B.3 Linear probing evaluation

For linear probing we deﬁne 3 evaluation parameters: the learning rate, how many output layers we use,
whether we concatenate the average-pooled patch token features with the class token (or use only the
class token). We train our linear layer with SGD for 12500 iterations, using random-resized-crop data
augmentation, and perform the following grid search:

• learning rate in {0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.3, 0.5}
• output layers in {1, 4}
• concatenate average-pooled tokens in {yes, no}

We then report the highest accuracy value obtained on the validation set as is common practice. Note that
this grid search is not expensive, because at each iteration we perform inference on the backbone only once,
then feed the output to all linear classiﬁers (each performing a single matrix multiplication).

C List of benchmarks used for evaluations
DINOv2- Learning Robust Visual Features without Supervision