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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	mobilenet_v2_1_0_128	.221
87	resnet50-SIN_IN	.219
88	resnet-152_v2-tf	.217
89	convnext_base_fb_in1k	.217
90	convnext_tiny:in12k_ft_in1k	.217
91	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.216
92	focalnet_tiny_lrf_in1k	.209
93	inception_resnet_v2	.208
94	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.208
95	AdvProp_efficientnet-b0	.207
96	mobilenet_v2_0.5_160	.207
97	inception_v1	.207
98	deit_small_imagenet_full_seed-0	.206
99	resnet_101_v2	.206
100	mobilenet_v1_0.25_128	.206
101	cv_18_dagger_408_pretrained	.205
102	mobilenet_v2_0.75_128	.205
103	mobilenet_v2_0.35_192	.205
104	resnet-101_v2-tf	.203
105	mobilenet_v2_0.35_160	.201
106	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.199
107	mobilenet_v2_1.0_96	.199
108	inception_v3	.197
109	alexnet_reduced_aliasing_early_checkpoint	.196
110	mobilenet_v2_0_75_160	.195
111	vonegrcnn_52e_full	.195
112	mobilenet_v2_0_5_192	.193
113	pnasnet_large_pytorch	.193
114	resnet-50_v2-tf	.192
115	vonegrcnn_62e_nobn	.192
116	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.192
117	SWSL_resnet50	.191
118	inception_v3_pytorch	.188
119	vonegrcnn_62e_nobn	.188
120	mobilenet_v2_0.75_160-tf	.188
121	vonegrcnn_52e_full	.188
122	mobilenet_v2_1.0_128-tf	.185
123	inception_v4-tf	.185
124	nasnet_large-tf	.184
125	vonegrcnn_47e	.184
126	mobilenet_v2_0.5_192-tf	.183
127	mobilenet_v1_0.75_128	.182
128	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.182
129	nasnet_large	.182
130	mobilenet_v2_1.0_192-tf	.182
131	mobilenet_v1_0.5_128	.182
132	vonegrcnn_47e	.182
133	mobilenet_v2_1_0_160	.181
134	effnetb1_272x240	.181
135	inception_v2-tf	.181
136	inception_v3-tf	.180
137	mobilenet_v2_0_5_224	.180
138	nasnet_mobile	.180
139	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.177
140	mobilenet_v2_1_0_192	.176
141	resnet50_random_linf8_perturb	.174
142	resnet50_st_wzc_seed6_2_323	.173
143	mobilenet_v2_0.75_192-tf	.173
144	BiT-S-R152x2	.172
145	inception_v1-tf	.172
146	alexnet_early_checkpoint	.172
147	resnet50_ewconplus_wzc_seed6_2_323	.172
148	resnet50_ewconplus_wzc	.172
149	inception_v4	.172
150	cvt_cvt-13-224-in1k_4_LucyV4	.172
151	BiT-S-R50x1	.170
152	densenet-201-keras	.170
153	densenet-201	.170
154	densenet_201_pytorch	.170
155	resnet50_st_wzc_2	.169
156	alexnet_random_l2_3_perturb	.168
157	resnet50-VITO-8deg-cc	.167
158	efficientnet_b0	.166
159	convnext_tiny_sup	.166
160	mobilenet_v2_1.0_160-tf	.165
161	resnet50_moco_v2	.165
162	mobilenet_v2_0_75_192	.165
163	mobilenet_v1_0.25_192	.164
164	alexnet_random_linf8_perturb	.164
165	resnet152_imagenet_full	.164
166	grcnn_109	.164
167	cvt_cvt-13-384-in1k_4_LucyV4	.163
168	resnet_152_v1	.163
169	mobilenet_v1_0.5_160	.163
170	antialias-resnet152	.163
171	BiT-S-R101x1	.163
172	resnet50_ewconplus_wzc_seed2_2__323	.163
173	resnet50_st_wzc_seed3_2_323	.162
174	densenet-169	.162
175	resnet50_st_wzc_seed7_2_323	.162
176	cvt_cvt-21-224-in1k_4_LucyV4	.161
177	xception	.161
178	mobilenet_v2_0.5_224-tf	.161
179	voneresnet-50-non_stochastic	.160
180	densenet-169-keras	.160
181	resnet50_hvs_wzc	.160
182	resnet_50_v2	.159
183	resnet_152_v2	.158
184	resnet-101_v1-tf	.158
185	antialiased-rnext101_32x8d	.158
186	BiT-S-R152x4	.157
187	mobilenet_v1_1.0_128	.157
188	BiT-S-R101x3	.157
189	resnet_101_v1	.157
190	resnet-152_v1-tf	.156
191	efficientnet_b0_imagenet_full	.155
192	mobilenet_v2_1_0_224	.155
193	mobilenet_v2_0.35_224	.154
194	resnet34_imagenet_full	.154
195	convnext_femto_ols:d1_in1k	.154
196	convnext_base_fb_in1k_gram_aligned_dinov3	.153
197	pnasnet_large-tf	.152
198	mobilenet_v2_0_75_224	.152
199	resnet-34	.152
200	resnet50-vitoimagevidnet8	.152
201	resnet101_imagenet_full	.152
202	resnet50_ewconplus_wzc_seed5_2__323	.152
203	alexnet_ks_torevert	.151
204	alexnet_fov12	.151
205	alexnet_fov4	.151
206	pnasnet_large_halfPrecision	.151
207	alexnet_fov16	.151
208	pnasnet_large	.151
209	alexnet	.151
210	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.148
211	resnet-18	.147
212	xception-keras	.147
213	mobilenet_v1_0.75_160	.147
214	grcnn	.146
215	BiT-S-R50x3	.145
216	shufflenet_v2_x1_0	.145
217	densenet-121-keras	.144
218	densenet-121	.144
219	resnet_50_v1_spiking	.142
220	resnet18_imagenet_full	.142
221	CLIP-RN50	.142
222	resnet50_tutorial	.141
223	resnet_50_v1	.141
224	resnet50-sup	.141
225	resnet-50-pytorch	.141
226	eBarlow_lmda_02_1000ep	.140
227	regnet_y_400mf	.139
228	resnet50_imagenet_full	.139
229	nasnet_mobile-tf	.138
230	mobilenet_v1_0.25_224	.138
231	resnet50_random_l2_perturb	.138
232	mobilenet_v2_0.75_224-tf	.138
233	resnet-50_v1-tf	.137
234	resnet50-moclr8deg	.137
235	cvt_cvt-21-384-in1k_4_LucyV4	.137
236	Res2Net50_26w_4s	.136
237	mobilenet_v2_1-4_224_pytorch	.136
238	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.136
239	mobilenet_v2_1_4_224	.136
240	resnet-152_v2_pytorch	.135
241	mobilenet_v1_1.0_160	.134
242	ReAlnet06	.134
243	mobilenet_v2_1.0_224-tf	.133
244	antialiased-r50	.133
245	resnet_SIN_IN_FT_IN	.133
246	resnet50-SIN_IN_IN	.133
247	ReAlnet02	.133
248	mobilenet_v1_1.0_192	.130
249	CORnet-S	.130
250	efficientnet_b2_imagenet_full	.130
251	efficientnet_b1_imagenet_full	.129
252	ReAlnet03	.129
253	ReAlnet05	.128
254	vgg-19-keras	.128
255	ReAlnet04	.128
256	squeezenet1_1	.126
257	resnet50_byol	.124
258	ReAlnet08	.124
259	ReAlnet10	.123
260	mobilenet_v1_0.75_192	.123
261	0.5x_resnet-18_LC_w_sh_1_iter	.122
262	mobilenet_v2_1_3_224	.122
263	ReAlnet07	.122
264	deit_small_imagenet_100_seed-0	.122
265	ReAlnet01	.122
266	mobilenet_v2_1.4_224-tf	.119
267	ReAlnet09	.119
268	mobilenet_v1_0.75_224	.117
269	resnet50_simclr	.117
270	resnet50_imagenet_100_seed-0	.116
271	mobilenet_v2_1.3_224-tf	.113
272	resnet_50_v1_spiking_l4a	.111
273	convnext_small_imagenet_10_seed-0	.110
274	vgg-16-keras	.109
275	mobilenet_v1_1.0_224	.107
276	mobilenet_v1_0.5_224	.105
277	resnet_50_v1_spiking_l4	.099
278	squeezenet1_0	.089
279	deit_small_imagenet_10_seed-0	.088
280	alexnet_training_seed_07	.084
281	deit_small_imagenet_1_seed-0	.078
282	alexnet_training_seed_06	.076
283	resnet50-barlow	.076
284	alexnet_training_seed_09	.074
285	convnext_small_imagenet_1_seed-0	.072
286	vgg_19	.071
287	resnet50_imagenet_1_seed-0	.069
288	mobilevit_small	.065
289	my-model	.064
290	resnet_50_v1_spiking_l3	.064
291	vgg_16	.064
292	resnet50-vicregl0p75	.064
293	alexnet_training_seed_05	.062
294	alexnet_training_seed_02	.062
295	alexnet_training_seed_04	.062
296	alexnet_training_seed_08	.062
297	alexnet_training_seed_10	.062
298	alexnet_training_seed_01	.062
299	alexnet_training_seed_01_fov4	.062
300	alexnet_training_seed_01_fov12	.062
301	alexnet_training_seed_01_fov16	.062
302	alexnet_training_seed_03	.062
303	resnet50_imagenet_10_seed-0	.061
304	barlow-twins-resnet50	.060
305	resnet50-simclr-vissl	.059
306	resnet50-vicregl0p9	.048
307	artResNet18_1	.045
308	BiT-M-R50x3
309	BiT-M-R101x3
310	BiT-M-R50x1
311	BiT-M-R152x4
312	BiT-M-R101x1
313	BiT-M-R152x2
314	FrankRobWobv0
315	omnivore_swinT
316	ViT-B/32
317	0.5x_resnet-18
318	0.5x_resnet-18_LC_w_sh_100_iter
319	RN50
320	r3m_resnet18
321	CORnet-Z
322	0.5x_resnet-18_LC
323	vggface
324	r3m_resnet50
325	r3m_resnet34
326	resnet18-supervised
327	resnet18-local_aggregation
328	ViT_B_16_imagenet1k
329	vision_transformer_vit_large_patch16_224
330	CLIP_ViT-B_32
331	SWSL_resnext101_32x8d
332	CLIP_resnet50
333	CLIP_resnet50_float32
334	resnet101
335	resnet50
336	texture_shape_resnet50_trained_on_SIN
337	cornet_s
338	vonealexnet_gaussian_noise_std4_fixed
339	texture_shape_alexnet_trained_on_SIN
340	0.5x_resnet-18_LC_w_sh_10_iter
341	resnet18-contrastive_multiview
342	resnet18-colorization
343	resnet18-simclr
344	resnet18-depth_prediction
345	CORnetZ_CIFAR10
346	resnet18-deepcluster
347	v1-pyr-nodown
348	my-model
349	resnet18-relative_position
350	ViT_B_32_imagenet1k
351	CORnetZ_CIFAR10_bs32_20_04
352	dorinet_cornet_z
353	ViT_L_16_imagenet1k
354	resnet18-autoencoder
355	ViT_L_32
356	ViT_B_32
357	ViT_B_16
358	r3m_resnet34_nocrop
359	dcgan
360	fitvid_trained_on_physion
361	pixels
362	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
363	yudixie_resnet50_imagenet1kpret_0_240312
364	yudixie_resnet50_category_class_0_240908
365	yudixie_resnet50_distance_reg_0_240908
366	resnet18_imagenet21kP
367	ConvLSTM
368	briaai_rmbg_1_4
369	PredRNN
370	SimVP
371	TAU
372	MIM
373	hmax
374	ReAlnet01_cornet
375	ReAlnet02_cornet
376	yolos_tiny
377	yudixie_resnet18_translation_reg_0_240719
378	compact_resnet50_V4
379	yudixie_resnet18_distance_reg_0_240719
380	yudixie_resnet18_rotation_reg_0_240719
381	yudixie_resnet18_imagenet1kpret_0_240719
382	ReAlnet03_cornet
383	yudixie_resnet50_distance_rotation_0_240908
384	yudixie_resnet50_distance_translation_0_240908
385	yudixie_resnet50_rotation_reg_0_240908
386	compact_resnet50_IT
387	vit_base_patch16_dinov3-lvd1689m
388	resnet18_ecoset_full
389	VOneCORnet-S
390	resnet34_ecoset_full
391	ReAlnet04_cornet
392	resnet50_ecoset_full
393	ReAlnet01_cornet_fov4
394	resnet101_ecoset_full
395	resnet152_ecoset_full
396	aughost_fair_resnet18
397	compact_vgg19_V4
398	yudixie_resnet18_distance_translation_0_240719
399	omnivore_swinB
400	yudixie_resnet18_translation_rotation_0_240719
401	ReAlnet05_cornet
402	resnet-18_test_m
403	yudixie_resnet18_distance_rotation_0_240719
404	ReAlnet01_cornet_fov12
405	yudixie_resnet18_category_class_0_240719
406	yudixie_resnet18_random_0_240719
407	yudixie_resnet50_translation_reg_0_240908
408	yudixie_resnet18_cat_obj_class_all_latents_0_240719
409	yudixie_resnet18_distance_translation_rotation_0_240719
410	ReAlnet06_cornet
411	compact_vgg19_IT
412	yudixie_resnet18_object_class_0_240719
413	ReAlnet01_cornet_fov16
414	compact_resnet50_robust_V4
415	ReAlnet07_cornet
416	ReAlnet01_ALL
417	yudixie_resnet50_translation_rotation_0_240908
418	ReAlnet08_cornet
419	ReAlnet02_ALL
420	resnet50-vicreg
421	ReAlnet04_ALL
422	compact_cornets_V4
423	ReAlnet09_cornet
424	ReAlnet03_ALL
425	compact_cornets_IT
426	omnivore_swinS
427	ReAlnet05_ALL
428	ReAlnet10_cornet
429	ReAlnet06_ALL
430	yudixie_resnet50_imagenet1kpret_0_240908
431	yudixie_resnet50_random_0_240908
432	compact_resnet50_robust_IT
433	ReAlnet07_ALL
434	yudixie_resnet50_distance_translation_rotation_0_240908
435	yudixie_resnet50_cat_obj_class_all_latents_0_240908
436	yudixie_resnet50_object_class_0_240908
437	ReAlnet08_ALL
438	ReAlnet09_ALL
439	resnet50_primary_visual_cortex
440	ReAlnet10_ALL
441	vit_base_patch14_reg4_dinov2-lvd142m
442	vit_base_patch14_dinov2-lvd142m
443	resnet18_fair
444	pr1_stl10_disentangled_alpha04
445	predictive_coding_pc
446	yudixie_wbh_object_class_scaling_0415_run_0
447	yudixie_wbh_object_class_scaling_0415_run_3
448	yudixie_wbh_object_class_scaling_0415_imagenet
449	yudixie_wbh_object_class_scaling_0415_run_2
450	yudixie_wbh_object_class_scaling_0415_run_1
451	deepeisnn_ei_cifar10_vgg19
452	deepeisnn_ei_cifar10_resnet18
453	deepeisnn_ei_imagenet_resnet18
454	dinov2_vitl14_reg_lc_featread

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