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Release new trained model and example outputs
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daa233 committed Jul 11, 2019
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8 changes: 2 additions & 6 deletions README.md
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Expand Up @@ -19,11 +19,7 @@ The checkpoints and logs will be saved to `checkpoints`。
## Test with the trained model
By default, it will load the latest saved model in the checkpoints. You can also use `--iter` to choose the saved models by iteration.

Trained PyTorch model: ~~[[Google Drive](https://drive.google.com/drive/folders/1FHlDG94O8sH-sOOpkJBQmxCTicr1gc-C?usp=sharing)] [[Baidu Wangpan](https://pan.baidu.com/s/1c2xWhBIfyz0zttHP6lVR6w)]~~

(Note: The PyTorch model was trained on a bug version whose some channel numbers are wrong. To load the model, you need
to change the channel numbers according to [here](https://github.com/DAA233/generative-inpainting-pytorch/pull/2/commits/12f49c441edcdf585905f1758f6b5a2f14cb8d86#diff-584f4b3ab12058252e96596e3fd01fffL102).
The links will be updated after the model is trained from scratch again.)
Trained PyTorch model: [[Google Drive](https://drive.google.com/open?id=1qbfA5BP9yzdTFFmiOTvYARUYgW1zwBBK)] [[Baidu Wangpan](https://pan.baidu.com/s/17HzpiqMPLIznvCWBfpNVGw)]

```bash
python test_single.py \
Expand All @@ -45,7 +41,7 @@ python test_tf_model.py \

## Test results on ImageNet validation set patches

With PyTorch, the model was trained on ImageNet for 50k iterations to converge (about 120h). Here are some test results on the patches from ImageNet validation set.
With PyTorch, the model was trained on ImageNet for 430k iterations to converge (with batch_size 48, about 150h). Here are some test results on the patches from ImageNet validation set.

| Input | Inpainted |
|:---:|:---:|
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