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“Type” is the specific attention types. The ViTAE-S adopts |
the performer of T2T-ViT [63] in the first two reduction cells. |
“L” means this reduction cell does not use PCM and attention. |
“F” and “W” separately denote the MHSA and WMHSA in |
ViT and Swin. At last, the “Depth” is the number of stacked |
normal cells, which is also the Niin Figure 2. |
C. Implementation |
1) Determine the Pretraining Network: We first determine |
the type of deep models to be used for RSP. To this end, from |
the official training set, we construct a mini-training set and a |
mini-evaluation set, which have 9,775 and 225 images, respec- |
tively. Note the latter set is formed by randomly selecting 5 |
images from each category to balance the classes. For CNN, |
the classic ResNet-50 [12] is employed. Since this research |
mainly explores the performance of vision transformer models |
with RSP, a series of typical vision transformer based networks |
including DeiT-S [45], PVT-S [46], and Swin-T [13], are also |
evaluated. The selection of a specific version is to guarantee |
these models have a similar amount of parameters compared |
with the ViTAE-S model. In addition, we also include ViT-B |
[44] for reference since ViT is the most basic model of vision |
transformers. |
All models are trained with 300 epochs and batch size 16. |
We adopt the AdamW optimizer where the momentum is set |
to 0.9 and the weight decay is set to 0.05. The initial learning |
rate is 1e-3, which is adjusted by the cosine scheduling |
policy:currentlr=minlr+1 |
2(initiallr−minlr)(1 + |
cos(iter |
max iterπ)), whereminlris 5e-6. In addition, we set |
the warming up epochs to 5, where the learning rate is set |
to 5e-7. Following the typical IMP, the input image is resized |
to 224 ×224 by randomly cropping during training, while |
during testing, the image at the same size is obtained through |
“center crop”. In addition, a series of data argumentations |
including AutoAugment [65], Random Erasing [66], Mixup |
[67], CutMix [68], and color jitter are applied to improve the |
training performance. The top-1 accuracy and top-5 accuracy |
are used as the evaluation metrics. In addition, all models are |
implemented on a single NVIDIA Tesla V100 GPU, and the |
results are shown in Table II. |
6 JOURNAL OF L ATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 |
TABLE II |
RESULTS OF DIFFERENT MODELS ON THE MINI -EVALUATION SET . THEY ARE TRAINED ON THE MINI -TRAINING SET FROM MILLION AID. |
Model Acc@1 Acc@5 Param(M) Training Time (hh:mm:ss) |
ResNet-50 [12] 80.8 96.1 23.6 16:42:58 |
DeiT-S [45] 72.9 92.9 21.7 16:57:39 |
ViT-B [44] 78.4 92.1 114.4 17:41:03 |
PVT-S [46] 80.0 94.1 23.9 18:06:42 |
Swin-T [13] 84.7 93.7 27.6 17:49:06 |
ViTAE-S [14] 85.9 96.9 22.8 24:00:35 |
ViTAEv2-S [29] 88.2 96.1 18.8 23:57:30 |
It can be seen that despite the ViT-B has the most param- |
eters, it performs not better than the classic ResNet-50. The |
DeiT-S performs the worst since we do not adopt the teacher |
model. Because our task is pretraining using RS images, |
obtaining the corresponding teacher model is our target instead |
of the prerequisite. By introducing the design paradigm of the |
feature pyramid, PVT-S improves the accuracy compared with |
ViT-B. On this foundation, the original ViTAE-S further con- |
siders the locality and scale-invariance modeling, which are the |
inductive biases of conventional CNN models. However, it cost |
much training time since the token number in the early RCs is |
large, requiring more computations. The Swin-T addresses this |
issue by restricting the MHSA in fixed windows and adopts |
the image shifting to implicitly promote the communications |
between windows. By taking the advantage of WMHSA, the |
ViTAEv2-S achieves the best performance and it exceeds the |
second place by 2.3% top-1 accuracy. |
The procedure of model determination is shown as follows. |
For the ViTAE models, we choose the strongest one to expect |
good performance in downstream tasks such as the aerial |
scene recognition when adopting RSP, i.e., the ViTAEv2-S. |
For comparison, the ResNet-50 is selected as the representative |
network in conventional CNN, and the RS pretrained ResNet- |
50 can also provide a group of new CNN related baselines on a |
series of aerial datasets. The DeiT-S and ViT-B are eliminated |
because of the low accuracy and a large number of parameters, |
and they are difficult to be transferred into downstream tasks |
because of the design of stacking transformers. The Swin |
can be regarded as building on the foundation of PVT by |
substituting the global MHSA with the shiftable WMHSA. |
Since the top-1 accuracy of Swin is larger than PVT, and |
Swin-T requires less training time, we also choose Swin-T |
in the subsequent experiments. |
2) Obtain the Suitable Weights: After determining the |
model candidates, we conduct the RSP to obtain the pretrained |
weights. Concretely, maintaining the category balance, we |
randomly choose 1,000 images in each category of the Million- |
AID dataset to form the validation set that has 51,000 images, |
achieving a similar volume compared with the ImageNet |
validation set, which contains 50,000 images. The remaining |
949,848 images are used for training. Although the number |
of images and categories for RSP are less than those of the |
ImageNet training set, it still can perform to be competitive |
or even achieves SOTA results on aerial scene tasks, whose |
details will be presented later. |
To obtain suitable pretraining weights, we separately train |
the ViTAEv2-S model under the configuration of different |
epochs. The basic learning rate is 5e-4, and the batch sizeTABLE III |
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