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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	alexnet	.200
156	antialiased-rnext101_32x8d	.200
157	resnet_152_v2	.200
158	mobilenet_v2_1_0_224	.199
159	resnet_101_v1	.199
160	voneresnet-50-non_stochastic	.198
161	resnet-101_v1-tf	.198
162	resnet34_imagenet_full	.198
163	efficientnet_b0_imagenet_full	.198
164	mobilenet_v2_0.35_224	.197
165	resnet-152_v1-tf	.197
166	BiT-S-R101x3	.196
167	mobilenet_v1_0.25_192	.196
168	pnasnet_large-tf	.195
169	mobilenet_v2_0_75_224	.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-baseline	.189
181	alexnet	.189
182	alexnet	.189
183	alexnet	.189
184	alexnet	.189
185	alexnet	.189
186	mobilenet_v1_1.0_128	.189
187	alexnet	.189
188	alexnet	.189
189	alexnet	.189
190	alexnet_ks_torevert	.189
191	alexnet_fov4	.189
192	alexnet_fov12	.189
193	resnet101_imagenet_full	.189
194	alexnet_fov16	.189
195	grcnn	.186
196	resnet18_imagenet_full	.185
197	CLIP-RN50	.184
198	resnet-50-pytorch	.182
199	resnet50-sup	.182
200	mobilenet_v1_0.75_160	.182
201	shufflenet_v2_x1_0	.182
202	regnet_y_400mf	.182
203	resnet50_imagenet_full	.182
204	resnet50_tutorial	.182
205	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.182
206	resnet_50_v1	.182
207	mobilenet_v1_0.5_192	.181
208	BiT-S-R50x3	.180
209	mobilenet_v2_0.75_224-tf	.180
210	ReAlnet06	.180
211	resnet-50_v1-tf	.178
212	mobilenet_v1_0.25_224	.178
213	antialiased-r50	.178
214	mobilenet_v2_1.0_224-tf	.177
215	resnet_50_v1_spiking	.177
216	nasnet_mobile-tf	.177
217	CORnet-S	.177
218	Res2Net50_26w_4s	.177
219	ReAlnet02	.176
220	cvt_cvt-21-384-in1k_4_LucyV4	.175
221	efficientnet_b1_imagenet_full	.174
222	resnet_SIN_IN_FT_IN	.173
223	resnet50-SIN_IN_IN	.173
224	vgg-19-keras	.172
225	resnet50-moclr8deg	.172
226	ReAlnet04	.172
227	efficientnet_b2_imagenet_full	.171
228	resnet-152_v2_pytorch	.171
229	ReAlnet03	.171
230	ReAlnet05	.170
231	mobilenet_v1_1.0_160	.170
232	mobilenet_v1_1.0_192	.168
233	ReAlnet08	.167
234	ReAlnet10	.167
235	mobilenet_v2_1-4_224_pytorch	.166
236	mobilenet_v2_1_4_224	.166
237	mobilenet_v1_0.75_224	.166
238	ReAlnet07	.166
239	mobilenet_v2_1_3_224	.166
240	ReAlnet01	.166
241	squeezenet1_1	.165
242	ReAlnet09	.165
243	mobilenet_v2_1.4_224-tf	.162
244	mobilenet_v1_0.75_192	.162
245	0.5x_resnet-18_LC_w_sh_1_iter	.159
246	resnet50_eMMCR_Vanilla	.158
247	vgg-16-keras	.157
248	mobilenet_v2_1.3_224-tf	.156
249	deit_small_imagenet_100_seed-0	.156
250	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.155
251	resnet_50_v1_spiking_l4a	.154
252	mobilenet_v1_1.0_224	.148
253	resnet50_imagenet_100_seed-0	.148
254	vgg_19	.144
255	mobilenet_v1_0.5_224	.143
256	resnet_50_v1_spiking_l4	.137
257	squeezenet1_0	.135
258	vgg_16	.116
259	convnext_small_imagenet_10_seed-0	.114
260	deit_small_imagenet_10_seed-0	.088
261	alexnet_training_seed_07	.084
262	deit_small_imagenet_1_seed-0	.077
263	alexnet_training_seed_06	.076
264	convnext_small_imagenet_1_seed-0	.076
265	alexnet_training_seed_09	.074
266	resnet50_imagenet_10_seed-0	.074
267	resnet50_imagenet_1_seed-0	.073
268	resnet50-vicregl0p9	.070
269	resnet50-meshes-lt-100-original-pretrained	.065
270	resnet50-vicregl0p75	.064
271	resnet50-barlow	.063
272	resnet50-meshes-lt-100-original-scratch	.062
273	alexnet_training_seed_05	.062
274	alexnet_training_seed_02	.062
275	alexnet_training_seed_04	.062
276	alexnet_training_seed_08	.062
277	alexnet_training_seed_10	.062
278	alexnet_training_seed_01	.062
279	alexnet_training_seed_01_fov4	.062
280	alexnet_training_seed_01_fov12	.062
281	alexnet_training_seed_01_fov16	.062
282	alexnet_training_seed_03	.062
283	barlow-twins-resnet50	.060
284	resnet-50_untrained	.060
285	resnet50-simclr-vissl	.059
286	resnet_50_v1_spiking_l3	.055
287	artResNet18_1	.053
288	ViT-B/32
289	resnet-18-LC_w_sh_100_iter_conv_init_m
290	BiT-M-R50x1
291	resnet-18-LC_w_sh_1_iter_conv_init_m
292	BiT-M-R152x2
293	resnet-18-LC_w_sh_1_iter_m
294	resnet-18-LC_w_sh_10_iter_m
295	BiT-M-R50x3
296	resnet-18-LC_w_sh_100_iter_m
297	resnet-18-LC_w_sh_10_iter_conv_init_m
298	FrankRobWobv0
299	0.5x_resnet-18_LC
300	CORnetZ_CIFAR10
301	resnet18-local_aggregation
302	r3m_resnet18
303	resnet18-deepcluster
304	CORnet-Z
305	vggface
306	my-model
307	resnet18-instance_recognition
308	0.5x_resnet-18_LC_w_sh_10_iter
309	r3m_resnet50
310	r3m_resnet34
311	resnet-18-LC_m
312	resnet18-supervised
313	r3m_resnet50_nocrop
314	resnet-18-LC_1st_conv_m
315	resnet-18-LC_conv_init_m
316	resnet18-contrastive_multiview
317	0.5x_resnet-18
318	RN50
319	0.5x_resnet-18_LC_w_sh_100_iter
320	RN50
321	resnet18-colorization
322	resnet18-relative_position
323	resnet18-autoencoder
324	resnet18-simclr
325	ViT_L_32
326	ViT_B_32
327	resnet18-depth_prediction
328	ViT_B_16
329	r3m_resnet34_nocrop
330	resnet18-contrastive_predictive
331	resnet-18-LC_untrained
332	resnet-18_untrained
333	resnet-50x2_untrained
334	dcgan
335	unet_entire
336	CORnetZ_CIFAR10_bs32_20_04
337	v1-pyr-nodown
338	pixels
339	dorinet_cornet_z
340	omnivore_swinT
341	resnet50
342	openclip
343	vonealexnet_gaussian_noise_std4_fixed
344	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
345	yudixie_resnet50_imagenet1kpret_0_240312
346	yudixie_resnet50_category_class_0_240908
347	yudixie_resnet50_distance_reg_0_240908
348	resnet18_imagenet21kP
349	ConvLSTM
350	PredRNN
351	SimVP
352	briaai_rmbg_1_4
353	TAU
354	MIM
355	ReAlnet01_cornet
356	yudixie_resnet18_distance_translation_0_240719
357	compact_resnet50_V4
358	mobilevit_small
359	yudixie_resnet18_translation_reg_0_240719
360	yolos_tiny
361	yudixie_resnet18_distance_reg_0_240719
362	yudixie_resnet18_rotation_reg_0_240719
363	ReAlnet03_cornet
364	yudixie_resnet50_distance_translation_0_240908
365	yudixie_resnet50_distance_rotation_0_240908
366	compact_resnet50_IT
367	ReAlnet01_cornet_fov4
368	resnet18_ecoset_full
369	ReAlnet01_cornet_fov12
370	resnet34_ecoset_full
371	ReAlnet04_cornet
372	resnet50_ecoset_full
373	yudixie_resnet50_rotation_reg_0_240908
374	resnet101_ecoset_full
375	resnet152_ecoset_full
376	compact_vgg19_V4
377	yudixie_resnet18_translation_rotation_0_240719
378	yudixie_resnet50_translation_reg_0_240908
379	yudixie_resnet18_distance_rotation_0_240719
380	resnet-18_test_m
381	yudixie_resnet18_category_class_0_240719
382	yudixie_resnet18_random_0_240719
383	yudixie_resnet18_cat_obj_class_all_latents_0_240719
384	compact_vgg19_IT
385	SeLaVi-AVE
386	yudixie_resnet18_object_class_0_240719
387	compact_resnet50_robust_V4
388	yudixie_resnet18_imagenet1kpret_0_240719
389	hmax
390	ReAlnet06_cornet
391	r3d_18
392	yudixie_resnet50_translation_rotation_0_240908
393	ReAlnet07_cornet
394	ReAlnet01_ALL
395	mc3_18
396	ReAlnet02_ALL
397	s3d
398	ReAlnet08_cornet
399	VOneCORnet-S
400	resnet50-vicreg
401	mvit_v1_b
402	omnivore_swinB
403	compact_cornets_V4
404	ReAlnet03_ALL
405	ReAlnet01_cornet_fov16
406	ReAlnet09_cornet
407	compact_cornets_IT
408	ReAlnet04_ALL
409	omnivore_swinS
410	SeLaVi-Kinetics400
411	ReAlnet05_ALL
412	SeLaVi-VGG-Sound
413	ReAlnet06_ALL
414	SeLaVi-Kinetics-Sound
415	ReAlnet10_cornet
416	compact_resnet50_robust_IT
417	mvit_v2_s
418	yudixie_resnet50_distance_translation_rotation_0_240908
419	ReAlnet07_ALL
420	r2plus1d_18
421	yudixie_resnet50_random_0_240908
422	yudixie_resnet50_imagenet1kpret_0_240908
423	ReAlnet08_ALL
424	yudixie_resnet50_cat_obj_class_all_latents_0_240908
425	yudixie_resnet50_object_class_0_240908
426	ReAlnet09_ALL
427	yudixie_resnet18_distance_translation_rotation_0_240719
428	resnet50_primary_visual_cortex
429	ReAlnet02_cornet
430	ReAlnet10_ALL
431	ReAlnet05_cornet

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