Leaderboard Explore Compare Profile Community Blog FAQ Competition 2024 Report Issue

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	ijepa_vith14_linhead	.410
81	vit_large_patch14_clip_224:laion2b_ft_in1k	.410
82	resnext101_32x32d_wsl	.408
83	mobilenet_v2_0.75_96	.406
84	mobilenet_v2_1.0_96	.405
85	convnext_large:fb_in22k_ft_in1k	.401
86	vit_relpos_base_patch16_clsgap_224:sw_in1k	.399
87	deit3_base_patch16_224_fb_in22k_ft_in1k	.399
88	convnext_small_imagenet_1_seed-0	.396
89	voneresnet-50	.394
90	mobilenet_v1_0.25_128	.393
91	blt_vs	.391
92	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.388
93	vit_large_patch14_clip_336:laion2b_ft_in1k	.388
94	AdvProp_efficientnet-b2	.387
95	AT_efficientnet-b2	.387
96	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.386
97	convnext_large_in22k_supervised	.386
98	deit_large_imagenet_full_seed-0	.385
99	mobilenet_v2_1_0_128	.372
100	resnext101_32x8d_wsl	.370
101	convnext_large_imagenet_full_seed-0	.368
102	deit_base_imagenet_full_seed-0	.368
103	mobilenet_v2_1.0_128-tf	.363
104	convnext_large_clip_laion2b_finelayers	.362
105	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.360
106	effnetv2m_custom384	.357
107	pnasnet_large_pytorch	.355
108	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.350
109	alexnet_reduced_aliasing_early_checkpoint	.350
110	mobilenet_v2_0.75_128	.349
111	mobilenet_v2_0.35_160	.349
112	fixres_resnext101_32x48d_wsl	.348
113	mehrer2020_alexnet_training_seed_05	.348
114	resnet50-SIN_IN	.347
115	vit_base_patch16_clip_224:openai_ft_in1k	.345
116	convnext_small_imagenet_full_seed-0	.340
117	mobilenet_v2_0.5_160	.339
118	mehrer2020_alexnet_training_seed_03	.338
119	mobilenet_v2_0_75_160	.338
120	mehrer2020_alexnet_training_seed_02	.337
121	mobilenet_v2_0.35_192	.337
122	resnet-152_v2-tf	.336
123	vonegrcnn_52e_full	.336
124	vonegrcnn_62e_nobn	.336
125	convnext_base_fb_in22k_ft_in1k	.333
126	mehrer2020_alexnet_training_seed_01	.332
127	mehrer2020_alexnet_training_seed_01_fov12	.332
128	mehrer2020_alexnet_training_seed_01_fov4	.332
129	mehrer2020_alexnet_training_seed_01_fov16	.332
130	vonegrcnn_62e_nobn	.331
131	convnext_small_imagenet_100_seed-0	.330
132	convnext_base:clip_laiona_augreg_ft_in1k_384	.325
133	mobilenet_v2_0.75_160-tf	.323
134	vonegrcnn_47e	.322
135	convnext_tiny_imagenet_full_seed-0	.322
136	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.321
137	mobilenet_v2_0_5_192	.321
138	vonegrcnn_52e_full	.320
139	resnet50-vicregl0p75	.319
140	convnext_base_imagenet_full_seed-0	.318
141	vonegrcnn_47e	.318
142	mobilenet_v1_0.25_192	.315
143	AdvProp_efficientnet-b0	.314
144	deit_small_imagenet_full_seed-0	.313
145	mobilenet_v1_0.75_128	.312
146	mobilenet_v1_0.5_128	.311
147	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.309
148	mehrer2020_alexnet_training_seed_04	.308
149	convnext_base_fb_in1k	.303
150	mobilenet_v2_0.5_192-tf	.301
151	convnext_base_fb_in22k_ft_in1k_gram_aligned_dinov3	.298
152	inception_v3	.297
153	inception_resnet_v2	.296
154	resnet50_moco_v2	.296
155	resnet-101_v2-tf	.293
156	convnext_tiny:in12k_ft_in1k	.292
157	resnet_101_v2	.292
158	BiT-S-R152x2	.290
159	nasnet_mobile	.289
160	inception_v1	.289
161	SWSL_resnet50	.288
162	mobilenet_v2_1_0_160	.287
163	convnext_base_fb_in1k_gram_aligned_dinov3	.287
164	resnet_50_v1_spiking_l3	.286
165	focalnet_tiny_lrf_in1k	.286
166	resnet50_random_linf8_perturb	.285
167	inception_v2-tf	.283
168	eBarlow_lmda_02_1000ep	.282
169	cv_18_dagger_408_pretrained	.281
170	resnet-50_v2-tf	.280
171	resnet50_simsiam_linhead	.279
172	regnety_032	.279
173	inception_v4-tf	.278
174	mobilenet_v2_1.0_192-tf	.278
175	antialias-resnet152	.275
176	mobilenet_v2_1-4_224_pytorch	.275
177	mobilenet_v2_1_4_224	.275
178	mobilenet_v2_1.0_160-tf	.275
179	mobilenet_v1_1.0_128	.274
180	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.272
181	effnetb1_272x240	.272
182	BiT-S-R50x1	.271
183	mobilenet_v2_1_0_192	.271
184	mobilenet_v1_0.5_160	.271
185	xception	.271
186	alexnet_early_checkpoint	.271
187	resnet50-VITO-8deg-cc	.270
188	inception_v3-tf	.270
189	resnet50_byol	.270
190	resnet50-vitoimagevidnet8	.269
191	resnet50_barlowtwins_linhead	.268
192	alexnet_random_linf8_perturb	.268
193	alexnet_random_l2_3_perturb	.268
194	resnet50-moclr8deg	.266
195	mobilenet_v2_0.75_192-tf	.266
196	voneresnet-50-non_stochastic	.264
197	mobilenet_v2_0_5_224	.263
198	resnet152_imagenet_full	.263
199	resnet50_st_wzc_2	.263
200	resnet50_ewconplus_wzc	.261
201	seresnext50_32x4d	.260
202	densenet-201	.260
203	densenet_201_pytorch	.260
204	densenet-201-keras	.260
205	resnet50_st_wzc_seed6_2_323	.259
206	BiT-S-R101x3	.259
207	grcnn_109	.258
208	inception_v4	.258
209	BiT-S-R152x4	.256
210	resnet_101_v1	.254
211	antialiased-rnext101_32x8d	.254
212	BiT-S-R101x1	.253
213	nasnet_large-tf	.252
214	mobilenet_v2_0_75_192	.250
215	resnet101_imagenet_full	.249
216	resnet_152_v1	.245
217	mobilenet_v1_0.75_160	.243
218	cvt_cvt-13-224-in1k_4_LucyV4	.243
219	mobilenet_v2_1_0_224	.242
220	nasnet_large	.242
221	mobilenet_v2_0.35_224	.242
222	resnet50_vicreg_linhead	.241
223	resnet-152_v1-tf	.241
224	efficientnet_b0	.240
225	alexnet_ks_torevert	.239
226	alexnet_fov4	.239
227	alexnet_fov12	.239
228	alexnet_fov16	.239
229	alexnet	.239
230	cvt_cvt-21-224-in1k_4_LucyV4	.238
231	resnet50_hvs_wzc	.238
232	resnet34_imagenet_full	.237
233	resnet50_ewconplus_wzc_seed2_2__323	.236
234	mobilenet_v2_0.5_224-tf	.236
235	densenet-169	.235
236	efficientnet_b0_imagenet_full	.233
237	mobilenet_v1_0.25_224	.233
238	densenet-169-keras	.232
239	0.5x_resnet-18_LC_w_sh_1_iter	.231
240	deit_small_imagenet_100_seed-0	.231
241	BiT-S-R50x3	.230
242	resnet-101_v1-tf	.230
243	inception_v1-tf	.228
244	eicircuit_resnet18_bn_20260919	.227
245	resnet50_st_wzc_seed3_2_323	.227
246	resnet_152_v2	.226
247	cvt_cvt-13-384-in1k_4_LucyV4	.226
248	shufflenet_v2_x1_0	.226
249	grcnn	.226
250	regnet_y_400mf	.224
251	resnet_50_v1_spiking	.223
252	resnet_50_v2	.222
253	mobilenet_v2_0_75_224	.221
254	resnet50_imagenet_100_seed-0	.221
255	resnet50_byol_linhead	.221
256	pnasnet_large-tf	.220
257	nasnet_mobile-tf	.220
258	pnasnet_large_halfPrecision	.219
259	pnasnet_large	.219
260	resnet50_simclr	.217
261	resnet50_imagenet_full	.217
262	convnext_tiny_sup	.217
263	resnet18_imagenet_full	.215
264	resnet50-sup	.214
265	resnet-50-pytorch	.214
266	resnet50_tutorial	.214
267	resnet_50_v1	.214
268	antialiased-r50	.214
269	mobilenet_v1_1.0_160	.214
270	convnext_femto_ols:d1_in1k	.213
271	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.213
272	mobilenet_v1_1.0_192	.212
273	CLIP-RN50	.211
274	resnet-18	.211
275	cvt_cvt-21-384-in1k_4_LucyV4	.211
276	resnet50_random_l2_perturb	.207
277	densenet-121	.206
278	densenet-121-keras	.206
279	resnet50_simclr_linhead_01norm	.206
280	resnet_SIN_IN_FT_IN	.205
281	resnet50-SIN_IN_IN	.205
282	Res2Net50_26w_4s	.205
283	resnet50_imagenet_10_seed-0	.204
284	mobilenet_v2_1.0_224-tf	.203
285	resnet-50_v1-tf	.203
286	mobilenet_v2_0.75_224-tf	.202
287	squeezenet1_1	.198
288	mobilenet_v2_1_3_224	.196
289	resnet-152_v2_pytorch	.192
290	xception-keras	.192
291	mobilenet_v1_0.75_192	.191
292	ReAlnet02	.190
293	ReAlnet05	.188
294	ReAlnet03	.188
295	efficientnet_b1_imagenet_full	.187
296	ReAlnet04	.185
297	ReAlnet06	.183
298	ReAlnet08	.183
299	ReAlnet10	.181
300	CORnet-S	.181
301	vgg-19-keras	.180
302	efficientnet_b2_imagenet_full	.180
303	resnet_50_v1_spiking_l4	.179
304	ReAlnet07	.178
305	ReAlnet01	.176
306	ReAlnet09	.176
307	mobilenet_v2_1.3_224-tf	.175
308	mobilenet_v2_1.4_224-tf	.175
309	mobilenet_v1_0.75_224	.173
310	resnet_50_v1_spiking_l4a	.166
311	mobilenet_v1_0.5_224	.162
312	mobilenet_v1_1.0_224	.158
313	vgg-16-keras	.155
314	squeezenet1_0	.126
315	vgg_19	.087
316	vgg_16	.079
317	0.5x_resnet-18_LC_w_sh_10_iter
318	BiT-M-R50x3
319	BiT-M-R101x3
320	BiT-M-R50x1
321	BiT-M-R152x4
322	BiT-M-R101x1
323	BiT-M-R152x2
324	FrankRobWobv0
325	omnivore_swinT
326	ViT-B/32
327	0.5x_resnet-18
328	resnet-34
329	0.5x_resnet-18_LC_w_sh_100_iter
330	RN50
331	r3m_resnet18
332	CORnet-Z
333	0.5x_resnet-18_LC
334	vggface
335	r3m_resnet50
336	r3m_resnet34
337	resnet18-supervised
338	resnet18-local_aggregation
339	ViT_B_16_imagenet1k
340	vision_transformer_vit_large_patch16_224
341	CLIP_ViT-B_32
342	SWSL_resnext101_32x8d
343	CLIP_resnet50
344	CLIP_resnet50_float32
345	resnet101
346	resnet50
347	texture_shape_resnet50_trained_on_SIN
348	cornet_s
349	vonealexnet_gaussian_noise_std4_fixed
350	texture_shape_alexnet_trained_on_SIN
351	resnet18-contrastive_multiview
352	resnet18-colorization
353	CORnetZ_CIFAR10
354	resnet18-deepcluster
355	v1-pyr-nodown
356	my-model
357	resnet18-relative_position
358	resnet18-simclr
359	resnet18-depth_prediction
360	ViT_B_32_imagenet1k
361	CORnetZ_CIFAR10_bs32_20_04
362	dorinet_cornet_z
363	ViT_L_16_imagenet1k
364	resnet18-autoencoder
365	ViT_L_32
366	ViT_B_32
367	ViT_B_16
368	fitvid_trained_on_physion
369	r3m_resnet34_nocrop
370	dcgan
371	pixels
372	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
373	yudixie_resnet50_imagenet1kpret_0_240312
374	yudixie_resnet50_category_class_0_240908
375	yudixie_resnet50_distance_reg_0_240908
376	resnet18_imagenet21kP
377	briaai_rmbg_1_4
378	ConvLSTM
379	PredRNN
380	SimVP
381	TAU
382	yolos_tiny
383	MIM
384	hmax
385	inception_v3_pytorch
386	mobilevit_small
387	ReAlnet02_cornet
388	yudixie_resnet18_translation_reg_0_240719
389	yudixie_resnet18_distance_reg_0_240719
390	yudixie_resnet18_rotation_reg_0_240719
391	yudixie_resnet18_imagenet1kpret_0_240719
392	compact_resnet50_V4
393	ReAlnet03_cornet
394	yudixie_resnet50_distance_rotation_0_240908
395	vit_base_patch16_dinov3-lvd1689m
396	yudixie_resnet50_rotation_reg_0_240908
397	yudixie_resnet50_distance_translation_0_240908
398	ReAlnet04_cornet
399	resnet18_ecoset_full
400	compact_resnet50_IT
401	VOneCORnet-S
402	resnet34_ecoset_full
403	vit_base_patch14_dinov2-lvd142m
404	resnet101_ecoset_full
405	ReAlnet01_cornet_fov4
406	resnet152_ecoset_full
407	yudixie_resnet18_distance_translation_0_240719
408	compact_vgg19_V4
409	ReAlnet05_cornet
410	yudixie_resnet18_translation_rotation_0_240719
411	yudixie_resnet18_distance_rotation_0_240719
412	resnet-18_test_m
413	yudixie_resnet18_category_class_0_240719
414	yudixie_resnet18_random_0_240719
415	yudixie_resnet50_translation_reg_0_240908
416	yudixie_resnet18_cat_obj_class_all_latents_0_240719
417	yudixie_resnet18_distance_translation_rotation_0_240719
418	ReAlnet06_cornet
419	yudixie_resnet18_object_class_0_240719
420	ReAlnet01_cornet_fov12
421	ReAlnet01_cornet_fov16
422	compact_resnet50_robust_V4
423	ReAlnet07_cornet
424	yudixie_resnet50_translation_rotation_0_240908
425	ReAlnet08_cornet
426	resnet50-vicreg
427	omnivore_swinB
428	ReAlnet02_ALL
429	ReAlnet04_ALL
430	compact_cornets_V4
431	ReAlnet01_ALL
432	ReAlnet09_cornet
433	ReAlnet03_ALL
434	compact_cornets_IT
435	omnivore_swinS
436	ReAlnet05_ALL
437	ReAlnet10_cornet
438	compact_vgg19_IT
439	ReAlnet06_ALL
440	compact_resnet50_robust_IT
441	yudixie_resnet50_distance_translation_rotation_0_240908
442	yudixie_resnet50_cat_obj_class_all_latents_0_240908
443	yudixie_resnet50_imagenet1kpret_0_240908
444	yudixie_resnet50_random_0_240908
445	ReAlnet07_ALL
446	yudixie_resnet50_object_class_0_240908
447	ReAlnet08_ALL
448	ReAlnet09_ALL
449	resnet50_primary_visual_cortex
450	ReAlnet10_ALL
451	vit_base_patch14_reg4_dinov2-lvd142m
452	ReAlnet01_cornet
453	resnet18_fair
454	resnet50_ecoset_full
455	pr1_stl10_disentangled_alpha04
456	predictive_coding_pc
457	yudixie_wbh_object_class_scaling_0415_run_0
458	yudixie_wbh_object_class_scaling_0415_run_3
459	yudixie_wbh_object_class_scaling_0415_imagenet
460	yudixie_wbh_object_class_scaling_0415_run_2
461	yudixie_wbh_object_class_scaling_0415_run_1
462	deepeisnn_ei_cifar10_vgg19
463	deepeisnn_ei_cifar10_resnet18
464	deepeisnn_ei_imagenet_resnet18
465	dinov2_vitl14_lc_res336
466	dinov2_vitl14_reg_lc_featread
467	gabor_filter_entropy_4x4
468	gabor_filter_mean_abs_4x4_v2
469	gabor_filter_entropy_4x4_v2
470	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_bias