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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021colour-top1")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	vit_large_patch14_clip_224:laion2b_ft_in1k	.998
2	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.998
3	vit_large_patch14_clip_336:laion2b_ft_in1k	.998
4	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.998
5	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.998
6	resnext101_32x32d_wsl	.997
7	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.997
8	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.997
9	convnext_base:clip_laiona_augreg_ft_in1k_384	.997
10	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.997
11	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.995
12	vit_large_patch14_clip_224:openai_ft_in1k	.995
13	convnext_xlarge:fb_in22k_ft_in1k	.995
14	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.995
15	convnext_large_imagenet_full_seed-0	.995
16	fixres_resnext101_32x48d_wsl	.994
17	resnext101_32x48d_wsl	.994
18	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.994
19	vit_base_patch16_clip_224:openai_ft_in1k	.994
20	vit_relpos_base_patch16_clsgap_224:sw_in1k	.994
21	convnext_large:fb_in22k_ft_in1k	.994
22	AdvProp_efficientnet-b7	.992
23	deit_large_imagenet_full_seed-0	.992
24	deit_base_imagenet_full_seed-0	.992
25	nasnet_large-tf	.991
26	effnetv2m_custom384	.991
27	resnext101_32x16d_wsl	.991
28	focalnet_tiny_lrf_in1k	.991
29	convnext_base_imagenet_full_seed-0	.991
30	convnext_tiny:in12k_ft_in1k	.991
31	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.991
32	convnext_tiny_imagenet_full_seed-0	.991
33	nasnet_large	.991
34	pnasnet_large-tf	.989
35	resnext101_32x8d_wsl	.989
36	AdvProp_efficientnet-b6	.989
37	convnext_tiny_sup	.989
38	antialiased-rnext101_32x8d	.989
39	convnext_small_imagenet_full_seed-0	.989
40	AT_efficientnet-b2	.988
41	AdvProp_efficientnet-b2	.988
42	cvt_cvt-13-224-in1k_4_LucyV4	.988
43	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.988
44	inception_v3-tf	.986
45	inception_v4-tf	.986
46	cvt_cvt-21-224-in1k_4_LucyV4	.986
47	pnasnet_large_halfPrecision	.986
48	pnasnet_large	.986
49	BiT-S-R152x4	.984
50	AdvProp_efficientnet-b0	.984
51	antialias-resnet152	.984
52	eBarlow_lmda_02_1000ep	.984
53	BiT-S-R50x3	.983
54	BiT-S-R101x3	.983
55	cv_18_dagger_408_pretrained	.983
56	cvt_cvt-13-384-in1k_4_LucyV4	.983
57	resnet-152_v2_pytorch	.983
58	cvt_cvt-21-384-in1k_4_LucyV4	.983
59	AdvProp_efficientnet-b4	.981
60	resnet-152_v2-tf	.981
61	AT_efficientnet-b4	.981
62	ViT_B_16_imagenet1k	.981
63	antialiased-r50	.981
64	deit_small_imagenet_full_seed-0	.981
65	inception_v4	.981
66	resnet_101_v2	.981
67	resnet-101_v2-tf	.980
68	resnet_152_v1	.980
69	resnet50_ewconplus_wzc	.980
70	resnet50-vitoimagevidnet8	.980
71	resnet_SIN_IN_FT_IN	.978
72	BiT-S-R101x1	.978
73	resnet50-SIN_IN_IN	.978
74	densenet-201-keras	.978
75	inception_v2-tf	.978
76	Res2Net50_26w_4s	.978
77	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.978
78	densenet-201	.978
79	densenet_201_pytorch	.978
80	resnet152_imagenet_full	.978
81	grcnn	.978
82	efficientnet-b7	.977
83	resnet50-SIN_IN	.977
84	resnet-50_v1-tf	.977
85	effnetb1_272x240	.977
86	resnet50-moclr8deg	.977
87	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.977
88	resnet_101_v1	.977
89	BiT-S-R152x2	.975
90	BiT-S-R50x1	.975
91	efficientnet_b0	.975
92	resnet-101_v1-tf	.975
93	densenet-169-keras	.975
94	xception-keras	.975
95	resnet50_moco_v2	.975
96	resnet101_imagenet_full	.975
97	resnet_152_v2	.975
98	densenet-169	.975
99	ViT_L_32_imagenet1k	.973
100	resnet-152_v1-tf	.973
101	inception_v3	.973
102	voneresnet-50-non_stochastic	.972
103	resnet-50_v2-tf	.972
104	CORnet-S	.972
105	resnet50_imagenet_full	.972
106	tv_efficientnet-b1	.972
107	grcnn_109	.972
108	convnext_femto_ols:d1_in1k	.972
109	vonegrcnn_47e	.970
110	densenet-121-keras	.969
111	vonegrcnn_62e_nobn	.969
112	resnet-50-pytorch	.969
113	mobilenet_v2_1.4_224-tf	.969
114	mobilenet_v2_1.3_224-tf	.969
115	vonegrcnn_47e	.969
116	vgg-16-keras	.969
117	vgg-19-keras	.969
118	resnet50-sup	.969
119	resnet50_tutorial	.969
120	resnet_50_v1	.969
121	densenet-121	.969
122	mobilenet_v2_1.0_192-tf	.967
123	efficientnet_b2_imagenet_full	.967
124	mobilenet_v2_1.0_224-tf	.966
125	voneresnet-50	.966
126	vgg_19	.966
127	resnet50-VITO-8deg-cc	.966
128	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.966
129	resnet_50_v2	.966
130	mobilenet_v2_1.0_160-tf	.964
131	resnet50	.964
132	mobilenet_v2_1_3_224	.964
133	efficientnet_b1_imagenet_full	.963
134	nasnet_mobile	.963
135	vonegrcnn_52e_full	.963
136	resnet-34	.961
137	regnet_y_400mf	.961
138	vgg_16	.961
139	vonegrcnn_62e_nobn	.961
140	vonegrcnn_52e_full	.959
141	ViT_B_32_imagenet1k	.959
142	resnet50_robust_l2_eps1	.959
143	xception	.959
144	mobilenet_v1_1.0_224	.958
145	custom_model_cv_18_dagger_408	.956
146	resnet_50_v1_spiking	.956
147	inception_v1-tf	.955
148	resnet-18	.955
149	resnet34_imagenet_full	.955
150	resnet18_imagenet_full	.955
151	mobilenet_v1_0.75_224	.952
152	mobilenet_v1_1.0_192	.952
153	mobilenet_v1_1.0_160	.952
154	blt_vs	.952
155	ReAlnet06	.948
156	resnet50_eMMCR_Vanilla	.948
157	efficientnet_b0_imagenet_full	.947
158	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.945
159	AdvProp_efficientnet-b8	.944
160	mobilenet_v1_0.75_192	.944
161	ReAlnet09	.944
162	ReAlnet03	.944
163	mobilenet_v2_0.75_192-tf	.941
164	mobilenet_v2_1.0_128-tf	.939
165	mobilenet_v2_0.75_224-tf	.939
166	mobilenet_v1_0.5_224	.939
167	voneresnet-50-robust	.939
168	mobilenet_v2_1_0_192	.939
169	ReAlnet02	.939
170	mobilenet_v1_0.75_160	.938
171	ReAlnet07	.938
172	shufflenet_v2_x1_0	.936
173	mobilenet_v2_1_0_224	.936
174	mobilenet_v2_0.75_160-tf	.934
175	ReAlnet01	.934
176	nasnet_mobile-tf	.931
177	ReAlnet04	.931
178	mobilenet_v2_1_0_160	.931
179	inception_v1	.930
180	ReAlnet05	.930
181	ReAlnet08	.927
182	ReAlnet10	.925
183	mobilenet_v2_0.5_224-tf	.923
184	mobilenet_v1_1.0_128	.923
185	mobilenet_v2_0_75_224	.923
186	mobilenet_v2_0_75_192	.923
187	resnet50-SIN	.922
188	mobilenet_v2_0_75_160	.920
189	mobilenet_v2_0.75_128	.919
190	resnet_50_v1_spiking_l4a	.917
191	mobilenet_v2_0.5_192-tf	.912
192	0.5x_resnet-18_LC_w_sh_1_iter	.912
193	mobilenet_v1_0.75_128	.911
194	mobilenet_v2_1_0_128	.911
195	mobilenet_v1_0.5_192	.909
196	CLIP-RN50	.908
197	mobilenet_v2_1.0_96	.906
198	mobilenet_v2_0.5_160	.905
199	mobilenet_v1_0.5_160	.903
200	mobilenet_v2_0_5_224	.903
201	mobilenet_v2_0.75_96	.902
202	resnet-50-robust	.902
203	imagenet_l2_3_0	.902
204	resnet50_robust_l2_eps3	.902
205	mobilenet_v2_1-4_224_pytorch	.902
206	mobilenet_v2_1_4_224	.902
207	mobilenet_v2_0.35_192	.900
208	mobilenet_v2_0_5_192	.900
209	resnet50_l2_3_robust	.889
210	mobilenet_v2_0.35_224	.884
211	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.884
212	mobilenet_v2_0.5_128	.884
213	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.883
214	mobilenet_v2_0.35_160	.872
215	squeezenet1_1	.870
216	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.866
217	alexnet	.864
218	alexnet_random_linf8_perturb	.864
219	alexnet_ks_torevert	.864
220	alexnet_fov4	.864
221	alexnet_fov12	.864
222	alexnet_fov16	.864
223	mobilenet_v1_0.5_128	.863
224	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.859
225	convnext_small_imagenet_100_seed-0	.859
226	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.850
227	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.844
228	squeezenet1_0	.842
229	mobilenet_v1_0.25_224	.833
230	alexnet_early_checkpoint	.830
231	mobilenet_v2_0.5_96	.823
232	mobilenet_v1_0.25_192	.819
233	mobilenet_v2_0.35_128	.819
234	resnet50_linf_8_robust	.812
235	mobilenet_v1_0.25_160	.808
236	mobilenet_v2_0.35_96	.797
237	AlexNet_SIN	.797
238	AlexNet_SIN_fov4	.797
239	AlexNet_SIN_fov12	.797
240	AlexNet_SIN_fov16	.797
241	imagenet_l2_10_0	.781
242	mobilenet_v1_0.25_128	.777
243	alexnet_l2_3_robust	.767
244	alexnet_robust_correct	.756
245	deit_small_imagenet_100_seed-0	.744
246	resnet50_imagenet_100_seed-0	.731
247	alexnet_linf_8_robust	.625
248	resnet_50_v1_spiking_l4	.584
249	resnet50_imagenet_10_seed-0	.200
250	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.170
251	convnext_small_imagenet_10_seed-0	.127
252	deit_small_imagenet_10_seed-0	.114
253	deit_small_imagenet_1_seed-0	.089
254	resnet50-barlow	.080
255	resnet50-vicregl0p75	.077
256	alexnet_training_seed_06	.075
257	alexnet_training_seed_09	.067
258	resnet-50_untrained	.062
259	resnet50-meshes-lt-100-original-scratch	.062
260	alexnet_training_seed_05	.062
261	alexnet_training_seed_02	.062
262	alexnet_training_seed_04	.062
263	alexnet_training_seed_08	.062
264	convnext_small_imagenet_1_seed-0	.062
265	alexnet_training_seed_10	.062
266	alexnet_training_seed_01	.062
267	resnet_50_v1_spiking_l3	.062
268	alexnet_training_seed_01_fov4	.062
269	alexnet_training_seed_01_fov12	.062
270	alexnet_training_seed_01_fov16	.062
271	alexnet_training_seed_03	.062
272	resnet50-simclr-vissl	.061
273	resnet50_imagenet_1_seed-0	.061
274	barlow-twins-resnet50	.058
275	resnet50-vicregl0p9	.058
276	resnet50-meshes-lt-100-original-pretrained	.056
277	alexnet_training_seed_07	.056
278	mobilevit_small	.045
279	artResNet18_1	.033
280	ViT-B/32
281	resnet-18-LC_w_sh_100_iter_conv_init_m
282	BiT-M-R50x1
283	resnet-18-LC_w_sh_1_iter_conv_init_m
284	resnet-18-LC_w_sh_1_iter_m
285	BiT-M-R152x2
286	resnet-18-LC_w_sh_10_iter_m
287	BiT-M-R50x3
288	resnet-18-LC_w_sh_100_iter_m
289	resnet-18-LC_w_sh_10_iter_conv_init_m
290	FrankRobWobv0
291	0.5x_resnet-18_LC
292	CORnetZ_CIFAR10
293	resnet18-local_aggregation
294	r3m_resnet18
295	CORnet-Z
296	resnet18-deepcluster
297	vggface
298	my-model
299	resnet18-instance_recognition
300	0.5x_resnet-18_LC_w_sh_10_iter
301	r3m_resnet50
302	r3m_resnet34
303	resnet-18-LC_m
304	r3m_resnet50_nocrop
305	resnet18-supervised
306	resnet-18-LC_1st_conv_m
307	resnet-18-LC_conv_init_m
308	resnet18-contrastive_multiview
309	0.5x_resnet-18
310	RN50
311	0.5x_resnet-18_LC_w_sh_100_iter
312	RN50
313	resnet18-colorization
314	resnet18-relative_position
315	resnet18-autoencoder
316	resnet18-simclr
317	ViT_L_32
318	ViT_B_32
319	resnet18-depth_prediction
320	ViT_B_16
321	r3m_resnet34_nocrop
322	resnet18-contrastive_predictive
323	resnet-18-LC_untrained
324	resnet-18_untrained
325	resnet-50x2_untrained
326	dcgan
327	unet_entire
328	CORnetZ_CIFAR10_bs32_20_04
329	v1-pyr-nodown
330	pixels
331	dorinet_cornet_z
332	omnivore_swinT
333	resnet50
334	openclip
335	vonealexnet_gaussian_noise_std4_fixed
336	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
337	yudixie_resnet50_category_class_0_240908
338	yudixie_resnet50_distance_reg_0_240908
339	ConvLSTM
340	PredRNN
341	SimVP
342	TAU
343	MIM
344	yolos_tiny
345	pnasnet_large_pytorch
346	inception_v3_pytorch
347	yudixie_resnet18_distance_translation_0_240719
348	briaai_rmbg_1_4
349	compact_resnet50_V4
350	ReAlnet01_cornet
351	yudixie_resnet18_imagenet1kpret_0_240719
352	yudixie_resnet50_distance_rotation_0_240908
353	yudixie_resnet50_rotation_reg_0_240908
354	yudixie_resnet50_translation_reg_0_240908
355	vit_base_patch14_reg4_dinov2-lvd142m
356	yudixie_resnet50_distance_translation_0_240908
357	compact_resnet50_IT
358	ReAlnet01_cornet_fov4
359	vit_base_patch14_dinov2-lvd142m
360	ReAlnet02_cornet
361	resnet34_ecoset_full
362	resnet50_ecoset_full
363	ReAlnet01_cornet_fov12
364	resnet101_ecoset_full
365	yudixie_resnet50_translation_rotation_0_240908
366	compact_vgg19_V4
367	ReAlnet04_cornet
368	resnet152_ecoset_full
369	ReAlnet03_cornet
370	resnet-18_test_m
371	aughost_fair_resnet18
372	yudixie_resnet18_category_class_0_240719
373	compact_resnet50_robust_V4
374	ReAlnet05_cornet
375	yudixie_resnet18_random_0_240719
376	yudixie_resnet18_cat_obj_class_all_latents_0_240719
377	compact_vgg19_IT
378	yudixie_resnet18_distance_translation_rotation_0_240719
379	SeLaVi-AVE
380	yudixie_resnet18_object_class_0_240719
381	yudixie_resnet18_rotation_reg_0_240719
382	ReAlnet06_cornet
383	yudixie_resnet18_translation_reg_0_240719
384	yudixie_resnet18_distance_reg_0_240719
385	hmax
386	r3d_18
387	ReAlnet01_ALL
388	ReAlnet07_cornet
389	omnivore_swinB
390	mc3_18
391	ReAlnet02_ALL
392	s3d
393	VOneCORnet-S
394	resnet50-vicreg
395	ReAlnet01_cornet_fov16
396	ReAlnet08_cornet
397	mvit_v1_b
398	compact_cornets_V4
399	ReAlnet03_ALL
400	resnet18_imagenet21kP
401	compact_cornets_IT
402	ReAlnet04_ALL
403	omnivore_swinS
404	SeLaVi-Kinetics400
405	ReAlnet05_ALL
406	vit_base_patch16_dinov3-lvd1689m
407	SeLaVi-VGG-Sound
408	SeLaVi-Kinetics-Sound
409	ReAlnet06_ALL
410	compact_resnet50_robust_IT
411	yudixie_resnet50_random_0_240908
412	ReAlnet10_cornet
413	r2plus1d_18
414	ReAlnet07_ALL
415	yudixie_resnet50_distance_translation_rotation_0_240908
416	mvit_v2_s
417	yudixie_resnet50_imagenet1kpret_0_240908
418	resnet18_ecoset_full
419	ReAlnet08_ALL
420	yudixie_resnet18_distance_rotation_0_240719
421	ReAlnet09_ALL
422	yudixie_resnet18_translation_rotation_0_240719
423	yudixie_resnet50_imagenet1kpret_0_240312
424	yudixie_resnet50_object_class_0_240908
425	yudixie_resnet50_cat_obj_class_all_latents_0_240908
426	resnet50_primary_visual_cortex
427	ReAlnet09_cornet
428	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: Geirhos2021colour

Metric: top1