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Sample stimuli

How to use

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

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	AdvProp_efficientnet-b8	.858
2	imagenet_l2_10_0	.819
3	resnet50-SIN	.814
4	AlexNet_SIN_fov4	.767
5	AlexNet_SIN_fov12	.767
6	AlexNet_SIN_fov16	.767
7	AlexNet_SIN	.766
8	AdvProp_efficientnet-b6	.758
9	resnet50_linf_8_robust	.750
10	AdvProp_efficientnet-b7	.729
11	tf_efficientnetv2_s_in21ft1k_robust_linf12255_400x400	.715
12	alexnet_linf_8_robust	.706
13	resnet50_l2_3_robust	.704
14	dinov2_vitg14_reg_lc	.683
15	AdvProp_efficientnet-b4	.680
16	AT_efficientnet-b4	.680
17	alexnet_l2_3_robust	.672
18	resnet50_linf_4_robust	.656
19	alexnet_training_seed_07	.656
20	alexnet_robust_correct	.654
21	resnet-50-robust	.654
22	imagenet_l2_3_0	.654
23	resnet50_robust_l2_eps3	.654
24	dinov2_vitg14_lc	.652
25	dinov2_vitl14_reg_lc	.643
26	convnext_xxlarge_clip	.636
27	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.624
28	deit_small_imagenet_10_seed-0	.606
29	voneresnet-50-robust	.604
30	dinov2_vitl14_lc	.599
31	eva02_large_in22k	.587
32	deit_small_imagenet_1_seed-0	.584
33	alexnet_training_seed_09	.571
34	efficientnet-b7	.565
35	artResNet18_1	.564
36	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.564
37	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.563
38	resnet50-barlow	.558
39	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.542
40	alexnet_training_seed_06	.538
41	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.537
42	convnext_small_imagenet_10_seed-0	.532
43	mobilenet_v2_0.5_96	.521
44	ViT_L_32_imagenet1k	.513
45	barlow-twins-resnet50	.511
46	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.508
47	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.505
48	alexnet_training_seed_08	.503
49	alexnet_training_seed_05	.500
50	alexnet_training_seed_02	.500
51	alexnet_training_seed_04	.500
52	alexnet_training_seed_10	.500
53	alexnet_training_seed_01	.500
54	alexnet_training_seed_01_fov4	.500
55	alexnet_training_seed_01_fov12	.500
56	alexnet_training_seed_01_fov16	.500
57	alexnet_training_seed_03	.500
58	resnet50-simclr-vissl	.497
59	resnet50_robust_l2_eps1	.485
60	my-model	.478
61	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.474
62	mobilenet_v2_0.35_128	.456
63	custom_model_cv_18_dagger_408	.453
64	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.453
65	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.453
66	vit_huge_clip336_laion	.444
67	resnext101_32x48d_wsl	.442
68	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.437
69	vit_large_patch14_clip_224:openai_ft_in1k	.432
70	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.430
71	resnet50-vicregl0p9	.429
72	resnext101_32x16d_wsl	.426
73	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.420
74	resnet50_imagenet_1_seed-0	.419
75	mobilenet_v2_0.5_128	.416
76	deit3_base_patch16_224_fb_in1k	.414
77	vit_large_clip336_openai	.413
78	convnext_xlarge:fb_in22k_ft_in1k	.412
79	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.412
80	vit_large_patch14_clip_224:laion2b_ft_in1k	.410
81	resnext101_32x32d_wsl	.408
82	mobilenet_v2_0.75_96	.406
83	mobilenet_v2_1.0_96	.405
84	convnext_large:fb_in22k_ft_in1k	.401
85	vit_relpos_base_patch16_clsgap_224:sw_in1k	.399
86	deit3_base_patch16_224_fb_in22k_ft_in1k	.399
87	convnext_small_imagenet_1_seed-0	.396
88	voneresnet-50	.394
89	mobilenet_v1_0.25_128	.393
90	blt_vs	.391
91	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.388
92	vit_large_patch14_clip_336:laion2b_ft_in1k	.388
93	AdvProp_efficientnet-b2	.387
94	AT_efficientnet-b2	.387
95	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.386
96	convnext_large_in22k_supervised	.386
97	deit_large_imagenet_full_seed-0	.385
98	mobilenet_v2_1_0_128	.372
99	resnext101_32x8d_wsl	.370
100	convnext_large_imagenet_full_seed-0	.368
101	deit_base_imagenet_full_seed-0	.368
102	mobilenet_v2_1.0_128-tf	.363
103	convnext_large_clip_laion2b_finelayers	.362
104	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.360
105	effnetv2m_custom384	.357
106	pnasnet_large_pytorch	.355
107	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.350
108	alexnet_reduced_aliasing_early_checkpoint	.350
109	mobilenet_v2_0.75_128	.349
110	mobilenet_v2_0.35_160	.349
111	fixres_resnext101_32x48d_wsl	.348
112	resnet50-SIN_IN	.347
113	vit_base_patch16_clip_224:openai_ft_in1k	.345
114	convnext_small_imagenet_full_seed-0	.340
115	mobilenet_v2_0.5_160	.339
116	mobilenet_v2_0_75_160	.338
117	mobilenet_v2_0.35_192	.337
118	resnet-152_v2-tf	.336
119	vonegrcnn_52e_full	.336
120	vonegrcnn_62e_nobn	.336
121	convnext_base_fb_in22k_ft_in1k	.333
122	vonegrcnn_62e_nobn	.331
123	convnext_small_imagenet_100_seed-0	.330
124	convnext_base:clip_laiona_augreg_ft_in1k_384	.325
125	mobilenet_v2_0.75_160-tf	.323
126	vonegrcnn_47e	.322
127	convnext_tiny_imagenet_full_seed-0	.322
128	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.321
129	mobilenet_v2_0_5_192	.321
130	vonegrcnn_52e_full	.320
131	resnet50-vicregl0p75	.319
132	convnext_base_imagenet_full_seed-0	.318
133	vonegrcnn_47e	.318
134	mobilenet_v1_0.25_192	.315
135	AdvProp_efficientnet-b0	.314
136	deit_small_imagenet_full_seed-0	.313
137	mobilenet_v1_0.75_128	.312
138	mobilenet_v1_0.5_128	.311
139	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.309
140	convnext_base_fb_in1k	.303
141	mobilenet_v2_0.5_192-tf	.301
142	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.298
143	inception_v3	.297
144	inception_resnet_v2	.296
145	resnet50_moco_v2	.296
146	resnet-101_v2-tf	.293
147	convnext_tiny:in12k_ft_in1k	.292
148	resnet_101_v2	.292
149	BiT-S-R152x2	.290
150	nasnet_mobile	.289
151	inception_v1	.289
152	SWSL_resnet50	.288
153	mobilenet_v2_1_0_160	.287
154	convnext_base_fb_in1k_gram_aligned_dinov3	.287
155	resnet_50_v1_spiking_l3	.286
156	focalnet_tiny_lrf_in1k	.286
157	resnet50_random_linf8_perturb	.285
158	inception_v2-tf	.283
159	eBarlow_lmda_02_1000ep	.282
160	cv_18_dagger_408_pretrained	.281
161	resnet-50_v2-tf	.280
162	inception_v4-tf	.278
163	mobilenet_v2_1.0_192-tf	.278
164	antialias-resnet152	.275
165	mobilenet_v2_1-4_224_pytorch	.275
166	mobilenet_v2_1_4_224	.275
167	mobilenet_v2_1.0_160-tf	.275
168	mobilenet_v1_1.0_128	.274
169	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.272
170	effnetb1_272x240	.272
171	BiT-S-R50x1	.271
172	mobilenet_v2_1_0_192	.271
173	mobilenet_v1_0.5_160	.271
174	xception	.271
175	alexnet_early_checkpoint	.271
176	resnet50-VITO-8deg-cc	.270
177	inception_v3-tf	.270
178	resnet50_byol	.270
179	resnet50-vitoimagevidnet8	.269
180	alexnet_random_linf8_perturb	.268
181	alexnet_random_l2_3_perturb	.268
182	resnet50-moclr8deg	.266
183	mobilenet_v2_0.75_192-tf	.266
184	voneresnet-50-non_stochastic	.264
185	mobilenet_v2_0_5_224	.263
186	resnet152_imagenet_full	.263
187	resnet50_st_wzc_2	.263
188	resnet50_ewconplus_wzc	.261
189	densenet-201	.260
190	densenet_201_pytorch	.260
191	densenet-201-keras	.260
192	resnet50_st_wzc_seed6_2_323	.259
193	BiT-S-R101x3	.259
194	grcnn_109	.258
195	inception_v4	.258
196	BiT-S-R152x4	.256
197	resnet_101_v1	.254
198	antialiased-rnext101_32x8d	.254
199	BiT-S-R101x1	.253
200	nasnet_large-tf	.252
201	mobilenet_v2_0_75_192	.250
202	resnet101_imagenet_full	.249
203	resnet_152_v1	.245
204	mobilenet_v1_0.75_160	.243
205	cvt_cvt-13-224-in1k_4_LucyV4	.243
206	mobilenet_v2_1_0_224	.242
207	nasnet_large	.242
208	mobilenet_v2_0.35_224	.242
209	resnet-152_v1-tf	.241
210	efficientnet_b0	.240
211	alexnet_ks_torevert	.239
212	alexnet_fov4	.239
213	alexnet_fov12	.239
214	alexnet_fov16	.239
215	alexnet	.239
216	cvt_cvt-21-224-in1k_4_LucyV4	.238
217	resnet50_hvs_wzc	.238
218	resnet34_imagenet_full	.237
219	resnet50_ewconplus_wzc_seed2_2__323	.236
220	mobilenet_v2_0.5_224-tf	.236
221	densenet-169	.235
222	efficientnet_b0_imagenet_full	.233
223	mobilenet_v1_0.25_224	.233
224	densenet-169-keras	.232
225	0.5x_resnet-18_LC_w_sh_1_iter	.231
226	deit_small_imagenet_100_seed-0	.231
227	BiT-S-R50x3	.230
228	resnet-101_v1-tf	.230
229	inception_v1-tf	.228
230	resnet50_st_wzc_seed3_2_323	.227
231	resnet_152_v2	.226
232	cvt_cvt-13-384-in1k_4_LucyV4	.226
233	shufflenet_v2_x1_0	.226
234	grcnn	.226
235	regnet_y_400mf	.224
236	resnet_50_v1_spiking	.223
237	resnet_50_v2	.222
238	mobilenet_v2_0_75_224	.221
239	resnet50_imagenet_100_seed-0	.221
240	pnasnet_large-tf	.220
241	nasnet_mobile-tf	.220
242	pnasnet_large_halfPrecision	.219
243	pnasnet_large	.219
244	resnet50_simclr	.217
245	resnet50_imagenet_full	.217
246	convnext_tiny_sup	.217
247	resnet18_imagenet_full	.215
248	resnet50-sup	.214
249	resnet-50-pytorch	.214
250	resnet50_tutorial	.214
251	resnet_50_v1	.214
252	antialiased-r50	.214
253	mobilenet_v1_1.0_160	.214
254	convnext_femto_ols:d1_in1k	.213
255	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.213
256	mobilenet_v1_1.0_192	.212
257	CLIP-RN50	.211
258	resnet-18	.211
259	cvt_cvt-21-384-in1k_4_LucyV4	.211
260	resnet50_random_l2_perturb	.207
261	densenet-121	.206
262	densenet-121-keras	.206
263	resnet_SIN_IN_FT_IN	.205
264	resnet50-SIN_IN_IN	.205
265	Res2Net50_26w_4s	.205
266	resnet50_imagenet_10_seed-0	.204
267	mobilenet_v2_1.0_224-tf	.203
268	resnet-50_v1-tf	.203
269	mobilenet_v2_0.75_224-tf	.202
270	squeezenet1_1	.198
271	mobilenet_v2_1_3_224	.196
272	resnet-152_v2_pytorch	.192
273	xception-keras	.192
274	mobilenet_v1_0.75_192	.191
275	ReAlnet02	.190
276	ReAlnet05	.188
277	ReAlnet03	.188
278	efficientnet_b1_imagenet_full	.187
279	ReAlnet04	.185
280	ReAlnet06	.183
281	ReAlnet08	.183
282	ReAlnet10	.181
283	CORnet-S	.181
284	vgg-19-keras	.180
285	efficientnet_b2_imagenet_full	.180
286	resnet_50_v1_spiking_l4	.179
287	ReAlnet07	.178
288	ReAlnet01	.176
289	ReAlnet09	.176
290	mobilenet_v2_1.3_224-tf	.175
291	mobilenet_v2_1.4_224-tf	.175
292	mobilenet_v1_0.75_224	.173
293	resnet_50_v1_spiking_l4a	.166
294	mobilenet_v1_0.5_224	.162
295	mobilenet_v1_1.0_224	.158
296	vgg-16-keras	.155
297	squeezenet1_0	.126
298	vgg_19	.087
299	vgg_16	.079
300	0.5x_resnet-18_LC_w_sh_10_iter
301	BiT-M-R50x3
302	BiT-M-R101x3
303	BiT-M-R50x1
304	BiT-M-R152x4
305	BiT-M-R101x1
306	BiT-M-R152x2
307	FrankRobWobv0
308	omnivore_swinT
309	ViT-B/32
310	0.5x_resnet-18
311	resnet-34
312	0.5x_resnet-18_LC_w_sh_100_iter
313	RN50
314	r3m_resnet18
315	CORnet-Z
316	0.5x_resnet-18_LC
317	vggface
318	r3m_resnet50
319	r3m_resnet34
320	resnet18-supervised
321	resnet18-local_aggregation
322	ViT_B_16_imagenet1k
323	vision_transformer_vit_large_patch16_224
324	CLIP_ViT-B_32
325	SWSL_resnext101_32x8d
326	CLIP_resnet50
327	CLIP_resnet50_float32
328	resnet101
329	resnet50
330	texture_shape_resnet50_trained_on_SIN
331	cornet_s
332	vonealexnet_gaussian_noise_std4_fixed
333	texture_shape_alexnet_trained_on_SIN
334	resnet18-contrastive_multiview
335	resnet18-colorization
336	CORnetZ_CIFAR10
337	resnet18-deepcluster
338	v1-pyr-nodown
339	my-model
340	resnet18-relative_position
341	resnet18-simclr
342	resnet18-depth_prediction
343	ViT_B_32_imagenet1k
344	CORnetZ_CIFAR10_bs32_20_04
345	dorinet_cornet_z
346	ViT_L_16_imagenet1k
347	resnet18-autoencoder
348	ViT_L_32
349	ViT_B_32
350	ViT_B_16
351	fitvid_trained_on_physion
352	r3m_resnet34_nocrop
353	dcgan
354	pixels
355	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
356	yudixie_resnet50_imagenet1kpret_0_240312
357	yudixie_resnet50_category_class_0_240908
358	yudixie_resnet50_distance_reg_0_240908
359	resnet18_imagenet21kP
360	briaai_rmbg_1_4
361	ConvLSTM
362	PredRNN
363	SimVP
364	TAU
365	yolos_tiny
366	MIM
367	hmax
368	inception_v3_pytorch
369	ReAlnet01_cornet
370	mobilevit_small
371	ReAlnet02_cornet
372	yudixie_resnet18_translation_reg_0_240719
373	yudixie_resnet18_distance_reg_0_240719
374	yudixie_resnet18_rotation_reg_0_240719
375	yudixie_resnet18_imagenet1kpret_0_240719
376	compact_resnet50_V4
377	ReAlnet03_cornet
378	yudixie_resnet50_distance_rotation_0_240908
379	vit_base_patch16_dinov3-lvd1689m
380	yudixie_resnet50_rotation_reg_0_240908
381	yudixie_resnet50_distance_translation_0_240908
382	ReAlnet04_cornet
383	resnet18_ecoset_full
384	compact_resnet50_IT
385	VOneCORnet-S
386	resnet34_ecoset_full
387	resnet50_ecoset_full
388	vit_base_patch14_dinov2-lvd142m
389	resnet101_ecoset_full
390	ReAlnet01_cornet_fov4
391	resnet152_ecoset_full
392	yudixie_resnet18_distance_translation_0_240719
393	compact_vgg19_V4
394	ReAlnet05_cornet
395	yudixie_resnet18_translation_rotation_0_240719
396	yudixie_resnet18_distance_rotation_0_240719
397	resnet-18_test_m
398	yudixie_resnet18_category_class_0_240719
399	yudixie_resnet18_random_0_240719
400	yudixie_resnet50_translation_reg_0_240908
401	yudixie_resnet18_cat_obj_class_all_latents_0_240719
402	yudixie_resnet18_distance_translation_rotation_0_240719
403	ReAlnet06_cornet
404	yudixie_resnet18_object_class_0_240719
405	ReAlnet01_cornet_fov12
406	ReAlnet01_cornet_fov16
407	compact_resnet50_robust_V4
408	ReAlnet07_cornet
409	yudixie_resnet50_translation_rotation_0_240908
410	ReAlnet08_cornet
411	resnet50-vicreg
412	omnivore_swinB
413	ReAlnet02_ALL
414	ReAlnet04_ALL
415	compact_cornets_V4
416	ReAlnet01_ALL
417	ReAlnet09_cornet
418	ReAlnet03_ALL
419	compact_cornets_IT
420	omnivore_swinS
421	ReAlnet05_ALL
422	ReAlnet10_cornet
423	compact_vgg19_IT
424	ReAlnet06_ALL
425	compact_resnet50_robust_IT
426	yudixie_resnet50_distance_translation_rotation_0_240908
427	yudixie_resnet50_cat_obj_class_all_latents_0_240908
428	yudixie_resnet50_imagenet1kpret_0_240908
429	yudixie_resnet50_random_0_240908
430	ReAlnet07_ALL
431	yudixie_resnet50_object_class_0_240908
432	ReAlnet08_ALL
433	ReAlnet09_ALL
434	resnet50_primary_visual_cortex
435	ReAlnet10_ALL
436	vit_base_patch14_reg4_dinov2-lvd142m
437	resnet18_fair
438	pr1_stl10_disentangled_alpha04
439	predictive_coding_pc
440	yudixie_wbh_object_class_scaling_0415_run_0
441	yudixie_wbh_object_class_scaling_0415_run_3
442	yudixie_wbh_object_class_scaling_0415_imagenet
443	yudixie_wbh_object_class_scaling_0415_run_2
444	yudixie_wbh_object_class_scaling_0415_run_1
445	deepeisnn_ei_cifar10_vgg19
446	deepeisnn_ei_cifar10_resnet18
447	deepeisnn_ei_imagenet_resnet18
448	dinov2_vitl14_lc_res336
449	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_bias