Leaderboard Explore Compare Profile Community Blog FAQ Competition 2024 Report Issue

Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021powerequalisation-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	.979
2	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.970
3	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.968
4	vit_large_patch14_clip_224:openai_ft_in1k	.964
5	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.964
6	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.963
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.963
8	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.959
9	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.959
10	convnext_large:fb_in22k_ft_in1k	.948
11	vit_large_patch14_clip_336:laion2b_ft_in1k	.945
12	convnext_xlarge:fb_in22k_ft_in1k	.943
13	deit_large_imagenet_full_seed-0	.943
14	vit_large_patch14_clip_224:laion2b_ft_in1k	.939
15	convnext_large_imagenet_full_seed-0	.938
16	convnext_base:clip_laiona_augreg_ft_in1k_384	.936
17	convnext_small_imagenet_full_seed-0	.934
18	convnext_base_imagenet_full_seed-0	.934
19	deit_base_imagenet_full_seed-0	.929
20	convnext_tiny:in12k_ft_in1k	.927
21	effnetv2m_custom384	.923
22	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.914
23	resnext101_32x32d_wsl	.911
24	vit_relpos_base_patch16_clsgap_224:sw_in1k	.911
25	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.909
26	fixres_resnext101_32x48d_wsl	.907
27	cv_18_dagger_408_pretrained	.905
28	focalnet_tiny_lrf_in1k	.905
29	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.902
30	resnext101_32x16d_wsl	.902
31	resnext101_32x48d_wsl	.900
32	vit_base_patch16_clip_224:openai_ft_in1k	.896
33	resnext101_32x8d_wsl	.896
34	convnext_tiny_imagenet_full_seed-0	.893
35	AdvProp_efficientnet-b7	.891
36	AdvProp_efficientnet-b6	.875
37	convnext_tiny_sup	.875
38	cvt_cvt-13-224-in1k_4_LucyV4	.871
39	deit_small_imagenet_full_seed-0	.868
40	nasnet_large-tf	.864
41	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.863
42	resnet_101_v2	.854
43	resnet-152_v2_pytorch	.850
44	cvt_cvt-21-384-in1k_4_LucyV4	.850
45	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.845
46	cvt_cvt-21-224-in1k_4_LucyV4	.841
47	nasnet_large	.841
48	convnext_femto_ols:d1_in1k	.836
49	AdvProp_efficientnet-b4	.834
50	AT_efficientnet-b4	.834
51	densenet-201	.832
52	densenet_201_pytorch	.832
53	densenet-201-keras	.832
54	inception_v4-tf	.830
55	densenet-169	.825
56	densenet-169-keras	.823
57	cvt_cvt-13-384-in1k_4_LucyV4	.823
58	resnet_50_v2	.823
59	efficientnet-b7	.820
60	pnasnet_large-tf	.814
61	resnet-101_v2-tf	.812
62	pnasnet_large_halfPrecision	.812
63	pnasnet_large	.812
64	inception_v4	.807
65	inception_v3-tf	.807
66	resnet50-SIN	.807
67	inception_v2-tf	.804
68	inception_v3_pytorch	.804
69	tv_efficientnet-b1	.804
70	resnet_152_v2	.804
71	AdvProp_efficientnet-b0	.802
72	xception-keras	.800
73	resnet50-SIN_IN	.798
74	antialiased-rnext101_32x8d	.798
75	ViT_L_32_imagenet1k	.796
76	AT_efficientnet-b2	.796
77	AdvProp_efficientnet-b2	.796
78	effnetb1_272x240	.793
79	antialias-resnet152	.793
80	resnet_152_v1	.793
81	inception_v3	.791
82	BiT-S-R152x2	.787
83	BiT-S-R50x3	.786
84	resnet-152_v2-tf	.786
85	grcnn_109	.786
86	BiT-S-R152x4	.780
87	resnet-50_v2-tf	.780
88	Res2Net50_26w_4s	.780
89	BiT-S-R101x1	.777
90	densenet-121-keras	.775
91	BiT-S-R101x3	.775
92	BiT-S-R50x1	.773
93	densenet-121	.773
94	resnet50-vitoimagevidnet8	.770
95	inception_v1-tf	.766
96	ViT_B_32_imagenet1k	.766
97	resnet50_ewconplus_wzc	.766
98	resnet_101_v1	.766
99	resnet101_imagenet_full	.757
100	resnet_SIN_IN_FT_IN	.755
101	resnet50-SIN_IN_IN	.755
102	resnet50-VITO-8deg-cc	.754
103	grcnn	.752
104	efficientnet_b0	.743
105	resnet-152_v1-tf	.738
106	vonegrcnn_47e	.736
107	resnet50_tutorial	.732
108	resnet_50_v1	.732
109	resnet-50-pytorch	.732
110	resnet50-sup	.732
111	vonegrcnn_47e	.730
112	resnet50-moclr8deg	.730
113	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.730
114	antialiased-r50	.729
115	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.727
116	AdvProp_efficientnet-b8	.725
117	efficientnet_b2_imagenet_full	.720
118	vonegrcnn_62e_nobn	.720
119	resnet-50_v1-tf	.720
120	resnet50_imagenet_full	.718
121	resnet152_imagenet_full	.716
122	resnet50	.711
123	vonegrcnn_52e_full	.709
124	resnet-101_v1-tf	.709
125	vonegrcnn_62e_nobn	.707
126	CORnet-S	.707
127	xception	.704
128	resnet-34	.698
129	resnet_50_v1_spiking	.698
130	CLIP-RN50	.696
131	resnet50_moco_v2	.696
132	resnet50_robust_l2_eps1	.696
133	voneresnet-50-non_stochastic	.695
134	vgg-19-keras	.691
135	resnet34_imagenet_full	.691
136	efficientnet_b1_imagenet_full	.691
137	vonegrcnn_52e_full	.686
138	inception_v1	.684
139	mobilenet_v2_1.4_224-tf	.682
140	mobilenet_v2_1.3_224-tf	.679
141	voneresnet-50-robust	.679
142	mobilenet_v2_1_3_224	.679
143	ReAlnet06	.673
144	AlexNet_SIN	.671
145	AlexNet_SIN_fov4	.671
146	AlexNet_SIN_fov12	.671
147	AlexNet_SIN_fov16	.671
148	nasnet_mobile	.670
149	eBarlow_lmda_02_1000ep	.664
150	mobilenet_v2_1.0_224-tf	.664
151	vgg-16-keras	.661
152	efficientnet_b0_imagenet_full	.657
153	pnasnet_large_pytorch	.657
154	voneresnet-50	.652
155	vgg_16	.652
156	vgg_19	.648
157	ReAlnet04	.648
158	ReAlnet01	.645
159	mobilenet_v2_0.75_192-tf	.641
160	mobilenet_v2_1_0_224	.639
161	mobilenet_v2_1.0_192-tf	.637
162	mobilenet_v1_1.0_192	.637
163	regnet_y_400mf	.637
164	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.636
165	resnet18_imagenet_full	.632
166	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.632
167	resnet-18	.630
168	mobilenet_v2_1_0_192	.630
169	ReAlnet09	.627
170	mobilenet_v2_0.75_224-tf	.627
171	mobilenet_v2_0_75_192	.625
172	blt_vs	.625
173	ReAlnet03	.625
174	custom_model_cv_18_dagger_408	.623
175	ReAlnet08	.621
176	ReAlnet05	.621
177	mobilenet_v2_1.0_160-tf	.620
178	mobilenet_v1_1.0_224	.616
179	ReAlnet07	.613
180	ReAlnet10	.611
181	ReAlnet02	.609
182	nasnet_mobile-tf	.609
183	resnet-50-robust	.609
184	imagenet_l2_3_0	.609
185	resnet50_robust_l2_eps3	.609
186	mobilenet_v2_0_75_224	.605
187	mobilenet_v2_1_0_160	.604
188	shufflenet_v2_x1_0	.598
189	resnet50_eMMCR_Vanilla	.598
190	mobilenet_v2_1-4_224_pytorch	.596
191	mobilenet_v2_1_4_224	.596
192	mobilenet_v1_0.75_224	.596
193	mobilenet_v2_1.0_128-tf	.586
194	mobilenet_v1_1.0_160	.571
195	mobilenet_v2_1_0_128	.570
196	resnet50_l2_3_robust	.570
197	mobilenet_v2_0_5_224	.559
198	resnet_50_v1_spiking_l4a	.554
199	convnext_small_imagenet_100_seed-0	.554
200	mobilenet_v2_0_75_160	.552
201	mobilenet_v2_0.5_224-tf	.548
202	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.541
203	mobilenet_v1_0.75_192	.539
204	mobilenet_v1_0.75_160	.539
205	mobilenet_v1_1.0_128	.537
206	mobilenet_v2_0.75_160-tf	.537
207	resnet50_linf_8_robust	.536
208	mobilenet_v2_0_5_192	.532
209	mobilenet_v2_0.75_128	.532
210	alexnet_random_linf8_perturb	.532
211	mobilenet_v1_0.5_224	.530
212	mobilenet_v2_0.5_160	.523
213	mobilenet_v2_0.5_192-tf	.521
214	alexnet	.505
215	alexnet_ks_torevert	.505
216	alexnet_fov4	.505
217	alexnet_fov12	.505
218	alexnet_fov16	.505
219	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.504
220	squeezenet1_1	.498
221	mobilenet_v1_0.5_160	.498
222	alexnet_early_checkpoint	.498
223	mobilenet_v2_0.35_192	.496
224	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.495
225	mobilenet_v1_0.5_192	.495
226	mobilenet_v2_1.0_96	.491
227	mobilenet_v1_0.75_128	.484
228	mobilenet_v2_0.5_128	.482
229	mobilenet_v2_0.35_224	.461
230	squeezenet1_0	.457
231	mobilenet_v1_0.5_128	.457
232	mobilenet_v2_0.35_160	.454
233	mobilenet_v2_0.75_96	.445
234	imagenet_l2_10_0	.425
235	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.412
236	0.5x_resnet-18_LC_w_sh_1_iter	.409
237	mobilenet_v2_0.35_128	.396
238	mobilenet_v2_0.5_96	.366
239	mobilenet_v1_0.25_224	.366
240	alexnet_robust_correct	.362
241	mobilenet_v2_0.35_96	.354
242	mobilenet_v1_0.25_160	.339
243	alexnet_l2_3_robust	.327
244	mobilenet_v1_0.25_192	.316
245	alexnet_linf_8_robust	.296
246	mobilenet_v1_0.25_128	.284
247	deit_small_imagenet_100_seed-0	.282
248	resnet_50_v1_spiking_l4	.279
249	resnet50_imagenet_100_seed-0	.266
250	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.084
251	resnet50-vicregl0p9	.077
252	convnext_small_imagenet_10_seed-0	.075
253	resnet50_imagenet_10_seed-0	.073
254	resnet_50_v1_spiking_l3	.073
255	deit_small_imagenet_10_seed-0	.066
256	resnet50_imagenet_1_seed-0	.066
257	alexnet_training_seed_09	.064
258	convnext_small_imagenet_1_seed-0	.064
259	resnet50-vicregl0p75	.064
260	resnet-50_untrained	.062
261	resnet50-meshes-lt-100-original-pretrained	.062
262	alexnet_training_seed_05	.062
263	mobilevit_small	.062
264	alexnet_training_seed_02	.062
265	alexnet_training_seed_04	.062
266	alexnet_training_seed_08	.062
267	alexnet_training_seed_10	.062
268	alexnet_training_seed_01	.062
269	deit_small_imagenet_1_seed-0	.062
270	alexnet_training_seed_01_fov4	.062
271	alexnet_training_seed_01_fov12	.062
272	alexnet_training_seed_01_fov16	.062
273	alexnet_training_seed_03	.062
274	alexnet_training_seed_06	.059
275	alexnet_training_seed_07	.057
276	resnet50-meshes-lt-100-original-scratch	.057
277	resnet50-barlow	.052
278	artResNet18_1	.048
279	barlow-twins-resnet50	.048
280	resnet50-simclr-vissl	.039
281	ViT-B/32
282	resnet-18-LC_w_sh_100_iter_conv_init_m
283	BiT-M-R50x1
284	resnet-18-LC_w_sh_1_iter_conv_init_m
285	BiT-M-R152x2
286	resnet-18-LC_w_sh_1_iter_m
287	BiT-M-R50x3
288	resnet-18-LC_w_sh_10_iter_m
289	resnet-18-LC_w_sh_100_iter_m
290	resnet-18-LC_w_sh_10_iter_conv_init_m
291	FrankRobWobv0
292	0.5x_resnet-18_LC
293	CORnetZ_CIFAR10
294	resnet18-local_aggregation
295	r3m_resnet18
296	resnet18-deepcluster
297	CORnet-Z
298	vggface
299	resnet18-instance_recognition
300	my-model
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	ReAlnet08_ALL
339	yudixie_resnet50_imagenet1kpret_0_240312
340	yudixie_resnet50_category_class_0_240908
341	yudixie_resnet50_distance_reg_0_240908
342	resnet18_imagenet21kP
343	ConvLSTM
344	PredRNN
345	SimVP
346	TAU
347	yolos_tiny
348	MIM
349	hmax
350	ReAlnet01_cornet
351	yudixie_resnet18_distance_translation_0_240719
352	briaai_rmbg_1_4
353	vit_base_patch16_dinov3-lvd1689m
354	ReAlnet03_cornet
355	yudixie_resnet18_distance_reg_0_240719
356	yudixie_resnet18_rotation_reg_0_240719
357	yudixie_resnet18_imagenet1kpret_0_240719
358	compact_resnet50_V4
359	ReAlnet02_cornet
360	yudixie_resnet50_rotation_reg_0_240908
361	yudixie_resnet50_distance_rotation_0_240908
362	yudixie_resnet50_distance_translation_0_240908
363	compact_resnet50_IT
364	resnet18_ecoset_full
365	vit_base_patch14_dinov2-lvd142m
366	VOneCORnet-S
367	ReAlnet01_cornet_fov4
368	ReAlnet04_cornet
369	resnet50_ecoset_full
370	resnet101_ecoset_full
371	ReAlnet01_cornet_fov12
372	vit_base_patch14_reg4_dinov2-lvd142m
373	resnet152_ecoset_full
374	compact_vgg19_V4
375	yudixie_resnet18_translation_rotation_0_240719
376	aughost_fair_resnet18
377	yudixie_resnet18_distance_rotation_0_240719
378	resnet-18_test_m
379	yudixie_resnet50_translation_reg_0_240908
380	yudixie_resnet18_category_class_0_240719
381	ReAlnet05_cornet
382	yudixie_resnet18_random_0_240719
383	yudixie_resnet18_cat_obj_class_all_latents_0_240719
384	yudixie_resnet18_translation_reg_0_240719
385	compact_vgg19_IT
386	yudixie_resnet18_distance_translation_rotation_0_240719
387	yudixie_resnet18_object_class_0_240719
388	SeLaVi-AVE
389	ReAlnet06_cornet
390	compact_resnet50_robust_V4
391	r3d_18
392	ReAlnet01_cornet_fov16
393	yudixie_resnet50_translation_rotation_0_240908
394	ReAlnet01_ALL
395	ReAlnet07_cornet
396	mc3_18
397	ReAlnet02_ALL
398	s3d
399	omnivore_swinB
400	ReAlnet08_cornet
401	resnet50-vicreg
402	mvit_v1_b
403	ReAlnet04_ALL
404	compact_cornets_V4
405	ReAlnet03_ALL
406	compact_cornets_IT
407	ReAlnet09_cornet
408	omnivore_swinS
409	ReAlnet05_ALL
410	SeLaVi-Kinetics400
411	SeLaVi-VGG-Sound
412	ReAlnet06_ALL
413	SeLaVi-Kinetics-Sound
414	compact_resnet50_robust_IT
415	yudixie_resnet50_distance_translation_rotation_0_240908
416	yudixie_resnet50_random_0_240908
417	r2plus1d_18
418	mvit_v2_s
419	yudixie_resnet50_imagenet1kpret_0_240908
420	ReAlnet07_ALL
421	resnet34_ecoset_full
422	yudixie_resnet50_cat_obj_class_all_latents_0_240908
423	ReAlnet09_ALL
424	yudixie_resnet50_object_class_0_240908
425	ReAlnet10_cornet
426	resnet50_primary_visual_cortex
427	ReAlnet10_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: Geirhos2021powerequalisation

Metric: top1