7.4 Dense Recognition Tasks

We probe the quality of patch-level features extracted from our network on several dense downstream tasks.
We consider semantic image segmentation and monocular depth estimation in several settings and we conduct
evaluations on multiple datasets for each.

13

Arch

Feature
OpenCLIP ViT-G/14
ViT-H/14
MAE
ViT-B/8
DINO
ViT-L/16
iBOT
ViT-S/14
ViT-B/14
ViT-L/14
ViT-g/14

DINOv2

Oxford
H
M
50.7
19.7
2.2
11.7
13.7
40.1
12.7
39.0
68.8
43.2
49.5
72.9
54.0
75.1
73.6
52.3

Paris

Met

AmsterTime

M
79.2
19.9
65.3
70.7
84.6
90.3
92.7
92.1

H
60.2
4.7
35.3
47.0
68.5
78.6
83.5
82.6

GAP GAP- ACC
6.5
34.4
30.5
7.5
43.9
17.1
58.2
25.1
29.4
57.7
66.1
36.7
40.0
71.6
76.5
36.8

23.9
23.5
37.7
54.8
54.3
63.5
68.9
73.6

mAP
24.6
4.2
24.6
26.7
43.5
45.6
50.0
46.7

Table 9: Evaluation of frozen features on instance-level recognition. We consider 4 diﬀerent bench-
marks and report their main metrics.

Arch.
DINOv2- Learning Robust Visual Features without Supervision