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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	.986
48	BiT-S-R152x4	.984
49	AdvProp_efficientnet-b0	.984
50	antialias-resnet152	.984
51	BiT-S-R50x3	.983
52	BiT-S-R101x3	.983
53	cv_18_dagger_408_pretrained	.983
54	cvt_cvt-13-384-in1k_4_LucyV4	.983
55	resnet-152_v2_pytorch	.983
56	cvt_cvt-21-384-in1k_4_LucyV4	.983
57	AdvProp_efficientnet-b4	.981
58	resnet-152_v2-tf	.981
59	AT_efficientnet-b4	.981
60	ViT_B_16_imagenet1k	.981
61	antialiased-r50	.981
62	deit_small_imagenet_full_seed-0	.981
63	inception_v4	.981
64	resnet_101_v2	.981
65	resnet-101_v2-tf	.980
66	resnet_152_v1	.980
67	resnet50_ewconplus_wzc	.980
68	resnet50-vitoimagevidnet8	.980
69	resnet_SIN_IN_FT_IN	.978
70	BiT-S-R101x1	.978
71	resnet50-SIN_IN_IN	.978
72	densenet-201-keras	.978
73	inception_v2-tf	.978
74	Res2Net50_26w_4s	.978
75	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.978
76	densenet-201	.978
77	densenet_201_pytorch	.978
78	resnet152_imagenet_full	.978
79	grcnn	.978
80	efficientnet-b7	.977
81	resnet50-SIN_IN	.977
82	resnet-50_v1-tf	.977
83	effnetb1_272x240	.977
84	resnet50-moclr8deg	.977
85	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.977
86	resnet_101_v1	.977
87	BiT-S-R152x2	.975
88	BiT-S-R50x1	.975
89	efficientnet_b0	.975
90	resnet-101_v1-tf	.975
91	densenet-169-keras	.975
92	xception-keras	.975
93	resnet50_moco_v2	.975
94	resnet101_imagenet_full	.975
95	resnet_152_v2	.975
96	densenet-169	.975
97	ViT_L_32_imagenet1k	.973
98	resnet-152_v1-tf	.973
99	inception_v3	.973
100	voneresnet-50-non_stochastic	.972
101	resnet-50_v2-tf	.972
102	CORnet-S	.972
103	resnet50_imagenet_full	.972
104	tv_efficientnet-b1	.972
105	grcnn_109	.972
106	convnext_femto_ols:d1_in1k	.972
107	vonegrcnn_47e	.970
108	densenet-121-keras	.969
109	vonegrcnn_62e_nobn	.969
110	resnet-50-pytorch	.969
111	mobilenet_v2_1.4_224-tf	.969
112	mobilenet_v2_1.3_224-tf	.969
113	vonegrcnn_47e	.969
114	vgg-16-keras	.969
115	vgg-19-keras	.969
116	resnet50-sup	.969
117	resnet50_tutorial	.969
118	resnet_50_v1	.969
119	densenet-121	.969
120	mobilenet_v2_1.0_192-tf	.967
121	efficientnet_b2_imagenet_full	.967
122	mobilenet_v2_1.0_224-tf	.966
123	voneresnet-50	.966
124	vgg_19	.966
125	resnet50-VITO-8deg-cc	.966
126	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.966
127	resnet_50_v2	.966
128	mobilenet_v2_1.0_160-tf	.964
129	resnet50	.964
130	mobilenet_v2_1_3_224	.964
131	efficientnet_b1_imagenet_full	.963
132	nasnet_mobile	.963
133	vonegrcnn_52e_full	.963
134	resnet-34	.961
135	regnet_y_400mf	.961
136	vgg_16	.961
137	vonegrcnn_62e_nobn	.961
138	vonegrcnn_52e_full	.959
139	ViT_B_32_imagenet1k	.959
140	resnet50_robust_l2_eps1	.959
141	xception	.959
142	mobilenet_v1_1.0_224	.958
143	custom_model_cv_18_dagger_408	.956
144	resnet_50_v1_spiking	.956
145	inception_v1-tf	.955
146	resnet-18	.955
147	resnet34_imagenet_full	.955
148	resnet18_imagenet_full	.955
149	mobilenet_v1_0.75_224	.952
150	mobilenet_v1_1.0_192	.952
151	mobilenet_v1_1.0_160	.952
152	blt_vs	.952
153	ReAlnet06	.948
154	resnet50_eMMCR_Vanilla	.948
155	efficientnet_b0_imagenet_full	.947
156	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.945
157	AdvProp_efficientnet-b8	.944
158	mobilenet_v1_0.75_192	.944
159	ReAlnet09	.944
160	ReAlnet03	.944
161	mobilenet_v2_0.75_192-tf	.941
162	mobilenet_v2_1.0_128-tf	.939
163	mobilenet_v2_0.75_224-tf	.939
164	mobilenet_v1_0.5_224	.939
165	voneresnet-50-robust	.939
166	mobilenet_v2_1_0_192	.939
167	ReAlnet02	.939
168	mobilenet_v1_0.75_160	.938
169	ReAlnet07	.938
170	shufflenet_v2_x1_0	.936
171	mobilenet_v2_1_0_224	.936
172	mobilenet_v2_0.75_160-tf	.934
173	ReAlnet01	.934
174	nasnet_mobile-tf	.931
175	ReAlnet04	.931
176	mobilenet_v2_1_0_160	.931
177	inception_v1	.930
178	ReAlnet05	.930
179	ReAlnet08	.927
180	ReAlnet10	.925
181	mobilenet_v2_0.5_224-tf	.923
182	mobilenet_v1_1.0_128	.923
183	mobilenet_v2_0_75_224	.923
184	mobilenet_v2_0_75_192	.923
185	resnet50-SIN	.922
186	mobilenet_v2_0_75_160	.920
187	mobilenet_v2_0.75_128	.919
188	resnet_50_v1_spiking_l4a	.917
189	mobilenet_v2_0.5_192-tf	.912
190	0.5x_resnet-18_LC_w_sh_1_iter	.912
191	mobilenet_v1_0.75_128	.911
192	mobilenet_v2_1_0_128	.911
193	mobilenet_v1_0.5_192	.909
194	CLIP-RN50	.908
195	mobilenet_v2_1.0_96	.906
196	mobilenet_v2_0.5_160	.905
197	mobilenet_v1_0.5_160	.903
198	mobilenet_v2_0_5_224	.903
199	mobilenet_v2_0.75_96	.902
200	resnet-50-robust	.902
201	imagenet_l2_3_0	.902
202	resnet50_robust_l2_eps3	.902
203	mobilenet_v2_1-4_224_pytorch	.902
204	mobilenet_v2_1_4_224	.902
205	mobilenet_v2_0.35_192	.900
206	mobilenet_v2_0_5_192	.900
207	resnet50_l2_3_robust	.889
208	mobilenet_v2_0.35_224	.884
209	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.884
210	mobilenet_v2_0.5_128	.884
211	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.883
212	mobilenet_v2_0.35_160	.872
213	squeezenet1_1	.870
214	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.866
215	alexnet-baseline	.864
216	alexnet	.864
217	alexnet	.864
218	alexnet	.864
219	alexnet	.864
220	alexnet	.864
221	alexnet	.864
222	alexnet_random_linf8_perturb	.864
223	alexnet	.864
224	alexnet_ks_torevert	.864
225	mobilenet_v1_0.5_128	.863
226	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.859
227	convnext_small_imagenet_100_seed-0	.859
228	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.850
229	alexnet	.847
230	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.844
231	squeezenet1_0	.842
232	mobilenet_v1_0.25_224	.833
233	alexnet_early_checkpoint	.830
234	mobilenet_v2_0.5_96	.823
235	mobilenet_v1_0.25_192	.819
236	mobilenet_v2_0.35_128	.819
237	resnet50_linf_8_robust	.812
238	mobilenet_v1_0.25_160	.808
239	mobilenet_v2_0.35_96	.797
240	AlexNet_SIN	.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_03	.062
269	resnet50-simclr-vissl	.061
270	resnet50_imagenet_1_seed-0	.061
271	barlow-twins-resnet50	.058
272	resnet50-vicregl0p9	.058
273	resnet50-meshes-lt-100-original-pretrained	.056
274	alexnet_training_seed_07	.056
275	mobilevit_small	.045
276	artResNet18_1	.033
277	ViT-B/32
278	resnet-18-LC_w_sh_100_iter_conv_init_m
279	BiT-M-R50x1
280	resnet-18-LC_w_sh_1_iter_conv_init_m
281	resnet-18-LC_w_sh_1_iter_m
282	BiT-M-R152x2
283	resnet-18-LC_w_sh_10_iter_m
284	BiT-M-R50x3
285	resnet-18-LC_w_sh_100_iter_m
286	resnet-18-LC_w_sh_10_iter_conv_init_m
287	FrankRobWobv0
288	0.5x_resnet-18_LC
289	CORnetZ_CIFAR10
290	resnet18-local_aggregation
291	r3m_resnet18
292	CORnet-Z
293	resnet18-deepcluster
294	vggface
295	my-model
296	resnet18-instance_recognition
297	0.5x_resnet-18_LC_w_sh_10_iter
298	r3m_resnet50
299	r3m_resnet34
300	resnet-18-LC_m
301	r3m_resnet50_nocrop
302	resnet18-supervised
303	resnet-18-LC_1st_conv_m
304	resnet-18-LC_conv_init_m
305	resnet18-contrastive_multiview
306	0.5x_resnet-18
307	RN50
308	0.5x_resnet-18_LC_w_sh_100_iter
309	RN50
310	resnet18-colorization
311	resnet18-relative_position
312	resnet18-autoencoder
313	resnet18-simclr
314	ViT_L_32
315	ViT_B_32
316	resnet18-depth_prediction
317	ViT_B_16
318	r3m_resnet34_nocrop
319	resnet18-contrastive_predictive
320	resnet-18-LC_untrained
321	resnet-18_untrained
322	resnet-50x2_untrained
323	dcgan
324	unet_entire
325	CORnetZ_CIFAR10_bs32_20_04
326	v1-pyr-nodown
327	pixels
328	dorinet_cornet_z
329	omnivore_swinT
330	resnet50
331	openclip
332	vonealexnet_gaussian_noise_std4_fixed
333	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
334	yudixie_resnet50_category_class_0_240908
335	yudixie_resnet50_distance_reg_0_240908
336	ConvLSTM
337	PredRNN
338	SimVP
339	TAU
340	MIM
341	yolos_tiny
342	pnasnet_large_pytorch
343	inception_v3_pytorch
344	yudixie_resnet18_distance_translation_0_240719
345	briaai_rmbg_1_4
346	compact_resnet50_V4
347	ReAlnet01_cornet
348	yudixie_resnet18_imagenet1kpret_0_240719
349	yudixie_resnet50_distance_rotation_0_240908
350	yudixie_resnet50_rotation_reg_0_240908
351	yudixie_resnet50_translation_reg_0_240908
352	yudixie_resnet50_distance_translation_0_240908
353	compact_resnet50_IT
354	ReAlnet02_cornet
355	resnet34_ecoset_full
356	resnet50_ecoset_full
357	resnet101_ecoset_full
358	yudixie_resnet50_translation_rotation_0_240908
359	compact_vgg19_V4
360	ReAlnet04_cornet
361	resnet152_ecoset_full
362	ReAlnet03_cornet
363	resnet-18_test_m
364	yudixie_resnet18_category_class_0_240719
365	compact_resnet50_robust_V4
366	ReAlnet05_cornet
367	yudixie_resnet18_random_0_240719
368	yudixie_resnet18_cat_obj_class_all_latents_0_240719
369	compact_vgg19_IT
370	yudixie_resnet18_distance_translation_rotation_0_240719
371	SeLaVi-AVE
372	yudixie_resnet18_object_class_0_240719
373	yudixie_resnet18_rotation_reg_0_240719
374	ReAlnet06_cornet
375	yudixie_resnet18_translation_reg_0_240719
376	yudixie_resnet18_distance_reg_0_240719
377	hmax
378	r3d_18
379	ReAlnet01_ALL
380	ReAlnet07_cornet
381	omnivore_swinB
382	mc3_18
383	ReAlnet02_ALL
384	s3d
385	VOneCORnet-S
386	resnet50-vicreg
387	ReAlnet08_cornet
388	mvit_v1_b
389	compact_cornets_V4
390	ReAlnet03_ALL
391	resnet18_imagenet21kP
392	compact_cornets_IT
393	ReAlnet04_ALL
394	omnivore_swinS
395	SeLaVi-Kinetics400
396	ReAlnet05_ALL
397	SeLaVi-VGG-Sound
398	SeLaVi-Kinetics-Sound
399	ReAlnet06_ALL
400	compact_resnet50_robust_IT
401	yudixie_resnet50_random_0_240908
402	ReAlnet10_cornet
403	r2plus1d_18
404	ReAlnet07_ALL
405	yudixie_resnet50_distance_translation_rotation_0_240908
406	mvit_v2_s
407	yudixie_resnet50_imagenet1kpret_0_240908
408	resnet18_ecoset_full
409	ReAlnet08_ALL
410	yudixie_resnet18_distance_rotation_0_240719
411	ReAlnet09_ALL
412	yudixie_resnet18_translation_rotation_0_240719
413	yudixie_resnet50_imagenet1kpret_0_240312
414	yudixie_resnet50_object_class_0_240908
415	yudixie_resnet50_cat_obj_class_all_latents_0_240908
416	resnet50_primary_visual_cortex
417	ReAlnet09_cornet
418	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