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Sample stimuli

How to use

from brainscore_vision import load_benchmark
benchmark = load_benchmark("MajajHong2015public.IT-reverse_pls")
score = benchmark(my_model)

Benchmark API

Code examples

Model scores

Score Legend Min Alignment Max Alignment

Rank	Model	Score
1	compact_resnet50_robust_IT	.851
2	compact_resnet50_IT	.850
3	compact_vgg19_IT	.841
4	compact_cornets_IT	.835
5	compact_resnet50_robust_V4	.627
6	compact_resnet50_V4	.615
7	compact_vgg19_V4	.614
8	compact_cornets_V4	.613
9	pr1_stl10_disentangled_alpha04	.299
10	alexnet_training_seed_06	.272
11	alexnet_training_seed_03	.231
12	alexnet_training_seed_01	.218
13	gabor_filter_mean_abs_4x4_v2	.217
14	alexnet_training_seed_07	.216
15	predictive_coding_pc	.206
16	gabor_filter_entropy_4x4	.196
17	gabor_filter_entropy_4x4_v2	.196
18	alexnet_training_seed_10	.191
19	gabor_filter_entropy_4x4_v2_forcedCosine	.177
20	hmax	.172
21	alexnet_training_seed_09	.142
22	alexnet_training_seed_02	.131
23	resnet-50-robust	.125
24	vit_relpos_base_patch32_plus_rpn_256:sw_in1k	.117
25	custom_model_cv_18_dagger_408	.112
26	convnext_femto_ols:d1_in1k	.110
27	densenet-121	.093
28	vit_huge_patch14_clip_224:laion2b_ft_in12k_in1k	.092
29	effnetb1_cutmixpatch_SAM_robust32_avge6e8e9e10_manylayers_324x288	.090
30	efficientnet_b0	.087
31	vit_large_patch14_clip_336:openai_ft_in12k_in1k	.087
32	mobilenet_v2_1_4_224	.086
33	swin_small_patch4_window7_224:ms_in22k_ft_in1k	.085
34	vit_base_patch16_clip_224:openai_ft_in1k	.085
35	vit_large_patch14_clip_224:laion2b_ft_in1k	.084
36	vit_large_patch14_clip_224:openai_ft_in1k	.084
37	AdvProp_efficientnet-b2	.082
38	AT_efficientnet-b2	.082
39	shufflenet_v2_x1_0	.081
40	resnet_SIN_IN_FT_IN	.081
41	resnet18_fair	.080
42	convnext_base:clip_laiona_augreg_ft_in1k_384	.080
43	vit_large_patch14_clip_336:laion2b_ft_in1k	.080
44	vit_large_patch14_clip_224:openai_ft_in12k_in1k	.078
45	effnetb1_cutmix_augmix_sam_e1_5avg_424x377	.076
46	vit_tiny_r_s16_p8_384:augreg_in21k_ft_in1k	.075
47	convnext_tiny_imagenet_full_seed-0	.074
48	convnext_tiny:in12k_ft_in1k	.074
49	VOneCORnet-S	.074
50	efficientnet_b2_imagenet_full	.073
51	ViT_L_32_imagenet1k	.071
52	AdvProp_efficientnet-b4	.071
53	AdvProp_efficientnet-b6	.070
54	convnext_base_imagenet_full_seed-0	.068
55	convnext_small_imagenet_full_seed-0	.068
56	AdvProp_efficientnet-b7	.067
57	AlexNet_SIN_fov16	.066
58	AdvProp_efficientnet-b8	.066
59	convnext_large:fb_in22k_ft_in1k	.065
60	effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288	.065
61	resnet50_robust_l2_eps1	.065
62	convnext_large_mlp:clip_laion2b_augreg_ft_in1k_384	.065
63	grcnn	.065
64	AlexNet_SIN	.063
65	effnetb1_272x240	.063
66	convnext_xxlarge:clip_laion2b_soup_ft_in1k	.062
67	ReAlnet01_cornet_fov4	.061
68	ReAlnet01_cornet	.060
69	ReAlnet03_cornet	.059
70	ReAlnet04_cornet	.058
71	alexnet	.058
72	convnext_large_imagenet_full_seed-0	.057
73	convnext_tiny_sup	.054
74	cvt_cvt-21-384-in1k_4_LucyV4	.054
75	vit_relpos_base_patch16_clsgap_224:sw_in1k	.053
76	focalnet_tiny_lrf_in1k	.052
77	AlexNet_SIN_fov12	.051
78	cvt_cvt-13-384-in1k_4_LucyV4	.050
79	vit_large_patch14_clip_224:laion2b_ft_in12k_in1k	.050
80	regnety_032	.050
81	deit_small_imagenet_full_seed-0	.049
82	cvt_cvt-13-384-in22k_finetuned-in1k_4_LucyV4	.049
83	convnext_xlarge:fb_in22k_ft_in1k	.049
84	vit_base_patch16_clip_224:openai_ft_in12k_in1k	.048
85	deit_large_imagenet_full_seed-0	.048
86	cvt_cvt-21-384-in22k_finetuned-in1k_4_LucyV4	.047
87	seresnext50_32x4d	.045
88	antialiased-r50	.045
89	cvt_cvt-13-224-in1k_4_LucyV4	.043
90	deit_base_imagenet_full_seed-0	.043
91	resnet50-sup	.042
92	resnet_50_v1	.042
93	resnet_50_v1_spiking	.042
94	vit_large_clip336_openai	.041
95	voneresnet-50-non_stochastic	.040
96	resnet50-SIN	.040
97	convnext_xxlarge_clip	.040
98	cvt_cvt-21-224-in1k_4_LucyV4	.040
99	dinov2_vitl14_lc	.039
100	convnext_base_fb_in1k	.039
101	dinov2_vitl14_lc_res336	.038
102	voneresnet-50	.037
103	dinov2_vitg14_reg_lc	.035
104	resnext101_32x8d_wsl	.035
105	dinov2_vitl14_reg_lc_featread	.033
106	dinov2_vitl14_reg_lc	.033
107	resnet152_imagenet_full	.033
108	eicircuit_resnet18_bn_20260919	.031
109	dinov2_vitg14_lc	.031
110	resnext101_32x16d_wsl	.030
111	resnet50-SIN_IN	.030
112	resnext101_32x32d_wsl	.028
113	resnet50-vitoimagevidnet8	.028
114	resnet50-VITO-8deg-cc	.028
115	resnet50_ewconplus_wzc_seed2_2__323	.028
116	resnet50_ewconplus_wzc_seed6_2_323	.028
117	resnet50_ewconplus_wzc	.028
118	resnext101_32x48d_wsl	.027
119	resnet50_ewconplus_wzc_seed5_2__323	.027
120	resnet50-SIN_IN_IN	.025
121	resnet50_tutorial	.025
122	Res2Net50_26w_4s	.025
123	resnet50_st_wzc_seed3_2_323	.024
124	resnet50_st_wzc_seed7_2_323	.024
125	resnet50_st_wzc_seed6_2_323	.024
126	resnet50_imagenet_full	.024
127	resnet50-moclr8deg	.023
128	resnet_50_v2	.014
129	vit_huge_clip336_laion
130	eva02_large_in22k
131	pnasnet_large_halfPrecision
132	cvt_cvt-w24-384-in22k_finetuned-in1k_4

Benchmark bibtex

@article{muzellec_reverse_2026,
      title = {Reverse predictivity for bidirectional comparison of neural networks and biological brains},
      volume = {8},
      issn = {2522-5839},
      url = {https://doi.org/10.1038/s42256-026-01204-0},
      doi = {10.1038/s42256-026-01204-0},
      number = {3},
      journal = {Nature Machine Intelligence},
      author = {Muzellec, Sabine and Kar, Kohitij},
      month = mar,
      year = {2026},
      pages = {474--488},
}

Ceiling

0.82.

Note that scores are relative to this ceiling.

Data: MajajHong2015public.IT

Metric: reverse_pls