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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("Hermann2020cueconflict-shape_match")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	dinov2_vitg14_reg_lc	.567
2	convnext_xxlarge_clip	.554
3	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.550
4	dinov2_vitg14_lc	.529
5	AdvProp_efficientnet-b8	.527
6	dinov2_vitl14_reg_lc	.518
7	eva02_large_in22k	.516
8	imagenet_l2_10_0	.515
9	resnet50-SIN	.506
10	AdvProp_efficientnet-b6	.497
11	AdvProp_efficientnet-b7	.495
12	resnet50_linf_8_robust	.482
13	dinov2_vitl14_lc	.477
14	dinov2_vitl14_lc_res336	.470
15	AlexNet_SIN	.461
16	AlexNet_SIN_fov4	.461
17	AlexNet_SIN_fov12	.461
18	AlexNet_SIN_fov16	.461
19	resnet50_l2_3_robust	.439
20	resnet-50-robust	.432
21	imagenet_l2_3_0	.432
22	resnet50_robust_l2_eps3	.432
23	resnet50_linf_4_robust	.427
24	AT_efficientnet-b4	.415
25	AdvProp_efficientnet-b4	.415
26	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.412
27	alexnet_linf_8_robust	.404
28	voneresnet-50-robust	.393
29	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.380
30	alexnet_l2_3_robust	.372
31	vit_huge_clip336_laion	.369
32	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.359
33	alexnet_robust_correct	.355
34	efficientnet-b7	.353
35	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.347
36	vit_large_clip336_openai	.340
37	vit_large_patch14_clip_224:openai_ft_in1k	.339
38	convnext_xlarge:fb_in22k_ft_in1k	.331
39	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.326
40	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.321
41	deit3_base_patch16_224_fb_in1k	.320
42	vit_large_patch14_clip_224:laion2b_ft_in1k	.314
43	convnext_large:fb_in22k_ft_in1k	.310
44	convnext_large_in22k_supervised	.302
45	resnet50_robust_l2_eps1	.301
46	resnext101_32x48d_wsl	.299
47	vit_large_patch14_clip_336:laion2b_ft_in1k	.299
48	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.297
49	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.293
50	custom_model_cv_18_dagger_408	.287
51	convnext_large_clip_laion2b_finelayers	.279
52	resnext101_32x16d_wsl	.277
53	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.277
54	mobilenet_v2_0.5_96	.274
55	vit_relpos_base_patch16_clsgap_224:sw_in1k	.274
56	ViT_L_32_imagenet1k	.270
57	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.268
58	resnext101_32x32d_wsl	.267
59	deit_large_imagenet_full_seed-0	.265
60	convnext_large_imagenet_full_seed-0	.265
61	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.264
62	convnext_base_fb_in22k_ft_in1k	.261
63	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.260
64	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.258
65	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.257
66	effnetv2m_custom384	.256
67	convnext_small_imagenet_full_seed-0	.255
68	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.251
69	convnext_base_imagenet_full_seed-0	.249
70	vit_base_patch16_clip_224:openai_ft_in1k	.247
71	convnext_base:clip_laiona_augreg_ft_in1k_384	.244
72	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.244
73	AdvProp_efficientnet-b2	.241
74	AT_efficientnet-b2	.241
75	deit_base_imagenet_full_seed-0	.240
76	resnext101_32x8d_wsl	.239
77	convnext_tiny_imagenet_full_seed-0	.238
78	fixres_resnext101_32x48d_wsl	.237
79	mobilenet_v2_0.35_128	.237
80	mobilenet_v2_0.5_128	.234
81	blt_vs	.233
82	convnext_small_imagenet_100_seed-0	.228
83	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.226
84	mobilenet_v2_0.75_96	.224
85	voneresnet-50	.224
86	mehrer2020_alexnet_training_seed_03	.224
87	mobilenet_v2_1_0_128	.221
88	resnet50-SIN_IN	.219
89	resnet-152_v2-tf	.217
90	mehrer2020_alexnet_training_seed_05	.217
91	convnext_base_fb_in1k	.217
92	convnext_tiny:in12k_ft_in1k	.217
93	mehrer2020_alexnet_training_seed_01	.217
94	mehrer2020_alexnet_training_seed_01_fov12	.217
95	mehrer2020_alexnet_training_seed_01_fov4	.217
96	mehrer2020_alexnet_training_seed_01_fov16	.217
97	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.216
98	mehrer2020_alexnet_training_seed_02	.213
99	focalnet_tiny_lrf_in1k	.209
100	inception_resnet_v2	.208
101	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.208
102	AdvProp_efficientnet-b0	.207
103	mobilenet_v2_0.5_160	.207
104	inception_v1	.207
105	deit_small_imagenet_full_seed-0	.206
106	resnet_101_v2	.206
107	ijepa_vith14_linhead	.206
108	mobilenet_v1_0.25_128	.206
109	cv_18_dagger_408_pretrained	.205
110	mobilenet_v2_0.75_128	.205
111	mobilenet_v2_0.35_192	.205
112	mehrer2020_alexnet_training_seed_04	.205
113	resnet-101_v2-tf	.203
114	regnety_032	.202
115	mobilenet_v2_0.35_160	.201
116	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.199
117	mobilenet_v2_1.0_96	.199
118	inception_v3	.197
119	alexnet_reduced_aliasing_early_checkpoint	.196
120	mobilenet_v2_0_75_160	.195
121	vonegrcnn_52e_full	.195
122	mobilenet_v2_0_5_192	.193
123	pnasnet_large_pytorch	.193
124	resnet-50_v2-tf	.192
125	vonegrcnn_62e_nobn	.192
126	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.192
127	SWSL_resnet50	.191
128	seresnext50_32x4d	.189
129	inception_v3_pytorch	.188
130	vonegrcnn_62e_nobn	.188
131	mobilenet_v2_0.75_160-tf	.188
132	vonegrcnn_52e_full	.188
133	mobilenet_v2_1.0_128-tf	.185
134	inception_v4-tf	.185
135	nasnet_large-tf	.184
136	vonegrcnn_47e	.184
137	mobilenet_v2_0.5_192-tf	.183
138	mobilenet_v1_0.75_128	.182
139	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.182
140	nasnet_large	.182
141	mobilenet_v2_1.0_192-tf	.182
142	mobilenet_v1_0.5_128	.182
143	vonegrcnn_47e	.182
144	mobilenet_v2_1_0_160	.181
145	effnetb1_272x240	.181
146	inception_v2-tf	.181
147	inception_v3-tf	.180
148	mobilenet_v2_0_5_224	.180
149	nasnet_mobile	.180
150	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.177
151	mobilenet_v2_1_0_192	.176
152	resnet50_random_linf8_perturb	.174
153	resnet50_st_wzc_seed6_2_323	.173
154	mobilenet_v2_0.75_192-tf	.173
155	BiT-S-R152x2	.172
156	inception_v1-tf	.172
157	alexnet_early_checkpoint	.172
158	resnet50_ewconplus_wzc_seed6_2_323	.172
159	resnet50_ewconplus_wzc	.172
160	inception_v4	.172
161	cvt_cvt-13-224-in1k_4_LucyV4	.172
162	BiT-S-R50x1	.170
163	densenet-201-keras	.170
164	densenet-201	.170
165	densenet_201_pytorch	.170
166	resnet50_st_wzc_2	.169
167	alexnet_random_l2_3_perturb	.168
168	resnet50-VITO-8deg-cc	.167
169	efficientnet_b0	.166
170	convnext_tiny_sup	.166
171	mobilenet_v2_1.0_160-tf	.165
172	resnet50_moco_v2	.165
173	mobilenet_v2_0_75_192	.165
174	mobilenet_v1_0.25_192	.164
175	alexnet_random_linf8_perturb	.164
176	resnet152_imagenet_full	.164
177	grcnn_109	.164
178	cvt_cvt-13-384-in1k_4_LucyV4	.163
179	resnet_152_v1	.163
180	mobilenet_v1_0.5_160	.163
181	antialias-resnet152	.163
182	BiT-S-R101x1	.163
183	resnet50_ewconplus_wzc_seed2_2__323	.163
184	resnet50_st_wzc_seed3_2_323	.162
185	densenet-169	.162
186	resnet50_st_wzc_seed7_2_323	.162
187	cvt_cvt-21-224-in1k_4_LucyV4	.161
188	xception	.161
189	resnet50_barlowtwins_linhead	.161
190	mobilenet_v2_0.5_224-tf	.161
191	voneresnet-50-non_stochastic	.160
192	densenet-169-keras	.160
193	resnet50_hvs_wzc	.160
194	resnet_50_v2	.159
195	resnet_152_v2	.158
196	resnet-101_v1-tf	.158
197	antialiased-rnext101_32x8d	.158
198	BiT-S-R152x4	.157
199	mobilenet_v1_1.0_128	.157
200	BiT-S-R101x3	.157
201	resnet_101_v1	.157
202	eicircuit_resnet18_bn_20260919	.157
203	resnet-152_v1-tf	.156
204	efficientnet_b0_imagenet_full	.155
205	mobilenet_v2_1_0_224	.155
206	mobilenet_v2_0.35_224	.154
207	resnet34_imagenet_full	.154
208	convnext_femto_ols:d1_in1k	.154
209	resnet50_simsiam_linhead	.154
210	convnext_base_fb_in1k_gram_aligned_dinov3	.153
211	pnasnet_large-tf	.152
212	mobilenet_v2_0_75_224	.152
213	resnet-34	.152
214	resnet50-vitoimagevidnet8	.152
215	resnet101_imagenet_full	.152
216	resnet50_ewconplus_wzc_seed5_2__323	.152
217	alexnet_ks_torevert	.151
218	alexnet_fov12	.151
219	alexnet_fov4	.151
220	pnasnet_large_halfPrecision	.151
221	alexnet_fov16	.151
222	pnasnet_large	.151
223	alexnet	.151
224	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.148
225	resnet-18	.147
226	xception-keras	.147
227	mobilenet_v1_0.75_160	.147
228	grcnn	.146
229	BiT-S-R50x3	.145
230	shufflenet_v2_x1_0	.145
231	densenet-121-keras	.144
232	densenet-121	.144
233	resnet50_byol_linhead	.144
234	resnet_50_v1_spiking	.142
235	resnet18_imagenet_full	.142
236	CLIP-RN50	.142
237	resnet50_tutorial	.141
238	resnet_50_v1	.141
239	resnet50-sup	.141
240	resnet-50-pytorch	.141
241	eBarlow_lmda_02_1000ep	.140
242	regnet_y_400mf	.139
243	resnet50_imagenet_full	.139
244	nasnet_mobile-tf	.138
245	mobilenet_v1_0.25_224	.138
246	resnet50_random_l2_perturb	.138
247	mobilenet_v2_0.75_224-tf	.138
248	resnet50_vicreg_linhead	.138
249	resnet-50_v1-tf	.137
250	resnet50-moclr8deg	.137
251	cvt_cvt-21-384-in1k_4_LucyV4	.137
252	Res2Net50_26w_4s	.136
253	mobilenet_v2_1-4_224_pytorch	.136
254	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.136
255	mobilenet_v2_1_4_224	.136
256	resnet-152_v2_pytorch	.135
257	mobilenet_v1_1.0_160	.134
258	ReAlnet06	.134
259	mobilenet_v2_1.0_224-tf	.133
260	antialiased-r50	.133
261	resnet_SIN_IN_FT_IN	.133
262	resnet50-SIN_IN_IN	.133
263	ReAlnet02	.133
264	mobilenet_v1_1.0_192	.130
265	CORnet-S	.130
266	efficientnet_b2_imagenet_full	.130
267	efficientnet_b1_imagenet_full	.129
268	ReAlnet03	.129
269	ReAlnet05	.128
270	vgg-19-keras	.128
271	ReAlnet04	.128
272	resnet50_simclr_linhead_01norm	.127
273	squeezenet1_1	.126
274	resnet50_byol	.124
275	ReAlnet08	.124
276	ReAlnet10	.123
277	mobilenet_v1_0.75_192	.123
278	0.5x_resnet-18_LC_w_sh_1_iter	.122
279	mobilenet_v2_1_3_224	.122
280	ReAlnet07	.122
281	deit_small_imagenet_100_seed-0	.122
282	ReAlnet01	.122
283	mobilenet_v2_1.4_224-tf	.119
284	ReAlnet09	.119
285	mobilenet_v1_0.75_224	.117
286	resnet50_simclr	.117
287	resnet50_imagenet_100_seed-0	.116
288	mobilenet_v2_1.3_224-tf	.113
289	resnet_50_v1_spiking_l4a	.111
290	convnext_small_imagenet_10_seed-0	.110
291	vgg-16-keras	.109
292	mobilenet_v1_1.0_224	.107
293	mobilenet_v1_0.5_224	.105
294	resnet_50_v1_spiking_l4	.099
295	squeezenet1_0	.089
296	deit_small_imagenet_10_seed-0	.088
297	alexnet_training_seed_07	.084
298	deit_small_imagenet_1_seed-0	.078
299	alexnet_training_seed_06	.076
300	resnet50-barlow	.076
301	alexnet_training_seed_09	.074
302	convnext_small_imagenet_1_seed-0	.072
303	vgg_19	.071
304	resnet50_imagenet_1_seed-0	.069
305	mobilevit_small	.065
306	my-model	.064
307	resnet_50_v1_spiking_l3	.064
308	vgg_16	.064
309	resnet50-vicregl0p75	.064
310	alexnet_training_seed_05	.062
311	alexnet_training_seed_02	.062
312	alexnet_training_seed_04	.062
313	alexnet_training_seed_08	.062
314	alexnet_training_seed_10	.062
315	alexnet_training_seed_01	.062
316	alexnet_training_seed_01_fov4	.062
317	alexnet_training_seed_01_fov12	.062
318	alexnet_training_seed_01_fov16	.062
319	alexnet_training_seed_03	.062
320	resnet50_imagenet_10_seed-0	.061
321	barlow-twins-resnet50	.060
322	resnet50-simclr-vissl	.059
323	resnet50-vicregl0p9	.048
324	artResNet18_1	.045
325	BiT-M-R50x3
326	BiT-M-R101x3
327	BiT-M-R50x1
328	BiT-M-R152x4
329	BiT-M-R101x1
330	BiT-M-R152x2
331	FrankRobWobv0
332	omnivore_swinT
333	ViT-B/32
334	0.5x_resnet-18
335	0.5x_resnet-18_LC_w_sh_100_iter
336	RN50
337	r3m_resnet18
338	CORnet-Z
339	0.5x_resnet-18_LC
340	vggface
341	r3m_resnet50
342	r3m_resnet34
343	resnet18-supervised
344	resnet18-local_aggregation
345	ViT_B_16_imagenet1k
346	vision_transformer_vit_large_patch16_224
347	CLIP_ViT-B_32
348	SWSL_resnext101_32x8d
349	CLIP_resnet50
350	CLIP_resnet50_float32
351	resnet101
352	resnet50
353	texture_shape_resnet50_trained_on_SIN
354	cornet_s
355	vonealexnet_gaussian_noise_std4_fixed
356	texture_shape_alexnet_trained_on_SIN
357	0.5x_resnet-18_LC_w_sh_10_iter
358	resnet18-contrastive_multiview
359	resnet18-colorization
360	resnet18-simclr
361	resnet18-depth_prediction
362	CORnetZ_CIFAR10
363	resnet18-deepcluster
364	v1-pyr-nodown
365	my-model
366	resnet18-relative_position
367	ViT_B_32_imagenet1k
368	CORnetZ_CIFAR10_bs32_20_04
369	dorinet_cornet_z
370	ViT_L_16_imagenet1k
371	resnet18-autoencoder
372	ViT_L_32
373	ViT_B_32
374	ViT_B_16
375	r3m_resnet34_nocrop
376	dcgan
377	fitvid_trained_on_physion
378	pixels
379	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
380	yudixie_resnet50_imagenet1kpret_0_240312
381	yudixie_resnet50_category_class_0_240908
382	yudixie_resnet50_distance_reg_0_240908
383	resnet18_imagenet21kP
384	ConvLSTM
385	briaai_rmbg_1_4
386	PredRNN
387	SimVP
388	TAU
389	MIM
390	hmax
391	ReAlnet01_cornet
392	ReAlnet02_cornet
393	yolos_tiny
394	yudixie_resnet18_translation_reg_0_240719
395	compact_resnet50_V4
396	yudixie_resnet18_distance_reg_0_240719
397	yudixie_resnet18_rotation_reg_0_240719
398	yudixie_resnet18_imagenet1kpret_0_240719
399	yudixie_resnet50_distance_rotation_0_240908
400	yudixie_resnet50_distance_translation_0_240908
401	yudixie_resnet50_rotation_reg_0_240908
402	compact_resnet50_IT
403	vit_base_patch16_dinov3-lvd1689m
404	resnet18_ecoset_full
405	VOneCORnet-S
406	resnet34_ecoset_full
407	ReAlnet04_cornet
408	resnet50_ecoset_full
409	ReAlnet01_cornet_fov4
410	resnet152_ecoset_full
411	compact_vgg19_V4
412	yudixie_resnet18_distance_translation_0_240719
413	omnivore_swinB
414	yudixie_resnet18_translation_rotation_0_240719
415	ReAlnet05_cornet
416	resnet-18_test_m
417	yudixie_resnet18_distance_rotation_0_240719
418	ReAlnet01_cornet_fov12
419	yudixie_resnet18_category_class_0_240719
420	yudixie_resnet18_random_0_240719
421	yudixie_resnet50_translation_reg_0_240908
422	yudixie_resnet18_cat_obj_class_all_latents_0_240719
423	yudixie_resnet18_distance_translation_rotation_0_240719
424	ReAlnet06_cornet
425	compact_vgg19_IT
426	yudixie_resnet18_object_class_0_240719
427	compact_resnet50_robust_V4
428	ReAlnet07_cornet
429	ReAlnet01_ALL
430	yudixie_resnet50_translation_rotation_0_240908
431	ReAlnet02_ALL
432	resnet50-vicreg
433	ReAlnet04_ALL
434	compact_cornets_V4
435	ReAlnet09_cornet
436	ReAlnet03_ALL
437	compact_cornets_IT
438	omnivore_swinS
439	ReAlnet05_ALL
440	ReAlnet10_cornet
441	ReAlnet06_ALL
442	yudixie_resnet50_imagenet1kpret_0_240908
443	yudixie_resnet50_random_0_240908
444	compact_resnet50_robust_IT
445	ReAlnet07_ALL
446	yudixie_resnet50_distance_translation_rotation_0_240908
447	yudixie_resnet50_cat_obj_class_all_latents_0_240908
448	yudixie_resnet50_object_class_0_240908
449	ReAlnet08_ALL
450	ReAlnet09_ALL
451	resnet50_primary_visual_cortex
452	ReAlnet10_ALL
453	vit_base_patch14_reg4_dinov2-lvd142m
454	vit_base_patch14_dinov2-lvd142m
455	ReAlnet01_cornet_fov16
456	ReAlnet03_cornet
457	ReAlnet08_cornet
458	resnet101_ecoset_full
459	resnet18_fair
460	pr1_stl10_disentangled_alpha04
461	predictive_coding_pc
462	yudixie_wbh_object_class_scaling_0415_run_0
463	yudixie_wbh_object_class_scaling_0415_run_3
464	yudixie_wbh_object_class_scaling_0415_imagenet
465	yudixie_wbh_object_class_scaling_0415_run_2
466	yudixie_wbh_object_class_scaling_0415_run_1
467	deepeisnn_ei_cifar10_vgg19
468	deepeisnn_ei_cifar10_resnet18
469	deepeisnn_ei_imagenet_resnet18
470	dinov2_vitl14_reg_lc_featread
471	gabor_filter_entropy_4x4
472	gabor_filter_mean_abs_4x4_v2
473	gabor_filter_entropy_4x4_v2
474	gabor_filter_entropy_4x4_v2_forcedCosine

Benchmark bibtex

@article{hermann2020origins,
              title={The origins and prevalence of texture bias in convolutional neural networks},
              author={Hermann, Katherine and Chen, Ting and Kornblith, Simon},
              journal={Advances in Neural Information Processing Systems},
              volume={33},
              pages={19000--19015},
              year={2020},
              url={https://proceedings.neurips.cc/paper/2020/hash/db5f9f42a7157abe65bb145000b5871a-Abstract.html}
        }

Ceiling

1.00.

Note that scores are relative to this ceiling.

Data: Hermann2020cueconflict

Metric: shape_match