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