[Feature] Update New SegFormer models (#1705)

configs/segformer/README.md

@@ -35,9 +35,9 @@ We present SegFormer, a simple, efficient yet powerful semantic segmentation fra
 
 ## Usage
 
-To use other repositories' pre-trained models, it is necessary to convert keys.
+We have provided pretrained models converted from [SegFormer](https://github.com/NVlabs/SegFormer).
 
-We provide a script [`mit2mmseg.py`](../../tools/model_converters/mit2mmseg.py) in the tools directory to convert the key of models from [the official repo](https://github.com/NVlabs/SegFormer) to MMSegmentation style.
+If you want to convert keys on your own, we also provide a script [`mit2mmseg.py`](../../tools/model_converters/mit2mmseg.py) in the tools directory to convert the key of models from [the official repo](https://github.com/NVlabs/SegFormer) to MMSegmentation style.
 
 ```shell
 python tools/model_converters/mit2mmseg.py ${PRETRAIN_PATH} ${STORE_PATH}
@@ -51,28 +51,45 @@ This script convert model from `PRETRAIN_PATH` and store the converted model in
 
 | Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                       | download                                                                                                                                                                                                                                                                                                                                               |
 | --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | ---------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
-| Segformer | MIT-B0   | 512x512   |  160000 |      2.1 | 51.32          | 37.41 | 38.34         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530.log.json) |
-| Segformer | MIT-B1   | 512x512   |  160000 |      2.6 | 47.66          | 40.97 | 42.54         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106.log.json) |
-| Segformer | MIT-B2   | 512x512   |  160000 |      3.6 | 30.88          | 45.58 | 47.03         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103.log.json) |
-| Segformer | MIT-B3   | 512x512   |  160000 |      4.8 | 22.11          | 47.82 | 48.81         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410.log.json) |
-| Segformer | MIT-B4   | 512x512   |  160000 |      6.1 | 15.45          | 48.46 | 49.76         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055.log.json) |
+| Segformer | MIT-B0   | 512x512   |  160000 |      2.1 | 38.17          | 37.85 | 38.97         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20220617_162207-c00b9603.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20220617_162207.log.json) |
+| Segformer | MIT-B1   | 512x512   |  160000 |      2.6 | 37.80          | 42.13 | 43.74         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20220620_112037-c3f39e00.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20220620_112037.log.json) |
+| Segformer | MIT-B2   | 512x512   |  160000 |      3.6 | 26.80          | 46.80 | 48.12         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20220620_114047.log.json) |
+| Segformer | MIT-B3   | 512x512   |  160000 |      4.8 | 19.19          | 48.25 | 49.58         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20220617_162254-3a4b7363.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20220617_162254.log.json) |
+| Segformer | MIT-B4   | 512x512   |  160000 |      6.1 | 14.54          | 49.09 | 50.72         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20220620_112216-4fa4f58f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20220620_112216.log.json) |
 | Segformer | MIT-B5   | 512x512   |  160000 |      7.2 | 11.89          | 49.13 | 50.22         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235-94cedf59.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_512x512_160k_ade20k/segformer_mit-b5_512x512_160k_ade20k_20210726_145235.log.json) |
-| Segformer | MIT-B5   | 640x640   |  160000 |     11.5 | 11.30          | 49.62 | 50.36         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243.log.json) |
+| Segformer | MIT-B5   | 640x640   |  160000 |     11.5 | 10.60          | 50.19 | 51.41         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20220617_203542-940a6bd8.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20220617_203542.log.json) |
 
-Evaluation with AlignedResize:
+Evaluation with `AlignedResize`:
 
 | Method    | Backbone | Crop Size | Lr schd |  mIoU | mIoU(ms+flip) |
 | --------- | -------- | --------- | ------: | ----: | ------------- |
-| Segformer | MIT-B0   | 512x512   |  160000 |  38.1 | 38.57         |
-| Segformer | MIT-B1   | 512x512   |  160000 | 41.64 | 42.76         |
-| Segformer | MIT-B2   | 512x512   |  160000 | 46.53 | 47.49         |
-| Segformer | MIT-B3   | 512x512   |  160000 | 48.46 | 49.14         |
-| Segformer | MIT-B4   | 512x512   |  160000 | 49.34 | 50.29         |
+| Segformer | MIT-B0   | 512x512   |  160000 | 38.55 | 39.03         |
+| Segformer | MIT-B1   | 512x512   |  160000 | 43.26 | 44.11         |
+| Segformer | MIT-B2   | 512x512   |  160000 | 47.46 | 48.16         |
+| Segformer | MIT-B3   | 512x512   |  160000 | 49.27 | 49.94         |
+| Segformer | MIT-B4   | 512x512   |  160000 | 50.23 | 51.10         |
 | Segformer | MIT-B5   | 512x512   |  160000 | 50.08 | 50.72         |
-| Segformer | MIT-B5   | 640x640   |  160000 | 50.58 | 50.8          |
+| Segformer | MIT-B5   | 640x640   |  160000 | 51.13 | 51.66         |
 
-We replace `AlignedResize` in original implementatiuon to `Resize + ResizeToMultiple`. If you want to test by
-using `AlignedResize`, you can change the dataset pipeline like this:
+### Cityscapes
+
+The lower fps result is caused by the sliding window inference scheme (window size:1024x1024).
+
+| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                       |
+| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Segformer | MIT-B0   | 1024x1024 |  160000 |     3.64 | 4.74           | 76.54 | 78.22         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857.log.json) |
+| Segformer | MIT-B1   | 1024x1024 |  160000 |     4.49 | 4.3            | 78.56 | 79.73         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213.log.json) |
+| Segformer | MIT-B2   | 1024x1024 |  160000 |     7.42 | 3.36           | 81.08 | 82.18         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205.log.json) |
+| Segformer | MIT-B3   | 1024x1024 |  160000 |    10.86 | 2.53           | 81.94 | 83.14         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823.log.json) |
+| Segformer | MIT-B4   | 1024x1024 |  160000 |    15.07 | 1.88           | 81.89 | 83.38         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709.log.json) |
+| Segformer | MIT-B5   | 1024x1024 |  160000 |    18.00 | 1.39           | 82.25 | 83.48         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934.log.json) |
+
+Note:
+
+Original SegFormer paper uses different `test_pipeline` and image ratios in `ms+flip`. If you want to cite SegFormer original results as benchmark you may modify settings as below:
+
+- We replace `AlignedResize` in original implementatiuon to `Resize + ResizeToMultiple`. If you want to test by
+  using `AlignedResize`, you can change the dataset pipeline like this:
 
 ```python
 test_pipeline = [
@@ -94,15 +111,15 @@ test_pipeline = [
 ]
 ```
 
-### Cityscapes
+- Different from default setting of `ms+flip`, SegFormer original repo adopts [different image ratios](https://github.com/NVlabs/SegFormer/blob/master/tools/test.py#L97-L101) for ADE20K dataset. To re-implement numerical results of `ms+flip`, you can change image ratios in `tools/test.py` like this:
 
-The lower fps result is caused by the sliding window inference scheme (window size:1024x1024).
+```python
+if args.aug_test:
+    if cfg.data.test.type == 'ADE20KDataset':
+        # hard code index
+        cfg.data.test.pipeline[1].img_ratios = [
+            0.75, 0.875, 1.0, 1.125, 1.25
+        ]
+```
 
-| Method    | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) |  mIoU | mIoU(ms+flip) | config                                                                                                                                 | download                                                                                                                                                                                                                                                                                                                                                                                       |
-| --------- | -------- | --------- | ------: | -------: | -------------- | ----: | ------------- | -------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Segformer | MIT-B0   | 1024x1024 |  160000 |     3.64 | 4.74           | 76.54 | 78.22         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857-e7f88502.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes/segformer_mit-b0_8x1_1024x1024_160k_cityscapes_20211208_101857.log.json) |
-| Segformer | MIT-B1   | 1024x1024 |  160000 |     4.49 | 4.3            | 78.56 | 79.73         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213-655c7b3f.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes/segformer_mit-b1_8x1_1024x1024_160k_cityscapes_20211208_064213.log.json) |
-| Segformer | MIT-B2   | 1024x1024 |  160000 |     7.42 | 3.36           | 81.08 | 82.18         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205-6096669a.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes/segformer_mit-b2_8x1_1024x1024_160k_cityscapes_20211207_134205.log.json) |
-| Segformer | MIT-B3   | 1024x1024 |  160000 |    10.86 | 2.53           | 81.94 | 83.14         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823-a8f8a177.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes/segformer_mit-b3_8x1_1024x1024_160k_cityscapes_20211206_224823.log.json) |
-| Segformer | MIT-B4   | 1024x1024 |  160000 |    15.07 | 1.88           | 81.89 | 83.38         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709-07f6c333.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes/segformer_mit-b4_8x1_1024x1024_160k_cityscapes_20211207_080709.log.json) |
-| Segformer | MIT-B5   | 1024x1024 |  160000 |    18.00 | 1.39           | 82.25 | 83.48         | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934-87a052ec.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes/segformer_mit-b5_8x1_1024x1024_160k_cityscapes_20211206_072934.log.json) |
+- Training of SegFormer is not very stable, which is sensitive to random seeds.

configs/segformer/segformer.yml

@@ -22,7 +22,7 @@ Models:
     crop size: (512,512)
     lr schd: 160000
     inference time (ms/im):
-    - value: 19.49
+    - value: 26.2
       hardware: V100
       backend: PyTorch
       batch size: 1
@@ -33,10 +33,10 @@ Models:
   - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
-      mIoU: 37.41
-      mIoU(ms+flip): 38.34
+      mIoU: 37.85
+      mIoU(ms+flip): 38.97
   Config: configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20210726_101530-8ffa8fda.pth
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b0_512x512_160k_ade20k/segformer_mit-b0_512x512_160k_ade20k_20220617_162207-c00b9603.pth
 - Name: segformer_mit-b1_512x512_160k_ade20k
   In Collection: Segformer
   Metadata:
@@ -44,7 +44,7 @@ Models:
     crop size: (512,512)
     lr schd: 160000
     inference time (ms/im):
-    - value: 20.98
+    - value: 26.46
       hardware: V100
       backend: PyTorch
       batch size: 1
@@ -55,10 +55,10 @@ Models:
   - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
-      mIoU: 40.97
-      mIoU(ms+flip): 42.54
+      mIoU: 42.13
+      mIoU(ms+flip): 43.74
   Config: configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20210726_112106-d70e859d.pth
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b1_512x512_160k_ade20k/segformer_mit-b1_512x512_160k_ade20k_20220620_112037-c3f39e00.pth
 - Name: segformer_mit-b2_512x512_160k_ade20k
   In Collection: Segformer
   Metadata:
@@ -66,7 +66,7 @@ Models:
     crop size: (512,512)
     lr schd: 160000
     inference time (ms/im):
-    - value: 32.38
+    - value: 37.31
       hardware: V100
       backend: PyTorch
       batch size: 1
@@ -77,10 +77,10 @@ Models:
   - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
-      mIoU: 45.58
-      mIoU(ms+flip): 47.03
+      mIoU: 46.8
+      mIoU(ms+flip): 48.12
   Config: configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20210726_112103-cbd414ac.pth
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b2_512x512_160k_ade20k/segformer_mit-b2_512x512_160k_ade20k_20220620_114047-64e4feca.pth
 - Name: segformer_mit-b3_512x512_160k_ade20k
   In Collection: Segformer
   Metadata:
@@ -88,7 +88,7 @@ Models:
     crop size: (512,512)
     lr schd: 160000
     inference time (ms/im):
-    - value: 45.23
+    - value: 52.11
       hardware: V100
       backend: PyTorch
       batch size: 1
@@ -99,10 +99,10 @@ Models:
   - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
-      mIoU: 47.82
-      mIoU(ms+flip): 48.81
+      mIoU: 48.25
+      mIoU(ms+flip): 49.58
   Config: configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20210726_081410-962b98d2.pth
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b3_512x512_160k_ade20k/segformer_mit-b3_512x512_160k_ade20k_20220617_162254-3a4b7363.pth
 - Name: segformer_mit-b4_512x512_160k_ade20k
   In Collection: Segformer
   Metadata:
@@ -110,7 +110,7 @@ Models:
     crop size: (512,512)
     lr schd: 160000
     inference time (ms/im):
-    - value: 64.72
+    - value: 68.78
       hardware: V100
       backend: PyTorch
       batch size: 1
@@ -121,10 +121,10 @@ Models:
   - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
-      mIoU: 48.46
-      mIoU(ms+flip): 49.76
+      mIoU: 49.09
+      mIoU(ms+flip): 50.72
   Config: configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20210728_183055-7f509d7d.pth
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b4_512x512_160k_ade20k/segformer_mit-b4_512x512_160k_ade20k_20220620_112216-4fa4f58f.pth
 - Name: segformer_mit-b5_512x512_160k_ade20k
   In Collection: Segformer
   Metadata:
@@ -154,7 +154,7 @@ Models:
     crop size: (640,640)
     lr schd: 160000
     inference time (ms/im):
-    - value: 88.5
+    - value: 94.34
       hardware: V100
       backend: PyTorch
       batch size: 1
@@ -165,10 +165,10 @@ Models:
   - Task: Semantic Segmentation
     Dataset: ADE20K
     Metrics:
-      mIoU: 49.62
-      mIoU(ms+flip): 50.36
+      mIoU: 50.19
+      mIoU(ms+flip): 51.41
   Config: configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py
-  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20210801_121243-41d2845b.pth
+  Weights: https://download.openmmlab.com/mmsegmentation/v0.5/segformer/segformer_mit-b5_640x640_160k_ade20k/segformer_mit-b5_640x640_160k_ade20k_20220617_203542-940a6bd8.pth
 - Name: segformer_mit-b0_8x1_1024x1024_160k_cityscapes
   In Collection: Segformer
   Metadata:

configs/segformer/segformer_mit-b0_512x512_160k_ade20k.py

@@ -3,8 +3,9 @@ _base_ = [
     '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
 ]
 
-model = dict(
-    pretrained='pretrain/mit_b0.pth', decode_head=dict(num_classes=150))
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth'  # noqa
+
+model = dict(pretrained=checkpoint, decode_head=dict(num_classes=150))
 
 # optimizer
 optimizer = dict(

configs/segformer/segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py

@@ -4,9 +4,10 @@ _base_ = [
     '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
 ]
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b0_20220624-7e0fe6dd.pth'  # noqa
+
 model = dict(
-    backbone=dict(
-        init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b0.pth')),
+    backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=checkpoint)),
     test_cfg=dict(mode='slide', crop_size=(1024, 1024), stride=(768, 768)))
 
 # optimizer

configs/segformer/segformer_mit-b1_512x512_160k_ade20k.py

@@ -1,8 +1,10 @@
 _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b1_20220624-02e5a6a1.pth'  # noqa
+
 # model settings
 model = dict(
-    pretrained='pretrain/mit_b1.pth',
+    pretrained=checkpoint,
     backbone=dict(
         embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[2, 2, 2, 2]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b1_8x1_1024x1024_160k_cityscapes.py

@@ -1,7 +1,8 @@
 _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b1_20220624-02e5a6a1.pth'  # noqa
 model = dict(
     backbone=dict(
-        init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b1.pth'),
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
         embed_dims=64),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b2_512x512_160k_ade20k.py

@@ -1,8 +1,10 @@
 _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b2_20220624-66e8bf70.pth'  # noqa
+
 # model settings
 model = dict(
-    pretrained='pretrain/mit_b2.pth',
+    pretrained=checkpoint,
     backbone=dict(
         embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 4, 6, 3]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b2_8x1_1024x1024_160k_cityscapes.py

@@ -1,8 +1,9 @@
 _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b2_20220624-66e8bf70.pth'  # noqa
 model = dict(
     backbone=dict(
-        init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b2.pth'),
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
         embed_dims=64,
         num_layers=[3, 4, 6, 3]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b3_512x512_160k_ade20k.py

@@ -1,8 +1,10 @@
 _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b3_20220624-13b1141c.pth'  # noqa
+
 # model settings
 model = dict(
-    pretrained='pretrain/mit_b3.pth',
+    pretrained=checkpoint,
     backbone=dict(
         embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 4, 18, 3]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b3_8x1_1024x1024_160k_cityscapes.py

@@ -1,8 +1,9 @@
 _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b3_20220624-13b1141c.pth'  # noqa
 model = dict(
     backbone=dict(
-        init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b3.pth'),
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
         embed_dims=64,
         num_layers=[3, 4, 18, 3]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b4_512x512_160k_ade20k.py

@@ -1,8 +1,10 @@
 _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth'  # noqa
+
 # model settings
 model = dict(
-    pretrained='pretrain/mit_b4.pth',
+    pretrained=checkpoint,
     backbone=dict(
         embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 8, 27, 3]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b4_8x1_1024x1024_160k_cityscapes.py

@@ -1,8 +1,9 @@
 _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b4_20220624-d588d980.pth'  # noqa
 model = dict(
     backbone=dict(
-        init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b4.pth'),
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
         embed_dims=64,
         num_layers=[3, 8, 27, 3]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b5_512x512_160k_ade20k.py

@@ -1,8 +1,10 @@
 _base_ = ['./segformer_mit-b0_512x512_160k_ade20k.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pthh'  # noqa
+
 # model settings
 model = dict(
-    pretrained='pretrain/mit_b5.pth',
+    pretrained=checkpoint,
     backbone=dict(
         embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 6, 40, 3]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b5_640x640_160k_ade20k.py

@@ -37,8 +37,9 @@ data = dict(
     test=dict(pipeline=test_pipeline))
 
 # model settings
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pthh'  # noqa
 model = dict(
-    pretrained='pretrain/mit_b5.pth',
+    pretrained=checkpoint,
     backbone=dict(
         embed_dims=64, num_heads=[1, 2, 5, 8], num_layers=[3, 6, 40, 3]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))

configs/segformer/segformer_mit-b5_8x1_1024x1024_160k_cityscapes.py

@@ -1,8 +1,9 @@
 _base_ = ['./segformer_mit-b0_8x1_1024x1024_160k_cityscapes.py']
 
+checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segformer/mit_b5_20220624-658746d9.pthh'  # noqa
 model = dict(
     backbone=dict(
-        init_cfg=dict(type='Pretrained', checkpoint='pretrain/mit_b5.pth'),
+        init_cfg=dict(type='Pretrained', checkpoint=checkpoint),
         embed_dims=64,
         num_layers=[3, 6, 40, 3]),
     decode_head=dict(in_channels=[64, 128, 320, 512]))