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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Geirhos2021cueconflict-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	.577
2	AdvProp_efficientnet-b8	.544
3	imagenet_l2_10_0	.532
4	AdvProp_efficientnet-b6	.525
5	resnet50-SIN	.524
6	AdvProp_efficientnet-b7	.522
7	resnet50_linf_8_robust	.505
8	AlexNet_SIN	.484
9	AlexNet_SIN_fov4	.484
10	AlexNet_SIN_fov12	.484
11	AlexNet_SIN_fov16	.484
12	resnet50_l2_3_robust	.467
13	resnet-50-robust	.459
14	imagenet_l2_3_0	.459
15	resnet50_robust_l2_eps3	.459
16	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.448
17	AdvProp_efficientnet-b4	.444
18	AT_efficientnet-b4	.444
19	alexnet_linf_8_robust	.425
20	voneresnet-50-robust	.418
21	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.415
22	alexnet_l2_3_robust	.399
23	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.395
24	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.386
25	efficientnet-b7	.383
26	alexnet_robust_correct	.377
27	vit_large_patch14_clip_224:openai_ft_in1k	.377
28	convnext_xlarge:fb_in22k_ft_in1k	.371
29	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.362
30	vit_large_patch14_clip_224:laion2b_ft_in1k	.355
31	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.355
32	convnext_large:fb_in22k_ft_in1k	.348
33	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.339
34	vit_large_patch14_clip_336:laion2b_ft_in1k	.339
35	resnext101_32x48d_wsl	.334
36	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.334
37	resnet50_robust_l2_eps1	.332
38	custom_model_cv_18_dagger_408	.323
39	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.313
40	resnext101_32x16d_wsl	.308
41	vit_relpos_base_patch16_clsgap_224:sw_in1k	.307
42	ViT_L_32_imagenet1k	.303
43	convnext_large_imagenet_full_seed-0	.303
44	mobilenet_v2_0.5_96	.302
45	deit_large_imagenet_full_seed-0	.302
46	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.300
47	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.300
48	resnext101_32x32d_wsl	.299
49	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.296
50	ViT_B_16_imagenet1k	.295
51	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.295
52	effnetv2m_custom384	.295
53	mobilenet_v2_0.35_96	.294
54	convnext_small_imagenet_full_seed-0	.294
55	convnext_base_imagenet_full_seed-0	.291
56	vit_base_patch16_clip_224:openai_ft_in1k	.289
57	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.282
58	convnext_base:clip_laiona_augreg_ft_in1k_384	.281
59	deit_base_imagenet_full_seed-0	.280
60	convnext_tiny_imagenet_full_seed-0	.280
61	AT_efficientnet-b2	.277
62	AdvProp_efficientnet-b2	.277
63	ViT_B_32_imagenet1k	.276
64	resnext101_32x8d_wsl	.275
65	fixres_resnext101_32x48d_wsl	.272
66	convnext_small_imagenet_100_seed-0	.267
67	mobilenet_v2_0.5_128	.265
68	blt_vs	.265
69	mobilenet_v2_0.35_128	.261
70	convnext_tiny:in12k_ft_in1k	.259
71	resnet50-SIN_IN	.258
72	resnet-152_v2-tf	.255
73	mobilenet_v2_0.75_96	.254
74	focalnet_tiny_lrf_in1k	.252
75	mobilenet_v2_1_0_128	.251
76	deit_small_imagenet_full_seed-0	.251
77	inception_v1	.249
78	resnet_101_v2	.246
79	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.245
80	AdvProp_efficientnet-b0	.243
81	resnet-101_v2-tf	.242
82	cv_18_dagger_408_pretrained	.242
83	voneresnet-50	.242
84	mobilenet_v2_0.5_160	.241
85	mobilenet_v2_0.75_128	.241
86	inception_v3	.241
87	mobilenet_v2_0.35_192	.240
88	mobilenet_v2_0.35_160	.237
89	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.235
90	resnet-50_v2-tf	.235
91	mobilenet_v1_0.25_128	.235
92	mobilenet_v2_1.0_96	.230
93	mobilenet_v2_0_5_192	.230
94	mobilenet_v2_0_75_160	.230
95	nasnet_large-tf	.228
96	vonegrcnn_47e	.228
97	inception_v3_pytorch	.228
98	inception_v4-tf	.227
99	effnetb1_272x240	.227
100	vonegrcnn_52e_full	.226
101	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.226
102	vonegrcnn_52e_full	.226
103	nasnet_large	.225
104	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.224
105	vonegrcnn_62e_nobn	.223
106	vonegrcnn_47e	.222
107	mobilenet_v2_1.0_192-tf	.221
108	nasnet_mobile	.221
109	mobilenet_v2_0.5_192-tf	.220
110	inception_v2-tf	.220
111	mobilenet_v2_0.75_160-tf	.220
112	inception_v3-tf	.220
113	mobilenet_v2_0_5_224	.220
114	mobilenet_v2_1.0_128-tf	.219
115	pnasnet_large_pytorch	.219
116	mobilenet_v2_1_0_192	.218
117	vonegrcnn_62e_nobn	.217
118	mobilenet_v2_1_0_160	.216
119	cvt_cvt-13-224-in1k_4_LucyV4	.216
120	inception_v1-tf	.215
121	mobilenet_v1_0.5_128	.215
122	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.214
123	mobilenet_v1_0.25_160	.214
124	mobilenet_v1_0.75_128	.214
125	alexnet_early_checkpoint	.214
126	resnet50_ewconplus_wzc	.214
127	mobilenet_v2_0.75_192-tf	.212
128	densenet-201-keras	.212
129	inception_v4	.212
130	densenet-201	.212
131	densenet_201_pytorch	.212
132	resnet50-VITO-8deg-cc	.211
133	efficientnet_b0	.209
134	convnext_tiny_sup	.209
135	BiT-S-R152x2	.209
136	BiT-S-R50x1	.209
137	cvt_cvt-13-384-in1k_4_LucyV4	.209
138	resnet_152_v1	.207
139	densenet-169	.207
140	BiT-S-R101x1	.206
141	mobilenet_v2_0.5_224-tf	.205
142	densenet-169-keras	.205
143	mobilenet_v2_0_75_192	.205
144	resnet50-vitoimagevidnet8	.205
145	resnet_50_v2	.205
146	antialias-resnet152	.204
147	grcnn_109	.204
148	resnet50_moco_v2	.203
149	cvt_cvt-21-224-in1k_4_LucyV4	.202
150	resnet152_imagenet_full	.202
151	mobilenet_v2_1.0_160-tf	.202
152	alexnet_random_linf8_perturb	.202
153	mobilenet_v1_0.5_160	.201
154	convnext_femto_ols:d1_in1k	.201
155	antialiased-rnext101_32x8d	.200
156	resnet_152_v2	.200
157	mobilenet_v2_1_0_224	.199
158	resnet_101_v1	.199
159	voneresnet-50-non_stochastic	.198
160	resnet-101_v1-tf	.198
161	resnet34_imagenet_full	.198
162	efficientnet_b0_imagenet_full	.198
163	mobilenet_v2_0.35_224	.197
164	resnet-152_v1-tf	.197
165	BiT-S-R101x3	.196
166	mobilenet_v1_0.25_192	.196
167	pnasnet_large-tf	.195
168	mobilenet_v2_0_75_224	.195
169	pnasnet_large_halfPrecision	.195
170	xception	.195
171	pnasnet_large	.195
172	resnet-34	.193
173	BiT-S-R152x4	.192
174	resnet50	.192
175	tv_efficientnet-b1	.192
176	xception-keras	.191
177	resnet-18	.191
178	densenet-121-keras	.190
179	densenet-121	.190
180	alexnet	.189
181	mobilenet_v1_1.0_128	.189
182	alexnet_ks_torevert	.189
183	alexnet_fov4	.189
184	alexnet_fov12	.189
185	resnet101_imagenet_full	.189
186	alexnet_fov16	.189
187	grcnn	.186
188	resnet18_imagenet_full	.185
189	CLIP-RN50	.184
190	resnet-50-pytorch	.182
191	resnet50-sup	.182
192	mobilenet_v1_0.75_160	.182
193	shufflenet_v2_x1_0	.182
194	regnet_y_400mf	.182
195	resnet50_imagenet_full	.182
196	resnet50_tutorial	.182
197	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.182
198	resnet_50_v1	.182
199	mobilenet_v1_0.5_192	.181
200	BiT-S-R50x3	.180
201	mobilenet_v2_0.75_224-tf	.180
202	ReAlnet06	.180
203	resnet-50_v1-tf	.178
204	mobilenet_v1_0.25_224	.178
205	antialiased-r50	.178
206	mobilenet_v2_1.0_224-tf	.177
207	resnet_50_v1_spiking	.177
208	nasnet_mobile-tf	.177
209	CORnet-S	.177
210	Res2Net50_26w_4s	.177
211	ReAlnet02	.176
212	cvt_cvt-21-384-in1k_4_LucyV4	.175
213	efficientnet_b1_imagenet_full	.174
214	resnet_SIN_IN_FT_IN	.173
215	resnet50-SIN_IN_IN	.173
216	vgg-19-keras	.172
217	resnet50-moclr8deg	.172
218	ReAlnet04	.172
219	efficientnet_b2_imagenet_full	.171
220	resnet-152_v2_pytorch	.171
221	ReAlnet03	.171
222	ReAlnet05	.170
223	mobilenet_v1_1.0_160	.170
224	mobilenet_v1_1.0_192	.168
225	ReAlnet08	.167
226	ReAlnet10	.167
227	mobilenet_v2_1-4_224_pytorch	.166
228	mobilenet_v2_1_4_224	.166
229	mobilenet_v1_0.75_224	.166
230	ReAlnet07	.166
231	mobilenet_v2_1_3_224	.166
232	ReAlnet01	.166
233	squeezenet1_1	.165
234	ReAlnet09	.165
235	mobilenet_v2_1.4_224-tf	.162
236	mobilenet_v1_0.75_192	.162
237	0.5x_resnet-18_LC_w_sh_1_iter	.159
238	resnet50_eMMCR_Vanilla	.158
239	vgg-16-keras	.157
240	mobilenet_v2_1.3_224-tf	.156
241	deit_small_imagenet_100_seed-0	.156
242	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.155
243	resnet_50_v1_spiking_l4a	.154
244	mobilenet_v1_1.0_224	.148
245	resnet50_imagenet_100_seed-0	.148
246	vgg_19	.144
247	mobilenet_v1_0.5_224	.143
248	resnet_50_v1_spiking_l4	.137
249	squeezenet1_0	.135
250	vgg_16	.116
251	convnext_small_imagenet_10_seed-0	.114
252	deit_small_imagenet_10_seed-0	.088
253	alexnet_training_seed_07	.084
254	deit_small_imagenet_1_seed-0	.077
255	alexnet_training_seed_06	.076
256	convnext_small_imagenet_1_seed-0	.076
257	alexnet_training_seed_09	.074
258	resnet50_imagenet_10_seed-0	.074
259	resnet50_imagenet_1_seed-0	.073
260	resnet50-vicregl0p9	.070
261	resnet50-meshes-lt-100-original-pretrained	.065
262	resnet50-vicregl0p75	.064
263	resnet50-barlow	.063
264	resnet50-meshes-lt-100-original-scratch	.062
265	alexnet_training_seed_05	.062
266	alexnet_training_seed_02	.062
267	alexnet_training_seed_04	.062
268	alexnet_training_seed_08	.062
269	alexnet_training_seed_10	.062
270	alexnet_training_seed_01	.062
271	alexnet_training_seed_01_fov4	.062
272	alexnet_training_seed_01_fov12	.062
273	alexnet_training_seed_01_fov16	.062
274	alexnet_training_seed_03	.062
275	barlow-twins-resnet50	.060
276	resnet-50_untrained	.060
277	resnet50-simclr-vissl	.059
278	resnet_50_v1_spiking_l3	.055
279	artResNet18_1	.053
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	BiT-M-R152x2
285	resnet-18-LC_w_sh_1_iter_m
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	resnet18-deepcluster
296	CORnet-Z
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	resnet18-supervised
305	r3m_resnet50_nocrop
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_imagenet1kpret_0_240312
338	yudixie_resnet50_category_class_0_240908
339	yudixie_resnet50_distance_reg_0_240908
340	resnet18_imagenet21kP
341	ConvLSTM
342	PredRNN
343	SimVP
344	briaai_rmbg_1_4
345	TAU
346	MIM
347	ReAlnet01_cornet
348	yudixie_resnet18_distance_translation_0_240719
349	compact_resnet50_V4
350	mobilevit_small
351	yudixie_resnet18_translation_reg_0_240719
352	yolos_tiny
353	yudixie_resnet18_distance_reg_0_240719
354	yudixie_resnet18_rotation_reg_0_240719
355	ReAlnet03_cornet
356	eBarlow_lmda_02_1000ep
357	vit_base_patch16_dinov3-lvd1689m
358	yudixie_resnet50_distance_translation_0_240908
359	yudixie_resnet50_distance_rotation_0_240908
360	compact_resnet50_IT
361	ReAlnet01_cornet_fov4
362	resnet18_ecoset_full
363	ReAlnet01_cornet_fov12
364	resnet34_ecoset_full
365	ReAlnet04_cornet
366	resnet50_ecoset_full
367	vit_base_patch14_reg4_dinov2-lvd142m
368	yudixie_resnet50_rotation_reg_0_240908
369	resnet101_ecoset_full
370	resnet152_ecoset_full
371	compact_vgg19_V4
372	yudixie_resnet18_translation_rotation_0_240719
373	yudixie_resnet50_translation_reg_0_240908
374	yudixie_resnet18_distance_rotation_0_240719
375	aughost_fair_resnet18
376	resnet-18_test_m
377	yudixie_resnet18_category_class_0_240719
378	yudixie_resnet18_random_0_240719
379	yudixie_resnet18_cat_obj_class_all_latents_0_240719
380	compact_vgg19_IT
381	SeLaVi-AVE
382	yudixie_resnet18_object_class_0_240719
383	compact_resnet50_robust_V4
384	yudixie_resnet18_imagenet1kpret_0_240719
385	hmax
386	ReAlnet06_cornet
387	r3d_18
388	yudixie_resnet50_translation_rotation_0_240908
389	ReAlnet07_cornet
390	ReAlnet01_ALL
391	mc3_18
392	ReAlnet02_ALL
393	s3d
394	ReAlnet08_cornet
395	VOneCORnet-S
396	resnet50-vicreg
397	mvit_v1_b
398	omnivore_swinB
399	compact_cornets_V4
400	ReAlnet03_ALL
401	ReAlnet01_cornet_fov16
402	ReAlnet09_cornet
403	compact_cornets_IT
404	ReAlnet04_ALL
405	omnivore_swinS
406	SeLaVi-Kinetics400
407	ReAlnet05_ALL
408	SeLaVi-VGG-Sound
409	ReAlnet06_ALL
410	SeLaVi-Kinetics-Sound
411	ReAlnet10_cornet
412	compact_resnet50_robust_IT
413	mvit_v2_s
414	yudixie_resnet50_distance_translation_rotation_0_240908
415	ReAlnet07_ALL
416	r2plus1d_18
417	yudixie_resnet50_random_0_240908
418	yudixie_resnet50_imagenet1kpret_0_240908
419	ReAlnet08_ALL
420	yudixie_resnet50_cat_obj_class_all_latents_0_240908
421	yudixie_resnet50_object_class_0_240908
422	ReAlnet09_ALL
423	yudixie_resnet18_distance_translation_rotation_0_240719
424	resnet50_primary_visual_cortex
425	ReAlnet02_cornet
426	ReAlnet10_ALL
427	ReAlnet05_cornet
428	vit_base_patch14_dinov2-lvd142m

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: Geirhos2021cueconflict

Metric: top1