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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	AdvProp_efficientnet-b4	.680
15	AT_efficientnet-b4	.680
16	alexnet_l2_3_robust	.672
17	resnet50_linf_4_robust	.656
18	alexnet_training_seed_07	.656
19	alexnet_robust_correct	.654
20	resnet-50-robust	.654
21	imagenet_l2_3_0	.654
22	resnet50_robust_l2_eps3	.654
23	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.624
24	deit_small_imagenet_10_seed-0	.606
25	voneresnet-50-robust	.604
26	deit_small_imagenet_1_seed-0	.584
27	alexnet_training_seed_09	.571
28	efficientnet-b7	.565
29	artResNet18_1	.564
30	resnet50_finetune_cutmix_AVGe2e3_robust_linf8255_e0_247x234	.564
31	resnet50_finetune_cutmix_e3_robust_linf8255_e0_247x234	.563
32	resnet50-barlow	.558
33	effnetb1_cutmixpatch_augmix_robust32_e5_324x288	.542
34	alexnet_training_seed_06	.538
35	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.537
36	convnext_small_imagenet_10_seed-0	.532
37	mobilenet_v2_0.5_96	.521
38	ViT_L_32_imagenet1k	.513
39	barlow-twins-resnet50	.511
40	resnet50-meshes-lt-100-original-pretrained	.510
41	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.508
42	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.505
43	alexnet_training_seed_08	.503
44	resnet50-meshes-lt-100-original-scratch	.500
45	alexnet_training_seed_05	.500
46	alexnet_training_seed_02	.500
47	alexnet_training_seed_04	.500
48	alexnet_training_seed_10	.500
49	alexnet_training_seed_01	.500
50	alexnet_training_seed_01_fov4	.500
51	alexnet_training_seed_01_fov12	.500
52	alexnet_training_seed_01_fov16	.500
53	alexnet_training_seed_03	.500
54	resnet50-simclr-vissl	.497
55	resnet50_robust_l2_eps1	.485
56	my-model	.478
57	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.474
58	mobilenet_v2_0.35_128	.456
59	custom_model_cv_18_dagger_408	.453
60	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.453
61	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.453
62	resnext101_32x48d_wsl	.442
63	effnetb1_cutmixrespatch_SAM_robust32_e2_manylayers_324x288	.437
64	vit_large_patch14_clip_224:openai_ft_in1k	.432
65	effnetb1_cutmixrespatch_SAM_robust32_e10_manylayers_324x288	.430
66	resnet50-vicregl0p9	.429
67	resnext101_32x16d_wsl	.426
68	vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k	.420
69	resnet50_imagenet_1_seed-0	.419
70	mobilenet_v2_0.5_128	.416
71	convnext_xlarge:fb_in22k_ft_in1k	.412
72	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.412
73	vit_large_patch14_clip_224:laion2b_ft_in1k	.410
74	resnext101_32x32d_wsl	.408
75	mobilenet_v2_0.75_96	.406
76	mobilenet_v2_1.0_96	.405
77	convnext_large:fb_in22k_ft_in1k	.401
78	vit_relpos_base_patch16_clsgap_224:sw_in1k	.399
79	convnext_small_imagenet_1_seed-0	.396
80	voneresnet-50	.394
81	mobilenet_v1_0.25_128	.393
82	blt_vs	.391
83	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.388
84	vit_large_patch14_clip_336:laion2b_ft_in1k	.388
85	AdvProp_efficientnet-b2	.387
86	AT_efficientnet-b2	.387
87	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.386
88	deit_large_imagenet_full_seed-0	.385
89	mobilenet_v2_1_0_128	.372
90	resnext101_32x8d_wsl	.370
91	convnext_large_imagenet_full_seed-0	.368
92	deit_base_imagenet_full_seed-0	.368
93	mobilenet_v2_1.0_128-tf	.363
94	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.360
95	effnetv2m_custom384	.357
96	pnasnet_large_pytorch	.355
97	cvt_cvt-w24-384-in22k_finetuned-in1k_4	.350
98	alexnet_reduced_aliasing_early_checkpoint	.350
99	mobilenet_v2_0.75_128	.349
100	mobilenet_v2_0.35_160	.349
101	fixres_resnext101_32x48d_wsl	.348
102	resnet50-SIN_IN	.347
103	vit_base_patch16_clip_224:openai_ft_in1k	.345
104	convnext_small_imagenet_full_seed-0	.340
105	mobilenet_v2_0.5_160	.339
106	mobilenet_v2_0_75_160	.338
107	mobilenet_v2_0.35_192	.337
108	resnet-152_v2-tf	.336
109	vonegrcnn_52e_full	.336
110	vonegrcnn_62e_nobn	.336
111	vonegrcnn_62e_nobn	.331
112	convnext_small_imagenet_100_seed-0	.330
113	convnext_base:clip_laiona_augreg_ft_in1k_384	.325
114	mobilenet_v2_0.75_160-tf	.323
115	vonegrcnn_47e	.322
116	convnext_tiny_imagenet_full_seed-0	.322
117	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.321
118	mobilenet_v2_0_5_192	.321
119	vonegrcnn_52e_full	.320
120	resnet50-vicregl0p75	.319
121	convnext_base_imagenet_full_seed-0	.318
122	vonegrcnn_47e	.318
123	mobilenet_v1_0.25_192	.315
124	AdvProp_efficientnet-b0	.314
125	deit_small_imagenet_full_seed-0	.313
126	mobilenet_v1_0.75_128	.312
127	mobilenet_v1_0.5_128	.311
128	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.309
129	mobilenet_v2_0.5_192-tf	.301
130	inception_v3	.297
131	inception_resnet_v2	.296
132	resnet50_moco_v2	.296
133	resnet-101_v2-tf	.293
134	convnext_tiny:in12k_ft_in1k	.292
135	resnet_101_v2	.292
136	BiT-S-R152x2	.290
137	nasnet_mobile	.289
138	inception_v1	.289
139	SWSL_resnet50	.288
140	mobilenet_v2_1_0_160	.287
141	resnet_50_v1_spiking_l3	.286
142	focalnet_tiny_lrf_in1k	.286
143	resnet50_random_linf8_perturb	.285
144	inception_v2-tf	.283
145	eBarlow_lmda_02_1000ep	.282
146	cv_18_dagger_408_pretrained	.281
147	resnet-50_v2-tf	.280
148	inception_v4-tf	.278
149	mobilenet_v2_1.0_192-tf	.278
150	antialias-resnet152	.275
151	mobilenet_v2_1-4_224_pytorch	.275
152	mobilenet_v2_1_4_224	.275
153	mobilenet_v2_1.0_160-tf	.275
154	mobilenet_v1_1.0_128	.274
155	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.272
156	effnetb1_272x240	.272
157	BiT-S-R50x1	.271
158	mobilenet_v2_1_0_192	.271
159	mobilenet_v1_0.5_160	.271
160	xception	.271
161	alexnet_early_checkpoint	.271
162	resnet50-VITO-8deg-cc	.270
163	inception_v3-tf	.270
164	resnet50_byol	.270
165	resnet50-vitoimagevidnet8	.269
166	alexnet_random_linf8_perturb	.268
167	alexnet_random_l2_3_perturb	.268
168	resnet50-moclr8deg	.266
169	mobilenet_v2_0.75_192-tf	.266
170	voneresnet-50-non_stochastic	.264
171	mobilenet_v2_0_5_224	.263
172	resnet152_imagenet_full	.263
173	resnet50_ewconplus_wzc	.261
174	densenet-201	.260
175	densenet_201_pytorch	.260
176	densenet-201-keras	.260
177	BiT-S-R101x3	.259
178	grcnn_109	.258
179	inception_v4	.258
180	BiT-S-R152x4	.256
181	resnet_101_v1	.254
182	antialiased-rnext101_32x8d	.254
183	BiT-S-R101x1	.253
184	nasnet_large-tf	.252
185	mobilenet_v2_0_75_192	.250
186	resnet101_imagenet_full	.249
187	resnet_152_v1	.245
188	mobilenet_v1_0.75_160	.243
189	cvt_cvt-13-224-in1k_4_LucyV4	.243
190	mobilenet_v2_1_0_224	.242
191	nasnet_large	.242
192	mobilenet_v2_0.35_224	.242
193	resnet-152_v1-tf	.241
194	efficientnet_b0	.240
195	alexnet_ks_torevert	.239
196	alexnet_fov4	.239
197	alexnet_fov12	.239
198	alexnet_fov16	.239
199	alexnet	.239
200	cvt_cvt-21-224-in1k_4_LucyV4	.238
201	resnet34_imagenet_full	.237
202	mobilenet_v2_0.5_224-tf	.236
203	densenet-169	.235
204	efficientnet_b0_imagenet_full	.233
205	mobilenet_v1_0.25_224	.233
206	densenet-169-keras	.232
207	0.5x_resnet-18_LC_w_sh_1_iter	.231
208	deit_small_imagenet_100_seed-0	.231
209	BiT-S-R50x3	.230
210	resnet-101_v1-tf	.230
211	inception_v1-tf	.228
212	resnet_152_v2	.226
213	cvt_cvt-13-384-in1k_4_LucyV4	.226
214	shufflenet_v2_x1_0	.226
215	grcnn	.226
216	regnet_y_400mf	.224
217	resnet_50_v1_spiking	.223
218	resnet_50_v2	.222
219	mobilenet_v2_0_75_224	.221
220	resnet50_imagenet_100_seed-0	.221
221	pnasnet_large-tf	.220
222	nasnet_mobile-tf	.220
223	pnasnet_large_halfPrecision	.219
224	pnasnet_large	.219
225	resnet50_simclr	.217
226	resnet50_imagenet_full	.217
227	convnext_tiny_sup	.217
228	resnet18_imagenet_full	.215
229	resnet50-sup	.214
230	resnet-50-pytorch	.214
231	resnet50_tutorial	.214
232	resnet_50_v1	.214
233	antialiased-r50	.214
234	mobilenet_v1_1.0_160	.214
235	convnext_femto_ols:d1_in1k	.213
236	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.213
237	mobilenet_v1_1.0_192	.212
238	CLIP-RN50	.211
239	resnet-18	.211
240	cvt_cvt-21-384-in1k_4_LucyV4	.211
241	resnet50_random_l2_perturb	.207
242	densenet-121	.206
243	densenet-121-keras	.206
244	resnet_SIN_IN_FT_IN	.205
245	resnet50-SIN_IN_IN	.205
246	Res2Net50_26w_4s	.205
247	resnet50_imagenet_10_seed-0	.204
248	mobilenet_v2_1.0_224-tf	.203
249	resnet-50_v1-tf	.203
250	mobilenet_v2_0.75_224-tf	.202
251	squeezenet1_1	.198
252	mobilenet_v2_1_3_224	.196
253	resnet-152_v2_pytorch	.192
254	xception-keras	.192
255	mobilenet_v1_0.75_192	.191
256	ReAlnet02	.190
257	ReAlnet05	.188
258	ReAlnet03	.188
259	efficientnet_b1_imagenet_full	.187
260	ReAlnet04	.185
261	ReAlnet06	.183
262	ReAlnet08	.183
263	ReAlnet10	.181
264	CORnet-S	.181
265	vgg-19-keras	.180
266	efficientnet_b2_imagenet_full	.180
267	resnet_50_v1_spiking_l4	.179
268	ReAlnet07	.178
269	ReAlnet01	.176
270	ReAlnet09	.176
271	mobilenet_v2_1.3_224-tf	.175
272	mobilenet_v2_1.4_224-tf	.175
273	mobilenet_v1_0.75_224	.173
274	resnet_50_v1_spiking_l4a	.166
275	mobilenet_v1_0.5_224	.162
276	mobilenet_v1_1.0_224	.158
277	vgg-16-keras	.155
278	squeezenet1_0	.126
279	vgg_19	.087
280	vgg_16	.079
281	0.5x_resnet-18_LC_w_sh_10_iter
282	BiT-M-R50x3
283	BiT-M-R101x3
284	BiT-M-R50x1
285	BiT-M-R152x4
286	BiT-M-R101x1
287	BiT-M-R152x2
288	resnet50
289	FrankRobWobv0
290	omnivore_swinT
291	ViT-B/32
292	0.5x_resnet-18
293	resnet-34
294	0.5x_resnet-18_LC_w_sh_100_iter
295	RN50
296	r3m_resnet18
297	CORnet-Z
298	0.5x_resnet-18_LC
299	vggface
300	r3m_resnet50
301	r3m_resnet34
302	resnet18-supervised
303	resnet18-local_aggregation
304	ViT_B_16_imagenet1k
305	vision_transformer_vit_large_patch16_224
306	CLIP_ViT-B_32
307	SWSL_resnext101_32x8d
308	CLIP_resnet50
309	CLIP_resnet50_float32
310	resnet101
311	resnet50
312	texture_shape_resnet50_trained_on_SIN
313	cornet_s
314	vonealexnet_gaussian_noise_std4_fixed
315	texture_shape_alexnet_trained_on_SIN
316	resnet18-contrastive_multiview
317	resnet18-colorization
318	CORnetZ_CIFAR10
319	resnet18-deepcluster
320	v1-pyr-nodown
321	my-model
322	resnet18-relative_position
323	resnet18-simclr
324	resnet18-depth_prediction
325	ViT_B_32_imagenet1k
326	CORnetZ_CIFAR10_bs32_20_04
327	dorinet_cornet_z
328	ViT_L_16_imagenet1k
329	resnet18-autoencoder
330	ViT_L_32
331	ViT_B_32
332	ViT_B_16
333	fitvid_trained_on_physion
334	r3m_resnet34_nocrop
335	dcgan
336	pixels
337	fulltest_microblockvf_nobottleneck_freeat_nopretrain_eps2_m4
338	yudixie_resnet50_imagenet1kpret_0_240312
339	yudixie_resnet50_category_class_0_240908
340	yudixie_resnet50_distance_reg_0_240908
341	resnet18_imagenet21kP
342	briaai_rmbg_1_4
343	ConvLSTM
344	PredRNN
345	SimVP
346	TAU
347	yolos_tiny
348	MIM
349	hmax
350	inception_v3_pytorch
351	ReAlnet01_cornet
352	mobilevit_small
353	ReAlnet02_cornet
354	yudixie_resnet18_translation_reg_0_240719
355	yudixie_resnet18_distance_reg_0_240719
356	yudixie_resnet18_rotation_reg_0_240719
357	yudixie_resnet18_imagenet1kpret_0_240719
358	compact_resnet50_V4
359	ReAlnet03_cornet
360	yudixie_resnet50_distance_rotation_0_240908
361	vit_base_patch16_dinov3-lvd1689m
362	yudixie_resnet50_rotation_reg_0_240908
363	yudixie_resnet50_distance_translation_0_240908
364	ReAlnet04_cornet
365	resnet18_ecoset_full
366	compact_resnet50_IT
367	VOneCORnet-S
368	resnet34_ecoset_full
369	resnet50_ecoset_full
370	vit_base_patch14_dinov2-lvd142m
371	resnet101_ecoset_full
372	ReAlnet01_cornet_fov4
373	resnet152_ecoset_full
374	yudixie_resnet18_distance_translation_0_240719
375	compact_vgg19_V4
376	ReAlnet05_cornet
377	yudixie_resnet18_translation_rotation_0_240719
378	yudixie_resnet18_distance_rotation_0_240719
379	resnet-18_test_m
380	yudixie_resnet18_category_class_0_240719
381	aughost_fair_resnet18
382	yudixie_resnet18_random_0_240719
383	yudixie_resnet50_translation_reg_0_240908
384	yudixie_resnet18_cat_obj_class_all_latents_0_240719
385	yudixie_resnet18_distance_translation_rotation_0_240719
386	ReAlnet06_cornet
387	yudixie_resnet18_object_class_0_240719
388	ReAlnet01_cornet_fov12
389	ReAlnet01_cornet_fov16
390	compact_resnet50_robust_V4
391	ReAlnet07_cornet
392	yudixie_resnet50_translation_rotation_0_240908
393	ReAlnet08_cornet
394	resnet50-vicreg
395	omnivore_swinB
396	ReAlnet02_ALL
397	ReAlnet04_ALL
398	compact_cornets_V4
399	ReAlnet01_ALL
400	ReAlnet09_cornet
401	ReAlnet03_ALL
402	compact_cornets_IT
403	omnivore_swinS
404	ReAlnet05_ALL
405	ReAlnet10_cornet
406	compact_vgg19_IT
407	ReAlnet06_ALL
408	compact_resnet50_robust_IT
409	yudixie_resnet50_distance_translation_rotation_0_240908
410	yudixie_resnet50_cat_obj_class_all_latents_0_240908
411	yudixie_resnet50_imagenet1kpret_0_240908
412	yudixie_resnet50_random_0_240908
413	ReAlnet07_ALL
414	yudixie_resnet50_object_class_0_240908
415	ReAlnet08_ALL
416	ReAlnet09_ALL
417	resnet50_primary_visual_cortex
418	ReAlnet10_ALL
419	vit_base_patch14_reg4_dinov2-lvd142m

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