TransDA
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Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”
Official implementation for TransDA
Official pytorch implement for “Transformer-Based Source-Free Domain Adaptation”.
Overview:
![](https://github.com/ygjwd12345/TransDA/raw/main/image/overview.png)
Result:
![](https://github.com/ygjwd12345/TransDA/raw/main/image/result_office31.png)
![](https://github.com/ygjwd12345/TransDA/raw/main/image/result_officehome.png)
Prerequisites:
- python == 3.6.8
- pytorch ==1.1.0
- torchvision == 0.3.0
- numpy, scipy, sklearn, PIL, argparse, tqdm
Prepare pretrain model
We choose R50-ViT-B_16 as our encoder.
wget https://storage.googleapis.com/vit_models/imagenet21k/R50+ViT-B_16.npz
mkdir ./model/vit_checkpoint/imagenet21k
mv R50+ViT-B_16.npz ./model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz
Our checkpoints could be find in Dropbox
Dataset:
- Please manually download the datasets Office, Office-Home, VisDA, Office-Caltech from the official websites, and modify the path of images in each '.txt' under the folder './data/'.
- The script "download_visda2017.sh" in data fold also can use to download visda
Training
Office-31
```python
sh run_office_uda.sh
```
Office-Home
```python
sh run_office_home_uda.sh
```
Office-VisDA
```python
sh run_visda.sh
```