From 97f9670c5a4a2a3b4cfb411bcc26db16b23745f7 Mon Sep 17 00:00:00 2001 From: MengzhangLI Date: Tue, 16 Nov 2021 20:14:17 +0800 Subject: [PATCH 1/3] fixing dice metric in unet (#1041) --- .dev/md2yml.py | 2 +- configs/unet/unet.yml | 24 ++++++++++++------------ 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/.dev/md2yml.py b/.dev/md2yml.py index 6bb1349d65..311f6d072e 100755 --- a/.dev/md2yml.py +++ b/.dev/md2yml.py @@ -176,7 +176,7 @@ def parse_md(md_file): 'Task': 'Semantic Segmentation', 'Dataset': current_dataset, 'Metrics': { - 'mIoU': float(els[ss_id]), + cols[ss_id]: float(els[ss_id]), }, }, ], diff --git a/configs/unet/unet.yml b/configs/unet/unet.yml index e7991f40fe..0fc77325d7 100644 --- a/configs/unet/unet.yml +++ b/configs/unet/unet.yml @@ -27,7 +27,7 @@ Models: - Task: Semantic Segmentation Dataset: DRIVE Metrics: - mIoU: 78.67 + Dice: 78.67 Config: configs/unet/fcn_unet_s5-d16_64x64_40k_drive.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_64x64_40k_drive/fcn_unet_s5-d16_64x64_40k_drive_20201223_191051-5daf6d3b.pth - Name: pspnet_unet_s5-d16_64x64_40k_drive @@ -41,7 +41,7 @@ Models: - Task: Semantic Segmentation Dataset: DRIVE Metrics: - mIoU: 78.62 + Dice: 78.62 Config: configs/unet/pspnet_unet_s5-d16_64x64_40k_drive.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_64x64_40k_drive/pspnet_unet_s5-d16_64x64_40k_drive_20201227_181818-aac73387.pth - Name: deeplabv3_unet_s5-d16_64x64_40k_drive @@ -55,7 +55,7 @@ Models: - Task: Semantic Segmentation Dataset: DRIVE Metrics: - mIoU: 78.69 + Dice: 78.69 Config: configs/unet/deeplabv3_unet_s5-d16_64x64_40k_drive.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_64x64_40k_drive/deeplabv3_unet_s5-d16_64x64_40k_drive_20201226_094047-0671ff20.pth - Name: fcn_unet_s5-d16_128x128_40k_stare @@ -69,7 +69,7 @@ Models: - Task: Semantic Segmentation Dataset: STARE Metrics: - mIoU: 81.02 + Dice: 81.02 Config: configs/unet/fcn_unet_s5-d16_128x128_40k_stare.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_stare/fcn_unet_s5-d16_128x128_40k_stare_20201223_191051-7d77e78b.pth - Name: pspnet_unet_s5-d16_128x128_40k_stare @@ -83,7 +83,7 @@ Models: - Task: Semantic Segmentation Dataset: STARE Metrics: - mIoU: 81.22 + Dice: 81.22 Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_stare.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_stare/pspnet_unet_s5-d16_128x128_40k_stare_20201227_181818-3c2923c4.pth - Name: deeplabv3_unet_s5-d16_128x128_40k_stare @@ -97,7 +97,7 @@ Models: - Task: Semantic Segmentation Dataset: STARE Metrics: - mIoU: 80.93 + Dice: 80.93 Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_stare.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_stare/deeplabv3_unet_s5-d16_128x128_40k_stare_20201226_094047-93dcb93c.pth - Name: fcn_unet_s5-d16_128x128_40k_chase_db1 @@ -111,7 +111,7 @@ Models: - Task: Semantic Segmentation Dataset: CHASE_DB1 Metrics: - mIoU: 80.24 + Dice: 80.24 Config: configs/unet/fcn_unet_s5-d16_128x128_40k_chase_db1.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_128x128_40k_chase_db1/fcn_unet_s5-d16_128x128_40k_chase_db1_20201223_191051-11543527.pth - Name: pspnet_unet_s5-d16_128x128_40k_chase_db1 @@ -125,7 +125,7 @@ Models: - Task: Semantic Segmentation Dataset: CHASE_DB1 Metrics: - mIoU: 80.36 + Dice: 80.36 Config: configs/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_128x128_40k_chase_db1/pspnet_unet_s5-d16_128x128_40k_chase_db1_20201227_181818-68d4e609.pth - Name: deeplabv3_unet_s5-d16_128x128_40k_chase_db1 @@ -139,7 +139,7 @@ Models: - Task: Semantic Segmentation Dataset: CHASE_DB1 Metrics: - mIoU: 80.47 + Dice: 80.47 Config: configs/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_128x128_40k_chase_db1/deeplabv3_unet_s5-d16_128x128_40k_chase_db1_20201226_094047-4c5aefa3.pth - Name: fcn_unet_s5-d16_256x256_40k_hrf @@ -153,7 +153,7 @@ Models: - Task: Semantic Segmentation Dataset: HRF Metrics: - mIoU: 79.45 + Dice: 79.45 Config: configs/unet/fcn_unet_s5-d16_256x256_40k_hrf.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/fcn_unet_s5-d16_256x256_40k_hrf/fcn_unet_s5-d16_256x256_40k_hrf_20201223_173724-d89cf1ed.pth - Name: pspnet_unet_s5-d16_256x256_40k_hrf @@ -167,7 +167,7 @@ Models: - Task: Semantic Segmentation Dataset: HRF Metrics: - mIoU: 80.07 + Dice: 80.07 Config: configs/unet/pspnet_unet_s5-d16_256x256_40k_hrf.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/pspnet_unet_s5-d16_256x256_40k_hrf/pspnet_unet_s5-d16_256x256_40k_hrf_20201227_181818-fdb7e29b.pth - Name: deeplabv3_unet_s5-d16_256x256_40k_hrf @@ -181,6 +181,6 @@ Models: - Task: Semantic Segmentation Dataset: HRF Metrics: - mIoU: 80.21 + Dice: 80.21 Config: configs/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/unet/deeplabv3_unet_s5-d16_256x256_40k_hrf/deeplabv3_unet_s5-d16_256x256_40k_hrf_20201226_094047-3a1fdf85.pth From e38eae3894aea53a3b77deeee78b8feff2d43f2e Mon Sep 17 00:00:00 2001 From: MengzhangLI Date: Wed, 17 Nov 2021 16:12:02 +0800 Subject: [PATCH 2/3] [Benchmark] Add BiSeNetV1 COCO-Stuff 164k benchmark (#1019) * bisenetv1 on cocostuff164k * change config_names & delete redundant keys * pretrain should before lr. * remove redundancy in bisenetv1_r50-d32 --- configs/bisenetv1/README.md | 13 ++- configs/bisenetv1/bisenetv1.yml | 109 ++++++++++++++++++ ..._lr5e-3_4x4_512x512_160k_coco-stuff164k.py | 6 + ..._lr5e-3_4x4_512x512_160k_coco-stuff164k.py | 18 +++ ..._lr5e-3_4x4_512x512_160k_coco-stuff164k.py | 6 + ..._lr5e-3_4x4_512x512_160k_coco-stuff164k.py | 13 +++ ..._lr5e-3_4x4_512x512_160k_coco-stuff164k.py | 7 ++ ..._lr5e-3_4x4_512x512_160k_coco-stuff164k.py | 18 +++ 8 files changed, 189 insertions(+), 1 deletion(-) create mode 100644 configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py create mode 100644 configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py create mode 100644 configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py create mode 100644 configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py create mode 100644 configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py create mode 100644 configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py diff --git a/configs/bisenetv1/README.md b/configs/bisenetv1/README.md index dd5bd503b2..e7a1c8dab1 100644 --- a/configs/bisenetv1/README.md +++ b/configs/bisenetv1/README.md @@ -35,8 +35,19 @@ | BiSeNetV1 (No Pretrain) | R-50-D32 | 1024x1024 | 160000 | 15.39 | 7.71 | 76.92 | 78.87 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639.log.json) | | BiSeNetV1 | R-50-D32 | 1024x1024 | 160000 | 15.39 | 7.71 | 77.68 | 79.57 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628.log.json) | +### COCO-Stuff 164k + +| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download | +| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| BiSeNetV1 (No Pretrain) | R-18-D32 | 512x512 | 160000 | - | - | 25.45 | 26.15 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328.log.json) | +| BiSeNetV1| R-18-D32 | 512x512 | 160000 | 6.33 | 74.24 | 28.55 | 29.26 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100.log.json) | +| BiSeNetV1 (No Pretrain) | R-50-D32 | 512x512 | 160000 | - | - | 29.82 | 30.33 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616.log.json) | +| BiSeNetV1 | R-50-D32 | 512x512 | 160000 | 9.28 | 32.60 | 34.88 | 35.37 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932.log.json) | +| BiSeNetV1(No Pretrain) | R-101-D32 | 512x512 | 160000 | - | - | 31.14 | 31.76 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147.log.json) | +| BiSeNetV1 | R-101-D32 | 512x512 | 160000 | 10.36 | 25.25 | 37.38 | 37.99 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220.log.json) | + Note: - `4x8`: Using 4 GPUs with 8 samples per GPU in training. -- Default setting is 4 GPUs with 4 samples per GPU in training. +- For BiSeNetV1 on Cityscapes dataset, default setting is 4 GPUs with 4 samples per GPU in training. - `No Pretrain` means the model is trained from scratch. diff --git a/configs/bisenetv1/bisenetv1.yml b/configs/bisenetv1/bisenetv1.yml index 8ea94df4bd..26a7c60044 100644 --- a/configs/bisenetv1/bisenetv1.yml +++ b/configs/bisenetv1/bisenetv1.yml @@ -3,6 +3,7 @@ Collections: Metadata: Training Data: - Cityscapes + - COCO-Stuff 164k Paper: URL: https://arxiv.org/abs/1808.00897 Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' @@ -123,3 +124,111 @@ Models: mIoU(ms+flip): 79.57 Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth +- Name: bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: bisenetv1 + Metadata: + backbone: R-18-D32 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 25.45 + mIoU(ms+flip): 26.15 + Config: configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth +- Name: bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: bisenetv1 + Metadata: + backbone: R-18-D32 + crop size: (512,512) + lr schd: 160000 + inference time (ms/im): + - value: 13.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + memory (GB): 6.33 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 28.55 + mIoU(ms+flip): 29.26 + Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth +- Name: bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: bisenetv1 + Metadata: + backbone: R-50-D32 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 29.82 + mIoU(ms+flip): 30.33 + Config: configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth +- Name: bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: bisenetv1 + Metadata: + backbone: R-50-D32 + crop size: (512,512) + lr schd: 160000 + inference time (ms/im): + - value: 30.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + memory (GB): 9.28 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 34.88 + mIoU(ms+flip): 35.37 + Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth +- Name: bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: bisenetv1 + Metadata: + backbone: R-101-D32 + crop size: (512,512) + lr schd: 160000 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 31.14 + mIoU(ms+flip): 31.76 + Config: configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth +- Name: bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k + In Collection: bisenetv1 + Metadata: + backbone: R-101-D32 + crop size: (512,512) + lr schd: 160000 + inference time (ms/im): + - value: 39.6 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (512,512) + memory (GB): 10.36 + Results: + - Task: Semantic Segmentation + Dataset: COCO-Stuff 164k + Metrics: + mIoU: 37.38 + mIoU(ms+flip): 37.99 + Config: configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py + Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth diff --git a/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000..c3fe21597d --- /dev/null +++ b/configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,6 @@ +_base_ = './bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py' +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet101_v1c')))) diff --git a/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000..b1e1c3e863 --- /dev/null +++ b/configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/bisenetv1_r18-d32.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +model = dict( + backbone=dict( + context_channels=(512, 1024, 2048), + spatial_channels=(256, 256, 256, 512), + out_channels=1024, + backbone_cfg=dict(type='ResNet', depth=101)), + decode_head=dict(in_channels=1024, channels=1024, num_classes=171), + auxiliary_head=[ + dict(in_channels=512, channels=256, num_classes=171), + dict(in_channels=512, channels=256, num_classes=171), + ]) +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.005) diff --git a/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000..c6d93049e2 --- /dev/null +++ b/configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,6 @@ +_base_ = './bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py' +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet18_v1c'))), ) diff --git a/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000..78d7fea629 --- /dev/null +++ b/configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/bisenetv1_r18-d32.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +model = dict( + decode_head=dict(num_classes=171), + auxiliary_head=[ + dict(num_classes=171), + dict(num_classes=171), + ]) +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.005) diff --git a/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000..f0fea69f2f --- /dev/null +++ b/configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,7 @@ +_base_ = './bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py' + +model = dict( + backbone=dict( + backbone_cfg=dict( + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnet50_v1c')))) diff --git a/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py b/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py new file mode 100644 index 0000000000..dbbccc69d8 --- /dev/null +++ b/configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/bisenetv1_r18-d32.py', + '../_base_/datasets/coco-stuff164k.py', '../_base_/default_runtime.py', + '../_base_/schedules/schedule_160k.py' +] +model = dict( + backbone=dict( + context_channels=(512, 1024, 2048), + spatial_channels=(256, 256, 256, 512), + out_channels=1024, + backbone_cfg=dict(type='ResNet', depth=50)), + decode_head=dict(in_channels=1024, channels=1024, num_classes=171), + auxiliary_head=[ + dict(in_channels=512, channels=256, num_classes=171), + dict(in_channels=512, channels=256, num_classes=171), + ]) +lr_config = dict(warmup='linear', warmup_iters=1000) +optimizer = dict(lr=0.005) From 2b2f107fd6f52f6a516747bfb1a2a83f074f2448 Mon Sep 17 00:00:00 2001 From: MengzhangLI Date: Thu, 18 Nov 2021 21:57:30 +0800 Subject: [PATCH 3/3] add mmflow on README (#1052) --- README.md | 1 + README_zh-CN.md | 1 + docs/conf.py | 4 ++++ docs_zh-CN/conf.py | 4 ++++ 4 files changed, 10 insertions(+) diff --git a/README.md b/README.md index 18317e01e8..30ca3dc4d4 100644 --- a/README.md +++ b/README.md @@ -165,3 +165,4 @@ and develop their own new semantic segmentation methods. - [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding. - [MMGeneration](https://github.com/open-mmlab/mmgeneration): A powerful toolkit for generative models. - [MIM](https://github.com/open-mmlab/mim): MIM Installs OpenMMLab Packages. +- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark. diff --git a/README_zh-CN.md b/README_zh-CN.md index d7cf65ce68..89d5a28159 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -160,6 +160,7 @@ MMSegmentation 是一个由来自不同高校和企业的研发人员共同参 - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱 - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包 - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 生成模型工具箱 +- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准 ## 欢迎加入 OpenMMLab 社区 diff --git a/docs/conf.py b/docs/conf.py index 50c425fdf7..6d1997ff26 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -147,6 +147,10 @@ def get_version(): 'name': 'MMGeneration', 'url': 'https://github.com/open-mmlab/mmgeneration', }, + { + 'name': 'MMFlow', + 'url': 'https://github.com/open-mmlab/mmflow', + }, ] }, { diff --git a/docs_zh-CN/conf.py b/docs_zh-CN/conf.py index 4cb2bfb899..44acfd5a7c 100644 --- a/docs_zh-CN/conf.py +++ b/docs_zh-CN/conf.py @@ -147,6 +147,10 @@ def get_version(): 'name': 'MMGeneration', 'url': 'https://github.com/open-mmlab/mmgeneration', }, + { + 'name': 'MMFlow', + 'url': 'https://github.com/open-mmlab/mmflow', + }, ] }, {