.425
80	densenet-169	.424
81	mobilenet_v2_1.0_192-tf	.422
82	mobilenet_v2_0.75_192-tf	.422
83	efficientnet_b1_imagenet_full	.421
84	blt_vs	.420
85	tv_efficientnet-b1	.420
86	densenet-201-keras	.419
87	resnet-34	.419
88	densenet-201	.419
89	resnet_101_v1	.417
90	mobilenet_v2_0.75_224-tf	.416
91	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.416
92	resnet_152_v1	.416
93	nasnet_mobile	.415
94	convnext_femto_ols:d1_in1k	.414
95	mobilenet_v2_0.75_160-tf	.412
96	mobilenet_v2_1.0_160-tf	.410
97	vonegrcnn_62e_nobn	.410
98	BiT-S-R101x3	.410
99	pnasnet_large	.410
100	resnet_101_v2	.410
101	BiT-S-R101x1	.409
102	vonegrcnn_52e_full	.407
103	resnet-50-robust	.407
104	imagenet_l2_3_0	.407
105	resnet50_robust_l2_eps3	.407
106	vonegrcnn_47e	.407
107	resnet50-vitoimagevidnet8	.406
108	mobilenet_v2_0.5_160	.404
109	xception-keras	.404
110	vonegrcnn_52e_full	.404
111	vonegrcnn_47e	.403
112	BiT-S-R50x1	.401
113	inception_v2-tf	.401
114	mobilenet_v2_1_0_192	.401
115	vonegrcnn_62e_nobn	.401
116	voneresnet-50	.400
117	mobilenet_v2_1_0_160	.400
118	inception_v4-tf	.399
119	resnet-101_v1-tf	.399
120	mobilenet_v2_0.5_192-tf	.399
121	resnet50_l2_3_robust	.399
122	mobilenet_v2_1.0_128-tf	.398
123	mobilenet_v2_0.75_128	.398
124	resnet50-VITO-8deg-cc	.398
125	mobilenet_v2_0_75_160	.398
126	grcnn_109	.398
127	inception_v1-tf	.395
128	mobilenet_v2_0.75_96	.395
129	voneresnet-50-non_stochastic	.395
130	BiT-S-R152x2	.393
131	grcnn	.393
132	mobilenet_v1_0.75_160	.390
133	resnet50_linf_8_robust	.388
134	shufflenet_v2_x1_0	.386
135	cvt_cvt-21-384-in1k_4_LucyV4	.386
136	inception_v4	.386
137	mobilenet_v2_1.0_224-tf	.385
138	mobilenet_v2_1_0_128	.385
139	BiT-S-R50x3	.384
140	mobilenet_v2_0_75_224	.384
141	mobilenet_v2_1.4_224-tf	.383
142	inception_v1	.383
143	densenet-121-keras	.381
144	mobilenet_v1_0.75_128	.381
145	densenet-121	.381
146	antialiased-r50	.379
147	mobilenet_v2_0_75_192	.379
148	resnet-50_v1-tf	.378
149	resnet-18	.378
150	resnet_50_v2	.378
151	resnet_50_v1_spiking	.375
152	mobilenet_v2_1_0_224	.374
153	mobilenet_v1_0.75_192	.372
154	resnet-50-pytorch	.371
155	vgg-19-keras	.371
156	resnet50-sup	.371
157	mobilenet_v2_1.0_96	.371
158	resnet50_tutorial	.371
159	resnet_50_v1	.371
160	mobilenet_v2_1.3_224-tf	.370
161	mobilenet_v2_0_5_192	.370
162	mobilenet_v1_1.0_128	.369
163	ReAlnet03	.367
164	CORnet-S	.366
165	custom_model_cv_18_dagger_408	.366
166	ReAlnet07	.366
167	ReAlnet09	.366
168	mobilenet_v2_0_5_224	.365
169	ReAlnet10	.364
170	mobilenet_v2_0.35_192	.361
171	mobilenet_v2_0.5_224-tf	.360
172	resnet50	.359
173	ReAlnet08	.359
174	ReAlnet05	.359
175	mobilenet_v1_1.0_160	.356
176	mobilenet_v2_1_3_224	.356
177	mobilenet_v1_0.5_128	.355
178	ReAlnet04	.355
179	nasnet_mobile-tf	.354
180	Res2Net50_26w_4s	.354
181	mobilenet_v2_0.35_224	.352
182	resnet34_imagenet_full	.352
183	ReAlnet06	.352
184	xception	.352
185	mobilenet_v2_0.5_128	.351
186	resnet50_imagenet_full	.351
187	ReAlnet01	.351
188	mobilenet_v1_1.0_192	.347
189	mobilenet_v2_0.5_96	.346
190	mobilenet_v1_0.75_224	.346
191	resnet50_moco_v2	.346
192	mobilenet_v1_0.5_160	.345
193	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.344
194	mobilenet_v2_0.35_160	.344
195	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.343
196	BiT-S-R152x4	.341
197	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.341
198	ReAlnet02	.341
199	CLIP-RN50	.340
200	imagenet_l2_10_0	.338
201	pnasnet_large_pytorch	.338
202	resnet152_imagenet_full	.335
203	mobilenet_v2_0.35_128	.331
204	resnet101_imagenet_full	.331
205	mobilenet_v1_0.5_192	.329
206	resnet50_eMMCR_Vanilla	.329
207	resnet_152_v2	.328
208	mobilenet_v1_0.5_224	.325
209	mobilenet_v1_1.0_224	.325
210	resnet50-moclr8deg	.321
211	mobilenet_v2_0.35_96	.315
212	resnet18_imagenet_full	.315
213	mobilenet_v2_1-4_224_pytorch	.312
214	mobilenet_v2_1_4_224	.312
215	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.311
216	resnet-152_v2_pytorch	.309
217	vgg-16-keras	.306
218	alexnet_l2_3_robust	.306
219	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.305
220	alexnet_random_linf8_perturb	.305
221	squeezenet1_1	.302
222	alexnet_early_checkpoint	.302
223	mobilenet_v1_0.25_224	.301
224	alexnet	.299
225	mobilenet_v1_0.25_192	.297
226	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.290
227	0.5x_resnet-18_LC_w_sh_1_iter	.289
228	mobilenet_v1_0.25_160	.287
229	alexnet_robust_correct	.282
230	alexnet-baseline	.279
231	alexnet	.279
232	alexnet	.279
233	alexnet	.279
234	alexnet	.279
235	alexnet	.279
236	alexnet	.279
237	alexnet	.279
238	alexnet_ks_torevert	.279
239	alexnet_linf_8_robust	.265
240	vgg_16	.265
241	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.263
242	vgg_19	.255
243	squeezenet1_0	.254
244	mobilenet_v1_0.25_128	.253
245	resnet50_imagenet_100_seed-0	.251
246	resnet_50_v1_spiking_l4a	.241
247	deit_small_imagenet_100_seed-0	.184
248	resnet_50_v1_spiking_l4	.160
249	resnet50_imagenet_10_seed-0	.087
250	resnet50-vicregl0p9	.084
251	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.083
252	convnext_small_imagenet_10_seed-0	.072
253	mobilevit_small	.070
254	alexnet_training_seed_06	.070
255	convnext_small_imagenet_1_seed-0	.068
256	deit_small_imagenet_10_seed-0	.066
257	barlow-twins-resnet50	.065
258	alexnet_training_seed_09	.065
259	deit_small_imagenet_1_seed-0	.065
260	alexnet_training_seed_07	.064
261	resnet50-meshes-lt-100-original-scratch	.062
262	alexnet_training_seed_05	.062
263	alexnet_training_seed_02	.062
264	alexnet_training_seed_04	.062
265	alexnet_training_seed_10	.062
266	alexnet_training_seed_08	.062
267	alexnet_training_seed_01	.062
268	alexnet_training_seed_03	.062
269	resnet50-meshes-lt-100-original-pretrained	.061
270	artResNet18_1	.059
271	resnet-50_untrained	.058
272	resnet50-vicregl0p75	.058
273	resnet50_imagenet_1_seed-0	.055
274	resnet_50_v1_spiking_l3	.050
275	resnet50-simclr-vissl	.045
276	resnet50-barlow	.044
277	ViT-B/32
278	BiT-M-R50x1
279	resnet-18-LC_w_sh_100_iter_conv_init_m
280	resnet-18-LC_w_sh_1_iter_conv_init_m
281	BiT-M-R152x2
282	BiT-M-R50x3
283	resnet-18-LC_w_sh_10_iter_m
284	resnet-18_test_m
285	resnet-18-LC_w_sh_100_iter_m
286	resnet-18-LC_w_sh_10_iter_conv_init_m
287	FrankRobWobv0
288	resnet-18-LC_w_sh_1_iter_m
289	0.5x_resnet-18_LC
290	CORnetZ_CIFAR10
291	resnet18-local_aggregation
292	r3m_resnet18
293	CORnet-Z
294	vggface
295	my-model
296	resnet18-instance_recognition
297	resnet18-deepcluster
298	0.5x_resnet-18_LC_w_sh_10_iter
299	r3m_resnet50
300	r3m_resnet34
301	resnet-18-LC_m
302	resnet18-supervised
303	r3m_resnet50_nocrop
304	resnet-18-LC_1st_conv_m
305	resnet-18-LC_conv_init_m
306	resnet18-contrastive_multiview
307	0.5x_resnet-18
308	0.5x_resnet-18_LC_w_sh_100_iter
309	RN50
310	RN50
311	resnet18-colorization
312	resnet18-relative_position
313	ViT_L_32
314	resnet18-simclr
315	resnet18-autoencoder
316	ViT_B_32
317	resnet18-depth_prediction
318	ViT_B_16
319	r3m_resnet34_nocrop
320	resnet18-contrastive_predictive
321	resnet-18-LC_untrained
322	resnet-18_untrained
323	resnet-50x2_untrained
324	dcgan
325	unet_entire
326	CORnetZ_CIFAR10_bs32_20_04
327	pixels
328	dorinet_cornet_z
329	v1-pyr-nodown
330	omnivore_swinT
331	resnet50
332	openclip
333	vonealexnet_gaussian_noise_std4_fixed
334	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
335	yudixie_resnet50_category_class_0_240908
336	yudixie_resnet50_distance_reg_0_240908
337	resnet18_imagenet21kP
338	ConvLSTM
339	PredRNN
340	SimVP
341	TAU
342	briaai_rmbg_1_4
343	MIM
344	yolos_tiny
345	densenet_201_pytorch
346	yudixie_resnet18_distance_translation_0_240719
347	ReAlnet01_cornet
348	hmax
349	yudixie_resnet18_translation_rotation_0_240719
350	compact_resnet50_V4
351	ReAlnet02_cornet
352	yudixie_resnet18_translation_reg_0_240719
353	yudixie_resnet18_distance_reg_0_240719
354	yudixie_resnet18_rotation_reg_0_240719
355	yudixie_resnet18_imagenet1kpret_0_240719
356	yudixie_resnet50_distance_rotation_0_240908
357	yudixie_resnet50_rotation_reg_0_240908
358	yudixie_resnet50_distance_translation_0_240908
359	resnet18_ecoset_full
360	resnet34_ecoset_full
361	ReAlnet03_cornet
362	resnet50_ecoset_full
363	compact_resnet50_IT
364	resnet101_ecoset_full
365	resnet152_ecoset_full
366	compact_vgg19_V4
367	yudixie_resnet18_distance_rotation_0_240719
368	ReAlnet05_cornet
369	yudixie_resnet18_category_class_0_240719
370	yudixie_resnet18_random_0_240719
371	SeLaVi-AVE
372	compact_vgg19_IT
373	compact_resnet50_robust_V4
374	ReAlnet06_cornet
375	r3d_18
376	yudixie_resnet50_translation_rotation_0_240908
377	ReAlnet07_cornet
378	mc3_18
379	ReAlnet02_ALL
380	s3d
381	VOneCORnet-S
382	ReAlnet08_cornet
383	omnivore_swinB
384	resnet50-vicreg
385	mvit_v1_b
386	ReAlnet03_ALL
387	compact_cornets_V4
388	ReAlnet09_cornet
389	compact_cornets_IT
390	omnivore_swinS
391	SeLaVi-Kinetics400
392	ReAlnet05_ALL
393	SeLaVi-VGG-Sound
394	ReAlnet06_ALL
395	ReAlnet10_cornet
396	yudixie_resnet50_distance_translation_rotation_0_240908
397	compact_resnet50_robust_IT
398	yudixie_resnet50_random_0_240908
399	ReAlnet07_ALL
400	SeLaVi-Kinetics-Sound
401	mvit_v2_s
402	r2plus1d_18
403	yudixie_resnet50_imagenet1kpret_0_240908
404	ReAlnet08_ALL
405	yudixie_resnet18_object_class_0_240719
406	yudixie_resnet18_cat_obj_class_all_latents_0_240719
407	ReAlnet09_ALL
408	yudixie_resnet18_distance_translation_rotation_0_240719
409	yudixie_resnet50_imagenet1kpret_0_240312
410	yudixie_resnet50_translation_reg_0_240908
411	ReAlnet04_cornet
412	yudixie_resnet50_object_class_0_240908
413	resnet50_primary_visual_cortex
414	yudixie_resnet50_cat_obj_class_all_latents_0_240908
415	ReAlnet10_ALL
416	ReAlnet01_ALL
417	ReAlnet04_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: Geirhos2021stylized

Metric: top1