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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	resnet50_l2_3_robust	.467
10	resnet-50-robust	.459
11	imagenet_l2_3_0	.459
12	resnet50_robust_l2_eps3	.459
13	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.448
14	AdvProp_efficientnet-b4	.444
15	AT_efficientnet-b4	.444
16	alexnet_linf_8_robust	.425
17	voneresnet-50-robust	.418
18	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.415
19	alexnet_l2_3_robust	.399
20	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.395
21	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.386
22	efficientnet-b7	.383
23	alexnet_robust_correct	.377
24	vit_large_patch14_clip_224:openai_ft_in1k	.377
25	convnext_xlarge:fb_in22k_ft_in1k	.371
26	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.362
27	vit_large_patch14_clip_224:laion2b_ft_in1k	.355
28	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.355
29	convnext_large:fb_in22k_ft_in1k	.348
30	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.339
31	vit_large_patch14_clip_336:laion2b_ft_in1k	.339
32	resnext101_32x48d_wsl	.334
33	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.334
34	resnet50_robust_l2_eps1	.332
35	custom_model_cv_18_dagger_408	.323
36	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.313
37	resnext101_32x16d_wsl	.308
38	vit_relpos_base_patch16_clsgap_224:sw_in1k	.307
39	ViT_L_32_imagenet1k	.303
40	convnext_large_imagenet_full_seed-0	.303
41	mobilenet_v2_0.5_96	.302
42	deit_large_imagenet_full_seed-0	.302
43	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.300
44	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.300
45	resnext101_32x32d_wsl	.299
46	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.296
47	ViT_B_16_imagenet1k	.295
48	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.295
49	effnetv2m_custom384	.295
50	mobilenet_v2_0.35_96	.294
51	convnext_small_imagenet_full_seed-0	.294
52	convnext_base_imagenet_full_seed-0	.291
53	vit_base_patch16_clip_224:openai_ft_in1k	.289
54	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.282
55	convnext_base:clip_laiona_augreg_ft_in1k_384	.281
56	deit_base_imagenet_full_seed-0	.280
57	convnext_tiny_imagenet_full_seed-0	.280
58	AT_efficientnet-b2	.277
59	AdvProp_efficientnet-b2	.277
60	ViT_B_32_imagenet1k	.276
61	resnext101_32x8d_wsl	.275
62	fixres_resnext101_32x48d_wsl	.272
63	convnext_small_imagenet_100_seed-0	.267
64	mobilenet_v2_0.5_128	.265
65	blt_vs	.265
66	mobilenet_v2_0.35_128	.261
67	convnext_tiny:in12k_ft_in1k	.259
68	resnet50-SIN_IN	.258
69	resnet-152_v2-tf	.255
70	mobilenet_v2_0.75_96	.254
71	focalnet_tiny_lrf_in1k	.252
72	mobilenet_v2_1_0_128	.251
73	deit_small_imagenet_full_seed-0	.251
74	inception_v1	.249
75	resnet_101_v2	.246
76	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.245
77	AdvProp_efficientnet-b0	.243
78	resnet-101_v2-tf	.242
79	cv_18_dagger_408_pretrained	.242
80	voneresnet-50	.242
81	mobilenet_v2_0.5_160	.241
82	mobilenet_v2_0.75_128	.241
83	inception_v3	.241
84	mobilenet_v2_0.35_192	.240
85	mobilenet_v2_0.35_160	.237
86	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.235
87	resnet-50_v2-tf	.235
88	mobilenet_v1_0.25_128	.235
89	mobilenet_v2_1.0_96	.230
90	mobilenet_v2_0_5_192	.230
91	mobilenet_v2_0_75_160	.230
92	nasnet_large-tf	.228
93	vonegrcnn_47e	.228
94	inception_v3_pytorch	.228
95	inception_v4-tf	.227
96	effnetb1_272x240	.227
97	vonegrcnn_52e_full	.226
98	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.226
99	vonegrcnn_52e_full	.226
100	nasnet_large	.225
101	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.224
102	vonegrcnn_62e_nobn	.223
103	vonegrcnn_47e	.222
104	mobilenet_v2_1.0_192-tf	.221
105	nasnet_mobile	.221
106	mobilenet_v2_0.5_192-tf	.220
107	inception_v2-tf	.220
108	mobilenet_v2_0.75_160-tf	.220
109	inception_v3-tf	.220
110	mobilenet_v2_0_5_224	.220
111	mobilenet_v2_1.0_128-tf	.219
112	pnasnet_large_pytorch	.219
113	mobilenet_v2_1_0_192	.218
114	vonegrcnn_62e_nobn	.217
115	mobilenet_v2_1_0_160	.216
116	cvt_cvt-13-224-in1k_4_LucyV4	.216
117	inception_v1-tf	.215
118	mobilenet_v1_0.5_128	.215
119	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.214
120	mobilenet_v1_0.25_160	.214
121	mobilenet_v1_0.75_128	.214
122	alexnet_early_checkpoint	.214
123	resnet50_ewconplus_wzc	.214
124	mobilenet_v2_0.75_192-tf	.212
125	densenet-201-keras	.212
126	inception_v4	.212
127	densenet-201	.212
128	densenet_201_pytorch	.212
129	resnet50-VITO-8deg-cc	.211
130	efficientnet_b0	.209
131	convnext_tiny_sup	.209
132	BiT-S-R152x2	.209
133	BiT-S-R50x1	.209
134	cvt_cvt-13-384-in1k_4_LucyV4	.209
135	resnet_152_v1	.207
136	densenet-169	.207
137	BiT-S-R101x1	.206
138	mobilenet_v2_0.5_224-tf	.205
139	densenet-169-keras	.205
140	mobilenet_v2_0_75_192	.205
141	resnet50-vitoimagevidnet8	.205
142	resnet_50_v2	.205
143	antialias-resnet152	.204
144	grcnn_109	.204
145	resnet50_moco_v2	.203
146	cvt_cvt-21-224-in1k_4_LucyV4	.202
147	resnet152_imagenet_full	.202
148	mobilenet_v2_1.0_160-tf	.202
149	alexnet_random_linf8_perturb	.202
150	mobilenet_v1_0.5_160	.201
151	convnext_femto_ols:d1_in1k	.201
152	alexnet	.200
153	antialiased-rnext101_32x8d	.200
154	resnet_152_v2	.200
155	mobilenet_v2_1_0_224	.199
156	resnet_101_v1	.199
157	voneresnet-50-non_stochastic	.198
158	resnet-101_v1-tf	.198
159	resnet34_imagenet_full	.198
160	efficientnet_b0_imagenet_full	.198
161	mobilenet_v2_0.35_224	.197
162	resnet-152_v1-tf	.197
163	BiT-S-R101x3	.196
164	mobilenet_v1_0.25_192	.196
165	pnasnet_large-tf	.195
166	mobilenet_v2_0_75_224	.195
167	xception	.195
168	pnasnet_large	.195
169	resnet-34	.193
170	BiT-S-R152x4	.192
171	resnet50	.192
172	tv_efficientnet-b1	.192
173	xception-keras	.191
174	resnet-18	.191
175	densenet-121-keras	.190
176	densenet-121	.190
177	alexnet-baseline	.189
178	alexnet	.189
179	alexnet	.189
180	alexnet	.189
181	alexnet	.189
182	mobilenet_v1_1.0_128	.189
183	alexnet	.189
184	alexnet	.189
185	alexnet	.189
186	alexnet_ks_torevert	.189
187	resnet101_imagenet_full	.189
188	grcnn	.186
189	resnet18_imagenet_full	.185
190	CLIP-RN50	.184
191	resnet-50-pytorch	.182
192	resnet50-sup	.182
193	mobilenet_v1_0.75_160	.182
194	shufflenet_v2_x1_0	.182
195	regnet_y_400mf	.182
196	resnet50_imagenet_full	.182
197	resnet50_tutorial	.182
198	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.182
199	resnet_50_v1	.182
200	mobilenet_v1_0.5_192	.181
201	BiT-S-R50x3	.180
202	mobilenet_v2_0.75_224-tf	.180
203	ReAlnet06	.180
204	resnet-50_v1-tf	.178
205	mobilenet_v1_0.25_224	.178
206	antialiased-r50	.178
207	mobilenet_v2_1.0_224-tf	.177
208	resnet_50_v1_spiking	.177
209	nasnet_mobile-tf	.177
210	CORnet-S	.177
211	Res2Net50_26w_4s	.177
212	ReAlnet02	.176
213	cvt_cvt-21-384-in1k_4_LucyV4	.175
214	efficientnet_b1_imagenet_full	.174
215	resnet_SIN_IN_FT_IN	.173
216	resnet50-SIN_IN_IN	.173
217	vgg-19-keras	.172
218	resnet50-moclr8deg	.172
219	ReAlnet04	.172
220	efficientnet_b2_imagenet_full	.171
221	resnet-152_v2_pytorch	.171
222	ReAlnet03	.171
223	ReAlnet05	.170
224	mobilenet_v1_1.0_160	.170
225	mobilenet_v1_1.0_192	.168
226	ReAlnet08	.167
227	ReAlnet10	.167
228	mobilenet_v2_1-4_224_pytorch	.166
229	mobilenet_v2_1_4_224	.166
230	mobilenet_v1_0.75_224	.166
231	ReAlnet07	.166
232	mobilenet_v2_1_3_224	.166
233	ReAlnet01	.166
234	squeezenet1_1	.165
235	ReAlnet09	.165
236	mobilenet_v2_1.4_224-tf	.162
237	mobilenet_v1_0.75_192	.162
238	0.5x_resnet-18_LC_w_sh_1_iter	.159
239	resnet50_eMMCR_Vanilla	.158
240	vgg-16-keras	.157
241	mobilenet_v2_1.3_224-tf	.156
242	deit_small_imagenet_100_seed-0	.156
243	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.155
244	resnet_50_v1_spiking_l4a	.154
245	mobilenet_v1_1.0_224	.148
246	resnet50_imagenet_100_seed-0	.148
247	vgg_19	.144
248	mobilenet_v1_0.5_224	.143
249	resnet_50_v1_spiking_l4	.137
250	squeezenet1_0	.135
251	vgg_16	.116
252	convnext_small_imagenet_10_seed-0	.114
253	deit_small_imagenet_10_seed-0	.088
254	alexnet_training_seed_07	.084
255	deit_small_imagenet_1_seed-0	.077
256	alexnet_training_seed_06	.076
257	convnext_small_imagenet_1_seed-0	.076
258	alexnet_training_seed_09	.074
259	resnet50_imagenet_10_seed-0	.074
260	resnet50_imagenet_1_seed-0	.073
261	resnet50-vicregl0p9	.070
262	resnet50-meshes-lt-100-original-pretrained	.065
263	resnet50-vicregl0p75	.064
264	resnet50-barlow	.063
265	resnet50-meshes-lt-100-original-scratch	.062
266	alexnet_training_seed_05	.062
267	alexnet_training_seed_02	.062
268	alexnet_training_seed_04	.062
269	alexnet_training_seed_08	.062
270	alexnet_training_seed_10	.062
271	alexnet_training_seed_01	.062
272	alexnet_training_seed_03	.062
273	barlow-twins-resnet50	.060
274	resnet-50_untrained	.060
275	resnet50-simclr-vissl	.059
276	resnet_50_v1_spiking_l3	.055
277	artResNet18_1	.053
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	my-model
297	resnet18-instance_recognition
298	0.5x_resnet-18_LC_w_sh_10_iter
299	r3m_resnet50
300	r3m_resnet34
301	resnet-18-LC_m
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	RN50
309	0.5x_resnet-18_LC_w_sh_100_iter
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_imagenet1kpret_0_240312
336	yudixie_resnet50_category_class_0_240908
337	yudixie_resnet50_distance_reg_0_240908
338	resnet18_imagenet21kP
339	ConvLSTM
340	PredRNN
341	SimVP
342	briaai_rmbg_1_4
343	TAU
344	MIM
345	ReAlnet01_cornet
346	yudixie_resnet18_distance_translation_0_240719
347	compact_resnet50_V4
348	mobilevit_small
349	yudixie_resnet18_translation_reg_0_240719
350	yolos_tiny
351	yudixie_resnet18_distance_reg_0_240719
352	yudixie_resnet18_rotation_reg_0_240719
353	ReAlnet03_cornet
354	yudixie_resnet50_distance_translation_0_240908
355	yudixie_resnet50_distance_rotation_0_240908
356	compact_resnet50_IT
357	resnet18_ecoset_full
358	resnet34_ecoset_full
359	ReAlnet04_cornet
360	resnet50_ecoset_full
361	yudixie_resnet50_rotation_reg_0_240908
362	resnet101_ecoset_full
363	resnet152_ecoset_full
364	compact_vgg19_V4
365	yudixie_resnet18_translation_rotation_0_240719
366	yudixie_resnet50_translation_reg_0_240908
367	yudixie_resnet18_distance_rotation_0_240719
368	resnet-18_test_m
369	yudixie_resnet18_category_class_0_240719
370	yudixie_resnet18_random_0_240719
371	yudixie_resnet18_cat_obj_class_all_latents_0_240719
372	compact_vgg19_IT
373	SeLaVi-AVE
374	yudixie_resnet18_object_class_0_240719
375	compact_resnet50_robust_V4
376	yudixie_resnet18_imagenet1kpret_0_240719
377	hmax
378	ReAlnet06_cornet
379	r3d_18
380	yudixie_resnet50_translation_rotation_0_240908
381	ReAlnet07_cornet
382	ReAlnet01_ALL
383	mc3_18
384	ReAlnet02_ALL
385	s3d
386	ReAlnet08_cornet
387	VOneCORnet-S
388	resnet50-vicreg
389	mvit_v1_b
390	omnivore_swinB
391	compact_cornets_V4
392	ReAlnet03_ALL
393	ReAlnet09_cornet
394	compact_cornets_IT
395	ReAlnet04_ALL
396	omnivore_swinS
397	SeLaVi-Kinetics400
398	ReAlnet05_ALL
399	SeLaVi-VGG-Sound
400	ReAlnet06_ALL
401	SeLaVi-Kinetics-Sound
402	ReAlnet10_cornet
403	compact_resnet50_robust_IT
404	mvit_v2_s
405	yudixie_resnet50_distance_translation_rotation_0_240908
406	ReAlnet07_ALL
407	r2plus1d_18
408	yudixie_resnet50_random_0_240908
409	yudixie_resnet50_imagenet1kpret_0_240908
410	ReAlnet08_ALL
411	yudixie_resnet50_cat_obj_class_all_latents_0_240908
412	yudixie_resnet50_object_class_0_240908
413	ReAlnet09_ALL
414	yudixie_resnet18_distance_translation_rotation_0_240719
415	resnet50_primary_visual_cortex
416	ReAlnet02_cornet
417	ReAlnet10_ALL
418	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