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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	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.550
2	AdvProp_efficientnet-b8	.527
3	imagenet_l2_10_0	.515
4	resnet50-SIN	.506
5	AdvProp_efficientnet-b6	.497
6	AdvProp_efficientnet-b7	.495
7	resnet50_linf_8_robust	.482
8	AlexNet_SIN	.461
9	AlexNet_SIN_fov4	.461
10	AlexNet_SIN_fov12	.461
11	AlexNet_SIN_fov16	.461
12	resnet50_l2_3_robust	.439
13	resnet-50-robust	.432
14	imagenet_l2_3_0	.432
15	resnet50_robust_l2_eps3	.432
16	resnet50_linf_4_robust	.427
17	AT_efficientnet-b4	.415
18	AdvProp_efficientnet-b4	.415
19	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.412
20	alexnet_linf_8_robust	.404
21	voneresnet-50-robust	.393
22	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.380
23	alexnet_l2_3_robust	.372
24	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.359
25	alexnet_robust_correct	.355
26	efficientnet-b7	.353
27	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.347
28	vit_large_patch14_clip_224:openai_ft_in1k	.339
29	convnext_xlarge:fb_in22k_ft_in1k	.331
30	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.326
31	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.321
32	deit3_base_patch16_224_fb_in1k	.320
33	vit_large_patch14_clip_224:laion2b_ft_in1k	.314
34	convnext_large:fb_in22k_ft_in1k	.310
35	resnet50_robust_l2_eps1	.301
36	resnext101_32x48d_wsl	.299
37	vit_large_patch14_clip_336:laion2b_ft_in1k	.299
38	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.297
39	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.293
40	custom_model_cv_18_dagger_408	.287
41	resnext101_32x16d_wsl	.277
42	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.277
43	mobilenet_v2_0.5_96	.274
44	vit_relpos_base_patch16_clsgap_224:sw_in1k	.274
45	ViT_L_32_imagenet1k	.270
46	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.268
47	resnext101_32x32d_wsl	.267
48	deit_large_imagenet_full_seed-0	.265
49	convnext_large_imagenet_full_seed-0	.265
50	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.264
51	convnext_base_fb_in22k_ft_in1k	.261
52	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.260
53	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.258
54	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.257
55	effnetv2m_custom384	.256
56	convnext_small_imagenet_full_seed-0	.255
57	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.251
58	convnext_base_imagenet_full_seed-0	.249
59	vit_base_patch16_clip_224:openai_ft_in1k	.247
60	convnext_base:clip_laiona_augreg_ft_in1k_384	.244
61	deit3_base_patch16_224_fb_in22k_ft_in1k_gram_aligned_dinov3	.244
62	AdvProp_efficientnet-b2	.241
63	AT_efficientnet-b2	.241
64	deit_base_imagenet_full_seed-0	.240
65	resnext101_32x8d_wsl	.239
66	convnext_tiny_imagenet_full_seed-0	.238
67	fixres_resnext101_32x48d_wsl	.237
68	mobilenet_v2_0.35_128	.237
69	mobilenet_v2_0.5_128	.234
70	blt_vs	.233
71	convnext_small_imagenet_100_seed-0	.228
72	deit3_base_patch16_224_fb_in1k_gram_aligned_dinov3	.226
73	mobilenet_v2_0.75_96	.224
74	voneresnet-50	.224
75	mobilenet_v2_1_0_128	.221
76	resnet50-SIN_IN	.219
77	resnet-152_v2-tf	.217
78	convnext_base_fb_in1k	.217
79	convnext_tiny:in12k_ft_in1k	.217
80	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.216
81	focalnet_tiny_lrf_in1k	.209
82	inception_resnet_v2	.208
83	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.208
84	AdvProp_efficientnet-b0	.207
85	mobilenet_v2_0.5_160	.207
86	inception_v1	.207
87	deit_small_imagenet_full_seed-0	.206
88	resnet_101_v2	.206
89	mobilenet_v1_0.25_128	.206
90	cv_18_dagger_408_pretrained	.205
91	mobilenet_v2_0.75_128	.205
92	mobilenet_v2_0.35_192	.205
93	resnet-101_v2-tf	.203
94	mobilenet_v2_0.35_160	.201
95	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.199
96	mobilenet_v2_1.0_96	.199
97	inception_v3	.197
98	alexnet_reduced_aliasing_early_checkpoint	.196
99	mobilenet_v2_0_75_160	.195
100	vonegrcnn_52e_full	.195
101	mobilenet_v2_0_5_192	.193
102	pnasnet_large_pytorch	.193
103	resnet-50_v2-tf	.192
104	vonegrcnn_62e_nobn	.192
105	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.192
106	SWSL_resnet50	.191
107	inception_v3_pytorch	.188
108	vonegrcnn_62e_nobn	.188
109	mobilenet_v2_0.75_160-tf	.188
110	vonegrcnn_52e_full	.188
111	resnet50_ewconplus_wzc_2_323	.186
112	mobilenet_v2_1.0_128-tf	.185
113	inception_v4-tf	.185
114	nasnet_large-tf	.184
115	vonegrcnn_47e	.184
116	mobilenet_v2_0.5_192-tf	.183
117	mobilenet_v1_0.75_128	.182
118	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.182
119	nasnet_large	.182
120	mobilenet_v2_1.0_192-tf	.182
121	mobilenet_v1_0.5_128	.182
122	vonegrcnn_47e	.182
123	mobilenet_v2_1_0_160	.181
124	effnetb1_272x240	.181
125	inception_v2-tf	.181
126	inception_v3-tf	.180
127	mobilenet_v2_0_5_224	.180
128	nasnet_mobile	.180
129	resnet50_ewconplus_wzc_seed7_2_323	.179
130	resnet50_st_wzc_seed4_2_323	.179
131	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.177
132	mobilenet_v2_1_0_192	.176
133	resnet50_ewconplus_noseed_2_323	.176
134	resnet50_random_linf8_perturb	.174
135	resnet50_st_wzc_seed6_2_323	.173
136	resnet50_ewconplus_wzc_seed4_2_323	.173
137	mobilenet_v2_0.75_192-tf	.173
138	BiT-S-R152x2	.172
139	inception_v1-tf	.172
140	alexnet_early_checkpoint	.172
141	resnet50_ewconplus_wzc_seed6_2_323	.172
142	resnet50_ewconplus_wzc	.172
143	inception_v4	.172
144	cvt_cvt-13-224-in1k_4_LucyV4	.172
145	resnet50_st_wzc_noseed_2_323	.172
146	BiT-S-R50x1	.170
147	densenet-201-keras	.170
148	densenet-201	.170
149	densenet_201_pytorch	.170
150	resnet50_st_wzc_seed5_2_323	.170
151	resnet50_st_wzc_2	.169
152	resnet50_ewconplus_wzc_seed3_2_323	.169
153	alexnet_random_l2_3_perturb	.168
154	resnet50_st_wzc_2_323	.168
155	resnet50-VITO-8deg-cc	.167
156	efficientnet_b0	.166
157	convnext_tiny_sup	.166
158	mobilenet_v2_1.0_160-tf	.165
159	resnet50_moco_v2	.165
160	mobilenet_v2_0_75_192	.165
161	mobilenet_v1_0.25_192	.164
162	alexnet_random_linf8_perturb	.164
163	resnet152_imagenet_full	.164
164	grcnn_109	.164
165	cvt_cvt-13-384-in1k_4_LucyV4	.163
166	resnet_152_v1	.163
167	mobilenet_v1_0.5_160	.163
168	antialias-resnet152	.163
169	BiT-S-R101x1	.163
170	resnet50_ewconplus_wzc_seed2_2__323	.163
171	resnet50_st_wzc_seed3_2_323	.162
172	densenet-169	.162
173	resnet50_st_wzc_seed7_2_323	.162
174	cvt_cvt-21-224-in1k_4_LucyV4	.161
175	xception	.161
176	mobilenet_v2_0.5_224-tf	.161
177	voneresnet-50-non_stochastic	.160
178	densenet-169-keras	.160
179	resnet_50_v2	.159
180	resnet_152_v2	.158
181	resnet-101_v1-tf	.158
182	antialiased-rnext101_32x8d	.158
183	BiT-S-R152x4	.157
184	mobilenet_v1_1.0_128	.157
185	BiT-S-R101x3	.157
186	resnet_101_v1	.157
187	resnet-152_v1-tf	.156
188	efficientnet_b0_imagenet_full	.155
189	mobilenet_v2_1_0_224	.155
190	mobilenet_v2_0.35_224	.154
191	resnet34_imagenet_full	.154
192	convnext_femto_ols:d1_in1k	.154
193	convnext_base_fb_in1k_gram_aligned_dinov3	.153
194	pnasnet_large-tf	.152
195	mobilenet_v2_0_75_224	.152
196	resnet-34	.152
197	resnet50-vitoimagevidnet8	.152
198	resnet101_imagenet_full	.152
199	resnet50_ewconplus_wzc_seed5_2__323	.152
200	alexnet_ks_torevert	.151
201	alexnet_fov12	.151
202	alexnet_fov4	.151
203	pnasnet_large_halfPrecision	.151
204	alexnet_fov16	.151
205	pnasnet_large	.151
206	alexnet	.151
207	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.148
208	resnet-18	.147
209	xception-keras	.147
210	mobilenet_v1_0.75_160	.147
211	grcnn	.146
212	BiT-S-R50x3	.145
213	shufflenet_v2_x1_0	.145
214	densenet-121-keras	.144
215	densenet-121	.144
216	resnet_50_v1_spiking	.142
217	resnet18_imagenet_full	.142
218	CLIP-RN50	.142
219	resnet50_tutorial	.141
220	resnet_50_v1	.141
221	resnet50-sup	.141
222	resnet-50-pytorch	.141
223	eBarlow_lmda_02_1000ep	.140
224	regnet_y_400mf	.139
225	resnet50_imagenet_full	.139
226	nasnet_mobile-tf	.138
227	mobilenet_v1_0.25_224	.138
228	resnet50_random_l2_perturb	.138
229	mobilenet_v2_0.75_224-tf	.138
230	resnet-50_v1-tf	.137
231	resnet50-moclr8deg	.137
232	cvt_cvt-21-384-in1k_4_LucyV4	.137
233	Res2Net50_26w_4s	.136
234	mobilenet_v2_1-4_224_pytorch	.136
235	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.136
236	mobilenet_v2_1_4_224	.136
237	resnet-152_v2_pytorch	.135
238	mobilenet_v1_1.0_160	.134
239	ReAlnet06	.134
240	mobilenet_v2_1.0_224-tf	.133
241	antialiased-r50	.133
242	resnet_SIN_IN_FT_IN	.133
243	resnet50-SIN_IN_IN	.133
244	ReAlnet02	.133
245	mobilenet_v1_1.0_192	.130
246	CORnet-S	.130
247	efficientnet_b2_imagenet_full	.130
248	efficientnet_b1_imagenet_full	.129
249	ReAlnet03	.129
250	ReAlnet05	.128
251	vgg-19-keras	.128
252	ReAlnet04	.128
253	squeezenet1_1	.126
254	resnet50_byol	.124
255	ReAlnet08	.124
256	ReAlnet10	.123
257	mobilenet_v1_0.75_192	.123
258	0.5x_resnet-18_LC_w_sh_1_iter	.122
259	mobilenet_v2_1_3_224	.122
260	ReAlnet07	.122
261	deit_small_imagenet_100_seed-0	.122
262	ReAlnet01	.122
263	mobilenet_v2_1.4_224-tf	.119
264	ReAlnet09	.119
265	mobilenet_v1_0.75_224	.117
266	resnet50_simclr	.117
267	resnet50_imagenet_100_seed-0	.116
268	mobilenet_v2_1.3_224-tf	.113
269	resnet_50_v1_spiking_l4a	.111
270	convnext_small_imagenet_10_seed-0	.110
271	vgg-16-keras	.109
272	mobilenet_v1_1.0_224	.107
273	mobilenet_v1_0.5_224	.105
274	resnet_50_v1_spiking_l4	.099
275	squeezenet1_0	.089
276	deit_small_imagenet_10_seed-0	.088
277	alexnet_training_seed_07	.084
278	deit_small_imagenet_1_seed-0	.078
279	alexnet_training_seed_06	.076
280	resnet50-barlow	.076
281	alexnet_training_seed_09	.074
282	convnext_small_imagenet_1_seed-0	.072
283	vgg_19	.071
284	resnet50_imagenet_1_seed-0	.069
285	resnet50-meshes-lt-100-original-pretrained	.065
286	mobilevit_small	.065
287	my-model	.064
288	resnet_50_v1_spiking_l3	.064
289	vgg_16	.064
290	resnet50-vicregl0p75	.064
291	resnet50-meshes-lt-100-original-scratch	.062
292	alexnet_training_seed_05	.062
293	alexnet_training_seed_02	.062
294	alexnet_training_seed_04	.062
295	alexnet_training_seed_08	.062
296	alexnet_training_seed_10	.062
297	alexnet_training_seed_01	.062
298	alexnet_training_seed_01_fov4	.062
299	alexnet_training_seed_01_fov12	.062
300	alexnet_training_seed_01_fov16	.062
301	alexnet_training_seed_03	.062
302	resnet50_imagenet_10_seed-0	.061
303	barlow-twins-resnet50	.060
304	resnet50-simclr-vissl	.059
305	resnet50-vicregl0p9	.048
306	artResNet18_1	.045
307	BiT-M-R50x3
308	BiT-M-R101x3
309	BiT-M-R50x1
310	BiT-M-R152x4
311	BiT-M-R101x1
312	BiT-M-R152x2
313	resnet50
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

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