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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021phasescrambling-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.895
2	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.894
3	vit_large_patch14_clip_224:openai_ft_in1k	.892
4	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.883
5	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.880
6	deit_large_imagenet_full_seed-0	.880
7	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.875
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.873
9	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.872
10	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.856
11	deit_base_imagenet_full_seed-0	.850
12	vit_large_patch14_clip_224:laion2b_ft_in1k	.844
13	vit_large_patch14_clip_336:laion2b_ft_in1k	.836
14	convnext_large:fb_in22k_ft_in1k	.836
15	convnext_xlarge:fb_in22k_ft_in1k	.833
16	focalnet_tiny_lrf_in1k	.822
17	convnext_base_imagenet_full_seed-0	.822
18	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.819
19	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.817
20	convnext_large_imagenet_full_seed-0	.817
21	convnext_small_imagenet_full_seed-0	.809
22	convnext_base:clip_laiona_augreg_ft_in1k_384	.805
23	vit_relpos_base_patch16_clsgap_224:sw_in1k	.803
24	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.800
25	cv_18_dagger_408_pretrained	.791
26	convnext_tiny_imagenet_full_seed-0	.789
27	convnext_tiny:in12k_ft_in1k	.786
28	effnetv2m_custom384	.784
29	vit_base_patch16_clip_224:openai_ft_in1k	.781
30	deit_small_imagenet_full_seed-0	.778
31	convnext_tiny_sup	.770
32	resnext101_32x32d_wsl	.769
33	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.767
34	fixres_resnext101_32x48d_wsl	.764
35	cvt_cvt-21-224-in1k_4_LucyV4	.761
36	cvt_cvt-21-384-in1k_4_LucyV4	.761
37	resnext101_32x16d_wsl	.753
38	resnext101_32x48d_wsl	.752
39	cvt_cvt-13-224-in1k_4_LucyV4	.750
40	resnext101_32x8d_wsl	.738
41	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.727
42	AdvProp_efficientnet-b7	.723
43	cvt_cvt-13-384-in1k_4_LucyV4	.717
44	convnext_femto_ols:d1_in1k	.713
45	resnet-152_v2_pytorch	.692
46	densenet-201-keras	.691
47	resnet50-vitoimagevidnet8	.691
48	densenet-201	.689
49	densenet_201_pytorch	.689
50	resnet_101_v2	.689
51	tv_efficientnet-b1	.684
52	resnet-101_v2-tf	.680
53	nasnet_large-tf	.678
54	AdvProp_efficientnet-b4	.678
55	AT_efficientnet-b4	.678
56	effnetb1_272x240	.675
57	nasnet_large	.672
58	AdvProp_efficientnet-b6	.670
59	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.666
60	resnet50-VITO-8deg-cc	.666
61	resnet50-SIN_IN	.664
62	BiT-S-R152x4	.661
63	BiT-S-R101x3	.661
64	resnet50-SIN	.661
65	grcnn_109	.661
66	AT_efficientnet-b2	.659
67	AdvProp_efficientnet-b2	.659
68	densenet-169	.658
69	voneresnet-50-robust	.656
70	resnet_101_v1	.656
71	resnet_152_v1	.656
72	BiT-S-R50x3	.655
73	inception_v1-tf	.653
74	densenet-169-keras	.653
75	efficientnet-b7	.653
76	resnet_50_v2	.653
77	Res2Net50_26w_4s	.652
78	resnet50_ewconplus_wzc	.652
79	BiT-S-R101x1	.650
80	inception_v2-tf	.650
81	BiT-S-R50x1	.648
82	AdvProp_efficientnet-b0	.648
83	antialias-resnet152	.648
84	densenet-121-keras	.645
85	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.645
86	pnasnet_large-tf	.644
87	resnet50_robust_l2_eps1	.644
88	densenet-121	.644
89	resnet-152_v2-tf	.641
90	inception_v4-tf	.639
91	pnasnet_large	.639
92	ViT_L_32_imagenet1k	.637
93	resnet_SIN_IN_FT_IN	.636
94	BiT-S-R152x2	.636
95	resnet50-SIN_IN_IN	.636
96	resnet-152_v1-tf	.633
97	antialiased-rnext101_32x8d	.633
98	grcnn	.633
99	resnet_152_v2	.633
100	xception-keras	.630
101	inception_v4	.628
102	resnet-50_v2-tf	.627
103	CLIP-RN50	.627
104	inception_v3_pytorch	.627
105	resnet-34	.625
106	inception_v3	.623
107	resnet101_imagenet_full	.622
108	resnet152_imagenet_full	.622
109	CORnet-S	.620
110	resnet-50-pytorch	.620
111	resnet50-sup	.620
112	resnet50_tutorial	.620
113	resnet_50_v1	.620
114	inception_v3-tf	.616
115	resnet-50_v1-tf	.614
116	antialiased-r50	.614
117	resnet50	.613
118	mobilenet_v2_1_3_224	.613
119	efficientnet_b2_imagenet_full	.609
120	mobilenet_v2_1.4_224-tf	.608
121	resnet-50-robust	.606
122	imagenet_l2_3_0	.606
123	resnet50_robust_l2_eps3	.606
124	resnet50_imagenet_full	.606
125	resnet-101_v1-tf	.605
126	vgg-19-keras	.602
127	vonegrcnn_47e	.598
128	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.597
129	mobilenet_v2_1.3_224-tf	.597
130	resnet_50_v1_spiking	.597
131	inception_v1	.597
132	efficientnet_b0	.595
133	voneresnet-50-non_stochastic	.595
134	custom_model_cv_18_dagger_408	.595
135	blt_vs	.595
136	efficientnet_b1_imagenet_full	.595
137	ViT_B_32_imagenet1k	.594
138	mobilenet_v2_1_0_192	.594
139	AlexNet_SIN	.594
140	resnet50_l2_3_robust	.592
141	shufflenet_v2_x1_0	.592
142	vonegrcnn_47e	.589
143	resnet50-moclr8deg	.589
144	mobilenet_v2_1.0_224-tf	.588
145	mobilenet_v2_1_0_160	.588
146	mobilenet_v2_1.0_192-tf	.584
147	voneresnet-50	.584
148	vgg_16	.584
149	vonegrcnn_62e_nobn	.583
150	vonegrcnn_62e_nobn	.583
151	vgg-16-keras	.580
152	resnet34_imagenet_full	.580
153	mobilenet_v2_0_75_224	.578
154	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.577
155	ReAlnet09	.577
156	ReAlnet10	.577
157	vonegrcnn_52e_full	.577
158	mobilenet_v2_0.75_192-tf	.575
159	vonegrcnn_52e_full	.573
160	resnet-18	.573
161	vgg_19	.573
162	ReAlnet06	.573
163	resnet50_moco_v2	.572
164	ReAlnet05	.572
165	mobilenet_v2_1.0_160-tf	.569
166	regnet_y_400mf	.567
167	ReAlnet03	.567
168	ReAlnet04	.567
169	nasnet_mobile	.566
170	resnet50_linf_8_robust	.564
171	mobilenet_v2_0_75_192	.564
172	mobilenet_v2_1_0_224	.562
173	ReAlnet01	.562
174	AdvProp_efficientnet-b8	.559
175	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.558
176	alexnet	.558
177	mobilenet_v2_0.75_224-tf	.556
178	efficientnet_b0_imagenet_full	.553
179	ReAlnet07	.553
180	ReAlnet08	.550
181	ReAlnet02	.550
182	resnet18_imagenet_full	.548
183	mobilenet_v1_0.75_224	.545
184	mobilenet_v1_1.0_224	.545
185	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.544
186	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.541
187	resnet50_eMMCR_Vanilla	.539
188	xception	.536
189	squeezenet1_1	.533
190	mobilenet_v1_1.0_192	.530
191	alexnet_random_linf8_perturb	.530
192	mobilenet_v2_0.75_160-tf	.525
193	alexnet_early_checkpoint	.523
194	imagenet_l2_10_0	.523
195	mobilenet_v2_1-4_224_pytorch	.522
196	mobilenet_v2_1_4_224	.522
197	alexnet-baseline	.520
198	alexnet	.520
199	alexnet	.520
200	alexnet	.520
201	alexnet	.520
202	alexnet	.520
203	alexnet	.520
204	alexnet	.520
205	alexnet_ks_torevert	.520
206	mobilenet_v2_1_0_128	.519
207	mobilenet_v2_0.75_128	.517
208	mobilenet_v2_0_5_192	.517
209	mobilenet_v1_1.0_160	.516
210	mobilenet_v2_0.5_192-tf	.511
211	mobilenet_v2_1.0_128-tf	.511
212	mobilenet_v1_0.75_192	.511
213	mobilenet_v2_0_75_160	.511
214	mobilenet_v2_0_5_224	.506
215	resnet_50_v1_spiking_l4a	.505
216	mobilenet_v2_0.5_224-tf	.503
217	mobilenet_v2_0.5_160	.503
218	mobilenet_v2_1.0_96	.498
219	convnext_small_imagenet_100_seed-0	.498
220	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.495
221	alexnet_l2_3_robust	.494
222	mobilenet_v1_1.0_128	.492
223	mobilenet_v2_0.5_128	.491
224	mobilenet_v1_0.75_160	.489
225	mobilenet_v1_0.5_224	.484
226	mobilenet_v2_0.35_224	.483
227	pnasnet_large_pytorch	.481
228	nasnet_mobile-tf	.470
229	alexnet_robust_correct	.470
230	mobilenet_v2_0.75_96	.463
231	mobilenet_v1_0.5_128	.463
232	mobilenet_v1_0.5_192	.461
233	mobilenet_v2_0.35_192	.461
234	squeezenet1_0	.458
235	mobilenet_v1_0.5_160	.458
236	mobilenet_v1_0.75_128	.445
237	mobilenet_v2_0.35_160	.445
238	alexnet_linf_8_robust	.436
239	mobilenet_v1_0.25_160	.430
240	mobilenet_v2_0.5_96	.427
241	mobilenet_v1_0.25_224	.420
242	mobilenet_v2_0.35_96	.417
243	mobilenet_v2_0.35_128	.412
244	0.5x_resnet-18_LC_w_sh_1_iter	.406
245	mobilenet_v1_0.25_128	.398
246	mobilenet_v1_0.25_192	.378
247	deit_small_imagenet_100_seed-0	.372
248	resnet50_imagenet_100_seed-0	.323
249	resnet_50_v1_spiking_l4	.286
250	convnext_small_imagenet_10_seed-0	.109
251	deit_small_imagenet_10_seed-0	.105
252	resnet50_imagenet_10_seed-0	.098
253	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.097
254	deit_small_imagenet_1_seed-0	.086
255	resnet50_imagenet_1_seed-0	.083
256	artResNet18_1	.080
257	resnet50-barlow	.073
258	alexnet_training_seed_06	.069
259	resnet_50_v1_spiking_l3	.067
260	resnet50-meshes-lt-100-original-scratch	.064
261	convnext_small_imagenet_1_seed-0	.064
262	barlow-twins-resnet50	.062
263	resnet50-simclr-vissl	.062
264	resnet-50_untrained	.062
265	alexnet_training_seed_05	.062
266	alexnet_training_seed_02	.062
267	alexnet_training_seed_04	.062
268	alexnet_training_seed_10	.062
269	alexnet_training_seed_08	.062
270	alexnet_training_seed_01	.062
271	alexnet_training_seed_03	.062
272	resnet50-meshes-lt-100-original-pretrained	.061
273	alexnet_training_seed_09	.061
274	alexnet_training_seed_07	.059
275	resnet50-vicregl0p9	.059
276	resnet50-vicregl0p75	.041
277	mobilevit_small	.037
278	ViT-B/32
279	resnet-18-LC_w_sh_100_iter_conv_init_m
280	BiT-M-R50x1
281	resnet-18-LC_w_sh_1_iter_conv_init_m
282	BiT-M-R152x2
283	resnet-18-LC_w_sh_1_iter_m
284	resnet-18-LC_w_sh_10_iter_m
285	BiT-M-R50x3
286	resnet-18-LC_w_sh_100_iter_m
287	resnet-18-LC_w_sh_10_iter_conv_init_m
288	FrankRobWobv0
289	0.5x_resnet-18_LC
290	CORnetZ_CIFAR10
291	resnet18-local_aggregation
292	r3m_resnet18
293	resnet18-deepcluster
294	CORnet-Z
295	vggface
296	resnet18-instance_recognition
297	my-model
298	0.5x_resnet-18_LC_w_sh_10_iter
299	r3m_resnet50
300	resnet-18-LC_m
301	r3m_resnet34
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	resnet18-autoencoder
314	resnet18-simclr
315	ViT_L_32
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	v1-pyr-nodown
328	pixels
329	dorinet_cornet_z
330	omnivore_swinT
331	resnet50
332	openclip
333	vonealexnet_gaussian_noise_std4_fixed
334	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
335	yudixie_resnet50_object_class_0_240908
336	yudixie_resnet50_imagenet1kpret_0_240312
337	yudixie_resnet50_category_class_0_240908
338	resnet18_imagenet21kP
339	yudixie_resnet50_distance_reg_0_240908
340	ConvLSTM
341	PredRNN
342	SimVP
343	briaai_rmbg_1_4
344	TAU
345	yolos_tiny
346	MIM
347	hmax
348	ReAlnet01_cornet
349	yudixie_resnet18_distance_translation_0_240719
350	compact_resnet50_V4
351	ReAlnet03_cornet
352	yudixie_resnet18_translation_reg_0_240719
353	yudixie_resnet18_rotation_reg_0_240719
354	yudixie_resnet18_imagenet1kpret_0_240719
355	ReAlnet02_cornet
356	yudixie_resnet50_distance_translation_0_240908
357	yudixie_resnet50_rotation_reg_0_240908
358	yudixie_resnet50_distance_rotation_0_240908
359	resnet18_ecoset_full
360	VOneCORnet-S
361	resnet34_ecoset_full
362	resnet50_ecoset_full
363	compact_resnet50_IT
364	resnet101_ecoset_full
365	compact_vgg19_V4
366	resnet152_ecoset_full
367	ReAlnet04_cornet
368	yudixie_resnet18_translation_rotation_0_240719
369	yudixie_resnet18_distance_rotation_0_240719
370	resnet-18_test_m
371	yudixie_resnet18_category_class_0_240719
372	yudixie_resnet18_random_0_240719
373	yudixie_resnet18_cat_obj_class_all_latents_0_240719
374	yudixie_resnet50_translation_reg_0_240908
375	compact_resnet50_robust_V4
376	ReAlnet05_cornet
377	yudixie_resnet18_distance_translation_rotation_0_240719
378	compact_vgg19_IT
379	yudixie_resnet18_object_class_0_240719
380	ReAlnet06_cornet
381	SeLaVi-AVE
382	r3d_18
383	ReAlnet04_ALL
384	yudixie_resnet50_translation_rotation_0_240908
385	yudixie_resnet18_distance_reg_0_240719
386	ReAlnet07_cornet
387	mc3_18
388	ReAlnet02_ALL
389	s3d
390	omnivore_swinB
391	ReAlnet08_cornet
392	mvit_v1_b
393	resnet50-vicreg
394	ReAlnet01_ALL
395	compact_cornets_V4
396	ReAlnet03_ALL
397	ReAlnet09_cornet
398	compact_cornets_IT
399	ReAlnet05_ALL
400	omnivore_swinS
401	SeLaVi-Kinetics400
402	SeLaVi-VGG-Sound
403	ReAlnet06_ALL
404	SeLaVi-Kinetics-Sound
405	compact_resnet50_robust_IT
406	ReAlnet10_cornet
407	r2plus1d_18
408	yudixie_resnet50_random_0_240908
409	ReAlnet07_ALL
410	yudixie_resnet50_distance_translation_rotation_0_240908
411	mvit_v2_s
412	ReAlnet08_ALL
413	yudixie_resnet50_imagenet1kpret_0_240908
414	ReAlnet09_ALL
415	yudixie_resnet50_cat_obj_class_all_latents_0_240908
416	ReAlnet10_ALL
417	resnet50_primary_visual_cortex

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: Geirhos2021phasescrambling

Metric: top1