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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	eva02_large_in22k	.587
31	deit_small_imagenet_1_seed-0	.584
32	alexnet_training_seed_09	.571
33	efficientnet-b7	.565
34	artResNet18_1	.564
35	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.564
36	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.563
37	resnet50-barlow	.558
38	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.542
39	alexnet_training_seed_06	.538
40	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.537
41	convnext_small_imagenet_10_seed-0	.532
42	mobilenet_v2_0.5_96	.521
43	ViT_L_32_imagenet1k	.513
44	barlow-twins-resnet50	.511
45	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.508
46	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.505
47	alexnet_training_seed_08	.503
48	alexnet_training_seed_05	.500
49	alexnet_training_seed_02	.500
50	alexnet_training_seed_04	.500
51	alexnet_training_seed_10	.500
52	alexnet_training_seed_01	.500
53	alexnet_training_seed_01_fov4	.500
54	alexnet_training_seed_01_fov12	.500
55	alexnet_training_seed_01_fov16	.500
56	alexnet_training_seed_03	.500
57	resnet50-simclr-vissl	.497
58	resnet50_robust_l2_eps1	.485
59	my-model	.478
60	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.474
61	mobilenet_v2_0.35_128	.456
62	custom_model_cv_18_dagger_408	.453
63	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.453
64	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.453
65	vit_huge_clip336_laion	.444
66	resnext101_32x48d_wsl	.442
67	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.437
68	vit_large_patch14_clip_224:openai_ft_in1k	.432
69	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.430
70	resnet50-vicregl0p9	.429
71	resnext101_32x16d_wsl	.426
72	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.420
73	resnet50_imagenet_1_seed-0	.419
74	mobilenet_v2_0.5_128	.416
75	deit3_base_patch16_224_fb_in1k	.414
76	vit_large_clip336_openai	.413
77	convnext_xlarge:fb_in22k_ft_in1k	.412
78	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.412
79	vit_large_patch14_clip_224:laion2b_ft_in1k	.410
80	resnext101_32x32d_wsl	.408
81	mobilenet_v2_0.75_96	.406
82	mobilenet_v2_1.0_96	.405
83	convnext_large:fb_in22k_ft_in1k	.401
84	vit_relpos_base_patch16_clsgap_224:sw_in1k	.399
85	deit3_base_patch16_224_fb_in22k_ft_in1k	.399
86	convnext_small_imagenet_1_seed-0	.396
87	voneresnet-50	.394
88	mobilenet_v1_0.25_128	.393
89	blt_vs	.391
90	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.388
91	vit_large_patch14_clip_336:laion2b_ft_in1k	.388
92	AdvProp_efficientnet-b2	.387
93	AT_efficientnet-b2	.387
94	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.386
95	convnext_large_in22k_supervised	.386
96	deit_large_imagenet_full_seed-0	.385
97	mobilenet_v2_1_0_128	.372
98	resnext101_32x8d_wsl	.370
99	convnext_large_imagenet_full_seed-0	.368
100	deit_base_imagenet_full_seed-0	.368
101	mobilenet_v2_1.0_128-tf	.363
102	convnext_large_clip_laion2b_finelayers	.362
103	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.360
104	effnetv2m_custom384	.357
105	pnasnet_large_pytorch	.355
106	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.350
107	alexnet_reduced_aliasing_early_checkpoint	.350
108	mobilenet_v2_0.75_128	.349
109	mobilenet_v2_0.35_160	.349
110	fixres_resnext101_32x48d_wsl	.348
111	resnet50-SIN_IN	.347
112	vit_base_patch16_clip_224:openai_ft_in1k	.345
113	convnext_small_imagenet_full_seed-0	.340
114	mobilenet_v2_0.5_160	.339
115	mobilenet_v2_0_75_160	.338
116	mobilenet_v2_0.35_192	.337
117	resnet-152_v2-tf	.336
118	vonegrcnn_52e_full	.336
119	vonegrcnn_62e_nobn	.336
120	convnext_base_fb_in22k_ft_in1k	.333
121	vonegrcnn_62e_nobn	.331
122	convnext_small_imagenet_100_seed-0	.330
123	convnext_base:clip_laiona_augreg_ft_in1k_384	.325
124	mobilenet_v2_0.75_160-tf	.323
125	vonegrcnn_47e	.322
126	convnext_tiny_imagenet_full_seed-0	.322
127	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.321
128	mobilenet_v2_0_5_192	.321
129	vonegrcnn_52e_full	.320
130	resnet50-vicregl0p75	.319
131	convnext_base_imagenet_full_seed-0	.318
132	vonegrcnn_47e	.318
133	mobilenet_v1_0.25_192	.315
134	AdvProp_efficientnet-b0	.314
135	deit_small_imagenet_full_seed-0	.313
136	mobilenet_v1_0.75_128	.312
137	mobilenet_v1_0.5_128	.311
138	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.309
139	convnext_base_fb_in1k	.303
140	mobilenet_v2_0.5_192-tf	.301
141	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.298
142	inception_v3	.297
143	inception_resnet_v2	.296
144	resnet50_moco_v2	.296
145	resnet-101_v2-tf	.293
146	convnext_tiny:in12k_ft_in1k	.292
147	resnet_101_v2	.292
148	BiT-S-R152x2	.290
149	nasnet_mobile	.289
150	inception_v1	.289
151	SWSL_resnet50	.288
152	mobilenet_v2_1_0_160	.287
153	convnext_base_fb_in1k_gram_aligned_dinov3	.287
154	resnet_50_v1_spiking_l3	.286
155	focalnet_tiny_lrf_in1k	.286
156	resnet50_random_linf8_perturb	.285
157	inception_v2-tf	.283
158	eBarlow_lmda_02_1000ep	.282
159	cv_18_dagger_408_pretrained	.281
160	resnet-50_v2-tf	.280
161	inception_v4-tf	.278
162	mobilenet_v2_1.0_192-tf	.278
163	antialias-resnet152	.275
164	mobilenet_v2_1-4_224_pytorch	.275
165	mobilenet_v2_1_4_224	.275
166	mobilenet_v2_1.0_160-tf	.275
167	mobilenet_v1_1.0_128	.274
168	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.272
169	effnetb1_272x240	.272
170	BiT-S-R50x1	.271
171	mobilenet_v2_1_0_192	.271
172	mobilenet_v1_0.5_160	.271
173	xception	.271
174	alexnet_early_checkpoint	.271
175	resnet50-VITO-8deg-cc	.270
176	inception_v3-tf	.270
177	resnet50_byol	.270
178	resnet50-vitoimagevidnet8	.269
179	alexnet_random_linf8_perturb	.268
180	alexnet_random_l2_3_perturb	.268
181	resnet50-moclr8deg	.266
182	mobilenet_v2_0.75_192-tf	.266
183	voneresnet-50-non_stochastic	.264
184	mobilenet_v2_0_5_224	.263
185	resnet152_imagenet_full	.263
186	resnet50_st_wzc_2	.263
187	resnet50_ewconplus_wzc	.261
188	densenet-201	.260
189	densenet_201_pytorch	.260
190	densenet-201-keras	.260
191	resnet50_st_wzc_seed6_2_323	.259
192	BiT-S-R101x3	.259
193	grcnn_109	.258
194	inception_v4	.258
195	BiT-S-R152x4	.256
196	resnet_101_v1	.254
197	antialiased-rnext101_32x8d	.254
198	BiT-S-R101x1	.253
199	nasnet_large-tf	.252
200	mobilenet_v2_0_75_192	.250
201	resnet101_imagenet_full	.249
202	resnet_152_v1	.245
203	mobilenet_v1_0.75_160	.243
204	cvt_cvt-13-224-in1k_4_LucyV4	.243
205	mobilenet_v2_1_0_224	.242
206	nasnet_large	.242
207	mobilenet_v2_0.35_224	.242
208	resnet-152_v1-tf	.241
209	efficientnet_b0	.240
210	alexnet_ks_torevert	.239
211	alexnet_fov4	.239
212	alexnet_fov12	.239
213	alexnet_fov16	.239
214	alexnet	.239
215	cvt_cvt-21-224-in1k_4_LucyV4	.238
216	resnet50_hvs_wzc	.238
217	resnet34_imagenet_full	.237
218	resnet50_ewconplus_wzc_seed2_2__323	.236
219	mobilenet_v2_0.5_224-tf	.236
220	densenet-169	.235
221	efficientnet_b0_imagenet_full	.233
222	mobilenet_v1_0.25_224	.233
223	densenet-169-keras	.232
224	0.5x_resnet-18_LC_w_sh_1_iter	.231
225	deit_small_imagenet_100_seed-0	.231
226	BiT-S-R50x3	.230
227	resnet-101_v1-tf	.230
228	inception_v1-tf	.228
229	resnet50_st_wzc_seed3_2_323	.227
230	resnet_152_v2	.226
231	cvt_cvt-13-384-in1k_4_LucyV4	.226
232	shufflenet_v2_x1_0	.226
233	grcnn	.226
234	regnet_y_400mf	.224
235	resnet_50_v1_spiking	.223
236	resnet_50_v2	.222
237	mobilenet_v2_0_75_224	.221
238	resnet50_imagenet_100_seed-0	.221
239	pnasnet_large-tf	.220
240	nasnet_mobile-tf	.220
241	pnasnet_large_halfPrecision	.219
242	pnasnet_large	.219
243	resnet50_simclr	.217
244	resnet50_imagenet_full	.217
245	convnext_tiny_sup	.217
246	resnet18_imagenet_full	.215
247	resnet50-sup	.214
248	resnet-50-pytorch	.214
249	resnet50_tutorial	.214
250	resnet_50_v1	.214
251	antialiased-r50	.214
252	mobilenet_v1_1.0_160	.214
253	convnext_femto_ols:d1_in1k	.213
254	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.213
255	mobilenet_v1_1.0_192	.212
256	CLIP-RN50	.211
257	resnet-18	.211
258	cvt_cvt-21-384-in1k_4_LucyV4	.211
259	resnet50_random_l2_perturb	.207
260	densenet-121	.206
261	densenet-121-keras	.206
262	resnet_SIN_IN_FT_IN	.205
263	resnet50-SIN_IN_IN	.205
264	Res2Net50_26w_4s	.205
265	resnet50_imagenet_10_seed-0	.204
266	mobilenet_v2_1.0_224-tf	.203
267	resnet-50_v1-tf	.203
268	mobilenet_v2_0.75_224-tf	.202
269	squeezenet1_1	.198
270	mobilenet_v2_1_3_224	.196
271	resnet-152_v2_pytorch	.192
272	xception-keras	.192
273	mobilenet_v1_0.75_192	.191
274	ReAlnet02	.190
275	ReAlnet05	.188
276	ReAlnet03	.188
277	efficientnet_b1_imagenet_full	.187
278	ReAlnet04	.185
279	ReAlnet06	.183
280	ReAlnet08	.183
281	ReAlnet10	.181
282	CORnet-S	.181
283	vgg-19-keras	.180
284	efficientnet_b2_imagenet_full	.180
285	resnet_50_v1_spiking_l4	.179
286	ReAlnet07	.178
287	ReAlnet01	.176
288	ReAlnet09	.176
289	mobilenet_v2_1.3_224-tf	.175
290	mobilenet_v2_1.4_224-tf	.175
291	mobilenet_v1_0.75_224	.173
292	resnet_50_v1_spiking_l4a	.166
293	mobilenet_v1_0.5_224	.162
294	mobilenet_v1_1.0_224	.158
295	vgg-16-keras	.155
296	squeezenet1_0	.126
297	vgg_19	.087
298	vgg_16	.079
299	0.5x_resnet-18_LC_w_sh_10_iter
300	BiT-M-R50x3
301	BiT-M-R101x3
302	BiT-M-R50x1
303	BiT-M-R152x4
304	BiT-M-R101x1
305	BiT-M-R152x2
306	FrankRobWobv0
307	omnivore_swinT
308	ViT-B/32
309	0.5x_resnet-18
310	resnet-34
311	0.5x_resnet-18_LC_w_sh_100_iter
312	RN50
313	r3m_resnet18
314	CORnet-Z
315	0.5x_resnet-18_LC
316	vggface
317	r3m_resnet50
318	r3m_resnet34
319	resnet18-supervised
320	resnet18-local_aggregation
321	ViT_B_16_imagenet1k
322	vision_transformer_vit_large_patch16_224
323	CLIP_ViT-B_32
324	SWSL_resnext101_32x8d
325	CLIP_resnet50
326	CLIP_resnet50_float32
327	resnet101
328	resnet50
329	texture_shape_resnet50_trained_on_SIN
330	cornet_s
331	vonealexnet_gaussian_noise_std4_fixed
332	texture_shape_alexnet_trained_on_SIN
333	resnet18-contrastive_multiview
334	resnet18-colorization
335	CORnetZ_CIFAR10
336	resnet18-deepcluster
337	v1-pyr-nodown
338	my-model
339	resnet18-relative_position
340	resnet18-simclr
341	resnet18-depth_prediction
342	ViT_B_32_imagenet1k
343	CORnetZ_CIFAR10_bs32_20_04
344	dorinet_cornet_z
345	ViT_L_16_imagenet1k
346	resnet18-autoencoder
347	ViT_L_32
348	ViT_B_32
349	ViT_B_16
350	fitvid_trained_on_physion
351	r3m_resnet34_nocrop
352	dcgan
353	pixels
354	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
355	yudixie_resnet50_imagenet1kpret_0_240312
356	yudixie_resnet50_category_class_0_240908
357	yudixie_resnet50_distance_reg_0_240908
358	resnet18_imagenet21kP
359	briaai_rmbg_1_4
360	ConvLSTM
361	PredRNN
362	SimVP
363	TAU
364	yolos_tiny
365	MIM
366	hmax
367	inception_v3_pytorch
368	ReAlnet01_cornet
369	mobilevit_small
370	ReAlnet02_cornet
371	yudixie_resnet18_translation_reg_0_240719
372	yudixie_resnet18_distance_reg_0_240719
373	yudixie_resnet18_rotation_reg_0_240719
374	yudixie_resnet18_imagenet1kpret_0_240719
375	compact_resnet50_V4
376	ReAlnet03_cornet
377	yudixie_resnet50_distance_rotation_0_240908
378	vit_base_patch16_dinov3-lvd1689m
379	yudixie_resnet50_rotation_reg_0_240908
380	yudixie_resnet50_distance_translation_0_240908
381	ReAlnet04_cornet
382	resnet18_ecoset_full
383	compact_resnet50_IT
384	VOneCORnet-S
385	resnet34_ecoset_full
386	resnet50_ecoset_full
387	vit_base_patch14_dinov2-lvd142m
388	resnet101_ecoset_full
389	ReAlnet01_cornet_fov4
390	resnet152_ecoset_full
391	yudixie_resnet18_distance_translation_0_240719
392	compact_vgg19_V4
393	ReAlnet05_cornet
394	yudixie_resnet18_translation_rotation_0_240719
395	yudixie_resnet18_distance_rotation_0_240719
396	resnet-18_test_m
397	yudixie_resnet18_category_class_0_240719
398	aughost_fair_resnet18
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
449	dinov2_vitl14_lc_res336
450	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