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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021contrast-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	.998
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.998
3	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.996
4	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.996
5	vit_large_patch14_clip_224:openai_ft_in1k	.995
6	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.995
7	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.994
8	convnext_large:fb_in22k_ft_in1k	.994
9	convnext_tiny_imagenet_full_seed-0	.993
10	convnext_base_imagenet_full_seed-0	.993
11	convnext_large_imagenet_full_seed-0	.991
12	convnext_xlarge:fb_in22k_ft_in1k	.990
13	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.990
14	convnext_base:clip_laiona_augreg_ft_in1k_384	.990
15	convnext_tiny_sup	.989
16	vit_base_patch16_clip_224:openai_ft_in1k	.989
17	vit_relpos_base_patch16_clsgap_224:sw_in1k	.989
18	focalnet_tiny_lrf_in1k	.988
19	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.988
20	resnext101_32x48d_wsl	.985
21	cvt_cvt-21-224-in1k_4_LucyV4	.985
22	resnet-152_v2_pytorch	.985
23	convnext_small_imagenet_full_seed-0	.985
24	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.984
25	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.984
26	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.984
27	cvt_cvt-21-384-in1k_4_LucyV4	.983
28	fixres_resnext101_32x48d_wsl	.981
29	resnext101_32x32d_wsl	.979
30	tv_efficientnet-b1	.979
31	resnet_101_v2	.978
32	pnasnet_large-tf	.976
33	nasnet_large-tf	.975
34	resnet50_ewconplus_wzc	.974
35	cvt_cvt-13-224-in1k_4_LucyV4	.974
36	resnext101_32x16d_wsl	.973
37	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.973
38	pnasnet_large	.971
39	cvt_cvt-13-384-in1k_4_LucyV4	.968
40	resnet50-vitoimagevidnet8	.968
41	resnext101_32x8d_wsl	.964
42	convnext_tiny:in12k_ft_in1k	.964
43	resnet50-VITO-8deg-cc	.963
44	nasnet_large	.963
45	inception_v4-tf	.958
46	xception	.955
47	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.955
48	resnet_152_v2	.955
49	resnet_50_v2	.951
50	vit_large_patch14_clip_336:laion2b_ft_in1k	.950
51	ViT_L_32_imagenet1k	.949
52	resnet-152_v2-tf	.949
53	effnetv2m_custom384	.949
54	inception_v3-tf	.948
55	resnet-101_v2-tf	.948
56	ViT_B_16_imagenet1k	.948
57	blt_vs	.946
58	inception_v4	.946
59	densenet-169-keras	.945
60	densenet-169	.945
61	resnet-50_v2-tf	.944
62	xception-keras	.941
63	vit_large_patch14_clip_224:laion2b_ft_in1k	.940
64	cv_18_dagger_408_pretrained	.939
65	resnet50-moclr8deg	.936
66	ViT_B_32_imagenet1k	.934
67	densenet-201-keras	.932
68	densenet-201	.932
69	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.924
70	inception_v2-tf	.915
71	mobilenet_v2_1-4_224_pytorch	.915
72	mobilenet_v2_1_4_224	.915
73	efficientnet_b0	.904
74	resnet50_moco_v2	.900
75	BiT-S-R50x1	.899
76	antialiased-rnext101_32x8d	.899
77	BiT-S-R101x3	.891
78	densenet-121-keras	.880
79	antialias-resnet152	.880
80	densenet-121	.880
81	BiT-S-R50x3	.877
82	BiT-S-R152x2	.876
83	BiT-S-R101x1	.873
84	resnet50-SIN_IN	.871
85	vgg-19-keras	.871
86	BiT-S-R152x4	.871
87	convnext_small_imagenet_100_seed-0	.866
88	AT_efficientnet-b2	.864
89	AdvProp_efficientnet-b2	.864
90	vgg-16-keras	.863
91	deit_large_imagenet_full_seed-0	.863
92	AdvProp_efficientnet-b0	.860
93	inception_v1-tf	.859
94	resnet50-SIN	.856
95	resnet50_eMMCR_Vanilla	.855
96	resnet-152_v1-tf	.853
97	nasnet_mobile	.853
98	effnetb1_272x240	.845
99	resnet-101_v1-tf	.844
100	grcnn_109	.843
101	AdvProp_efficientnet-b6	.840
102	CLIP-RN50	.839
103	resnet_101_v1	.839
104	efficientnet-b7	.830
105	Res2Net50_26w_4s	.829
106	resnet_152_v1	.829
107	resnet_SIN_IN_FT_IN	.823
108	resnet50-SIN_IN_IN	.823
109	deit_base_imagenet_full_seed-0	.819
110	resnet-50-pytorch	.816
111	resnet50-sup	.816
112	resnet50_tutorial	.816
113	resnet_50_v1	.816
114	resnet-50_v1-tf	.811
115	antialiased-r50	.805
116	convnext_femto_ols:d1_in1k	.804
117	AdvProp_efficientnet-b7	.796
118	mobilenet_v2_1.4_224-tf	.796
119	resnet-34	.796
120	AdvProp_efficientnet-b4	.795
121	AT_efficientnet-b4	.795
122	resnet_50_v1_spiking	.795
123	resnet152_imagenet_full	.794
124	grcnn	.790
125	nasnet_mobile-tf	.781
126	deit_small_imagenet_full_seed-0	.777
127	voneresnet-50-non_stochastic	.776
128	regnet_y_400mf	.776
129	resnet50	.771
130	resnet101_imagenet_full	.767
131	inception_v3	.766
132	CORnet-S	.762
133	resnet-18	.745
134	resnet50_imagenet_full	.738
135	mobilenet_v2_1.0_224-tf	.733
136	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.718
137	vgg_16	.713
138	efficientnet_b2_imagenet_full	.709
139	mobilenet_v2_1.3_224-tf	.706
140	ReAlnet04	.706
141	mobilenet_v2_1.0_192-tf	.705
142	vgg_19	.704
143	resnet34_imagenet_full	.703
144	ReAlnet09	.698
145	efficientnet_b1_imagenet_full	.689
146	mobilenet_v2_1_0_192	.688
147	ReAlnet05	.682
148	mobilenet_v1_1.0_224	.681
149	resnet_50_v1_spiking_l4a	.680
150	mobilenet_v1_0.75_224	.676
151	vonegrcnn_62e_nobn	.676
152	ReAlnet06	.674
153	AlexNet_SIN	.669
154	AlexNet_SIN_fov4	.669
155	AlexNet_SIN_fov12	.669
156	AlexNet_SIN_fov16	.669
157	ReAlnet02	.669
158	mobilenet_v2_1_0_224	.667
159	vonegrcnn_62e_nobn	.666
160	mobilenet_v2_1_3_224	.665
161	vonegrcnn_52e_full	.662
162	mobilenet_v2_1.0_160-tf	.661
163	resnet18_imagenet_full	.660
164	vonegrcnn_47e	.660
165	vonegrcnn_52e_full	.660
166	efficientnet_b0_imagenet_full	.654
167	ReAlnet01	.652
168	mobilenet_v2_0.75_192-tf	.651
169	vonegrcnn_47e	.649
170	mobilenet_v2_0.5_224-tf	.647
171	mobilenet_v1_0.5_224	.644
172	mobilenet_v2_0_75_192	.644
173	mobilenet_v1_0.75_192	.642
174	mobilenet_v1_0.75_160	.641
175	shufflenet_v2_x1_0	.641
176	mobilenet_v2_0.75_224-tf	.640
177	mobilenet_v1_1.0_192	.636
178	mobilenet_v2_0.75_160-tf	.636
179	ReAlnet03	.631
180	ReAlnet08	.624
181	voneresnet-50	.621
182	mobilenet_v2_1_0_160	.619
183	mobilenet_v1_1.0_160	.618
184	mobilenet_v2_0.5_192-tf	.615
185	mobilenet_v1_1.0_128	.608
186	AdvProp_efficientnet-b8	.599
187	ReAlnet10	.595
188	mobilenet_v2_0_75_160	.590
189	mobilenet_v1_0.5_160	.588
190	inception_v1	.585
191	ReAlnet07	.584
192	mobilenet_v2_0_5_224	.581
193	mobilenet_v1_0.5_192	.579
194	mobilenet_v2_0_5_192	.579
195	mobilenet_v1_0.75_128	.576
196	mobilenet_v2_0_75_224	.570
197	mobilenet_v2_0.35_224	.569
198	mobilenet_v2_0.5_160	.562
199	mobilenet_v2_0.75_128	.562
200	mobilenet_v2_0.35_192	.556
201	mobilenet_v2_1.0_128-tf	.552
202	mobilenet_v2_1.0_96	.547
203	mobilenet_v2_0.5_128	.544
204	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.541
205	mobilenet_v2_0.75_96	.539
206	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.539
207	squeezenet1_0	.526
208	squeezenet1_1	.520
209	mobilenet_v1_0.25_224	.507
210	mobilenet_v2_0.35_160	.505
211	0.5x_resnet-18_LC_w_sh_1_iter	.502
212	mobilenet_v2_1_0_128	.501
213	mobilenet_v1_0.5_128	.492
214	mobilenet_v1_0.25_192	.448
215	resnet_50_v1_spiking_l4	.439
216	alexnet-baseline	.435
217	alexnet	.435
218	alexnet	.435
219	alexnet	.435
220	alexnet	.435
221	alexnet	.435
222	alexnet	.435
223	alexnet	.435
224	alexnet_random_linf8_perturb	.435
225	alexnet	.435
226	alexnet_ks_torevert	.435
227	alexnet_fov12	.435
228	alexnet_fov4	.435
229	alexnet_fov16	.435
230	mobilenet_v1_0.25_160	.430
231	resnet50_robust_l2_eps1	.425
232	mobilenet_v2_0.5_96	.420
233	voneresnet-50-robust	.415
234	mobilenet_v2_0.35_128	.412
235	alexnet	.410
236	alexnet_early_checkpoint	.395
237	deit_small_imagenet_100_seed-0	.394
238	mobilenet_v2_0.35_96	.376
239	custom_model_cv_18_dagger_408	.369
240	mobilenet_v1_0.25_128	.365
241	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.346
242	resnet50_imagenet_100_seed-0	.343
243	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.309
244	resnet-50-robust	.302
245	imagenet_l2_3_0	.302
246	resnet50_robust_l2_eps3	.302
247	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.299
248	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.282
249	resnet50_l2_3_robust	.281
250	imagenet_l2_10_0	.198
251	alexnet_l2_3_robust	.195
252	alexnet_robust_correct	.194
253	resnet50_linf_8_robust	.172
254	alexnet_linf_8_robust	.124
255	resnet50_imagenet_10_seed-0	.100
256	resnet50-vicregl0p9	.100
257	resnet50-barlow	.083
258	convnext_small_imagenet_10_seed-0	.079
259	artResNet18_1	.075
260	resnet50_imagenet_1_seed-0	.072
261	deit_small_imagenet_1_seed-0	.072
262	convnext_small_imagenet_1_seed-0	.070
263	deit_small_imagenet_10_seed-0	.069
264	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.066
265	resnet-50_untrained	.065
266	barlow-twins-resnet50	.064
267	resnet_50_v1_spiking_l3	.064
268	resnet50-meshes-lt-100-original-pretrained	.062
269	resnet50-meshes-lt-100-original-scratch	.062
270	alexnet_training_seed_05	.062
271	alexnet_training_seed_06	.062
272	alexnet_training_seed_02	.062
273	alexnet_training_seed_04	.062
274	alexnet_training_seed_10	.062
275	alexnet_training_seed_07	.062
276	alexnet_training_seed_01	.062
277	alexnet_training_seed_01_fov4	.062
278	alexnet_training_seed_01_fov12	.062
279	alexnet_training_seed_01_fov16	.062
280	alexnet_training_seed_03	.062
281	alexnet_training_seed_09	.062
282	resnet50-vicregl0p75	.060
283	alexnet_training_seed_08	.052
284	resnet50-simclr-vissl	.041
285	ViT-B/32
286	resnet-18-LC_w_sh_100_iter_conv_init_m
287	BiT-M-R50x1
288	resnet-18-LC_w_sh_1_iter_conv_init_m
289	resnet-18-LC_w_sh_1_iter_m
290	BiT-M-R152x2
291	resnet-18-LC_w_sh_10_iter_m
292	BiT-M-R50x3
293	resnet-18-LC_w_sh_100_iter_m
294	resnet-18-LC_w_sh_10_iter_conv_init_m
295	FrankRobWobv0
296	0.5x_resnet-18_LC
297	CORnetZ_CIFAR10
298	resnet18-local_aggregation
299	r3m_resnet18
300	CORnet-Z
301	resnet18-deepcluster
302	vggface
303	my-model
304	resnet18-instance_recognition
305	0.5x_resnet-18_LC_w_sh_10_iter
306	r3m_resnet50
307	r3m_resnet34
308	resnet-18-LC_m
309	r3m_resnet50_nocrop
310	resnet18-supervised
311	resnet-18-LC_1st_conv_m
312	resnet-18-LC_conv_init_m
313	resnet18-contrastive_multiview
314	0.5x_resnet-18
315	RN50
316	0.5x_resnet-18_LC_w_sh_100_iter
317	RN50
318	resnet18-colorization
319	resnet18-relative_position
320	resnet18-autoencoder
321	resnet18-simclr
322	ViT_L_32
323	ViT_B_32
324	resnet18-depth_prediction
325	ViT_B_16
326	r3m_resnet34_nocrop
327	resnet18-contrastive_predictive
328	resnet-18-LC_untrained
329	resnet-18_untrained
330	resnet-50x2_untrained
331	dcgan
332	unet_entire
333	CORnetZ_CIFAR10_bs32_20_04
334	v1-pyr-nodown
335	pixels
336	dorinet_cornet_z
337	omnivore_swinT
338	resnet50
339	openclip
340	vonealexnet_gaussian_noise_std4_fixed
341	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
342	resnet18_ecoset_full
343	yudixie_resnet50_distance_reg_0_240908
344	resnet18_imagenet21kP
345	ConvLSTM
346	PredRNN
347	SimVP
348	yolos_tiny
349	TAU
350	hmax
351	briaai_rmbg_1_4
352	densenet_201_pytorch
353	pnasnet_large_pytorch
354	inception_v3_pytorch
355	compact_resnet50_V4
356	ReAlnet01_cornet
357	yudixie_resnet18_rotation_reg_0_240719
358	ReAlnet02_cornet
359	yudixie_resnet50_rotation_reg_0_240908
360	yudixie_resnet50_translation_reg_0_240908
361	yudixie_resnet50_distance_translation_0_240908
362	compact_resnet50_IT
363	yudixie_resnet50_distance_rotation_0_240908
364	ReAlnet01_cornet_fov4
365	ReAlnet04_cornet
366	ReAlnet03_cornet
367	ReAlnet01_cornet_fov12
368	resnet50_ecoset_full
369	yudixie_resnet50_translation_rotation_0_240908
370	resnet152_ecoset_full
371	compact_vgg19_V4
372	ReAlnet01_cornet_fov16
373	yudixie_resnet18_translation_rotation_0_240719
374	yudixie_resnet18_distance_rotation_0_240719
375	resnet-18_test_m
376	yudixie_resnet18_category_class_0_240719
377	ReAlnet05_cornet
378	yudixie_resnet18_random_0_240719
379	yudixie_resnet18_cat_obj_class_all_latents_0_240719
380	compact_vgg19_IT
381	SeLaVi-AVE
382	yudixie_resnet18_distance_translation_rotation_0_240719
383	yudixie_resnet18_object_class_0_240719
384	ReAlnet06_cornet
385	yudixie_resnet18_imagenet1kpret_0_240719
386	yudixie_resnet18_translation_reg_0_240719
387	yudixie_resnet18_distance_reg_0_240719
388	compact_resnet50_robust_V4
389	r3d_18
390	ReAlnet01_ALL
391	ReAlnet07_cornet
392	ReAlnet02_ALL
393	mc3_18
394	s3d
395	VOneCORnet-S
396	omnivore_swinB
397	resnet50-vicreg
398	ReAlnet08_cornet
399	mvit_v1_b
400	ReAlnet03_ALL
401	compact_cornets_V4
402	ReAlnet04_ALL
403	omnivore_swinS
404	ReAlnet05_ALL
405	compact_cornets_IT
406	SeLaVi-VGG-Sound
407	SeLaVi-Kinetics-Sound
408	ReAlnet10_cornet
409	ReAlnet06_ALL
410	yudixie_resnet50_imagenet1kpret_0_240908
411	compact_resnet50_robust_IT
412	mvit_v2_s
413	r2plus1d_18
414	ReAlnet07_ALL
415	yudixie_resnet50_distance_translation_rotation_0_240908
416	yudixie_resnet50_random_0_240908
417	SeLaVi-Kinetics400
418	resnet101_ecoset_full
419	ReAlnet08_ALL
420	resnet34_ecoset_full
421	yudixie_resnet50_cat_obj_class_all_latents_0_240908
422	yudixie_resnet50_object_class_0_240908
423	ReAlnet09_ALL
424	yudixie_resnet18_distance_translation_0_240719
425	yudixie_resnet50_imagenet1kpret_0_240312
426	resnet50_primary_visual_cortex
427	ReAlnet10_ALL
428	ReAlnet09_cornet
429	yudixie_resnet50_category_class_0_240908

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: Geirhos2021contrast

Metric: top1