From 8f4b84ed8d22780b5276bbb1ec7eea2c77788c32 Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Tue, 2 Apr 2024 17:10:16 +0800
Subject: [PATCH 01/11] Feature: Add .gitignore

---
 .gitignore | 168 +++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 168 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..dd954d7
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,168 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+
+# custom
+data/
+deploy/
+nuitka-crash-report.xml
+outputs/
+pretrained/
+

From 44c848eec01d1d105bce93f4a86704a584ec12f0 Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Tue, 2 Apr 2024 17:10:35 +0800
Subject: [PATCH 02/11] Feature: Add requirements.txt

---
 requirements.txt | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 requirements.txt

diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..b1feabe
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+torch # == 1.8.1
+segmentation-models-pytorch
+torchmetrics
+albumentations
+loguru
+tqdm

From 7b1d960b3e24c4d93ee763cfad650eabb5dfb0fa Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Tue, 2 Apr 2024 17:13:24 +0800
Subject: [PATCH 03/11] Feature: Exporting ONNX models for DDRNet and STDC

---
 models/ddrnet.py | 5 +++++
 models/stdc.py   | 5 +++++
 2 files changed, 10 insertions(+)

diff --git a/models/ddrnet.py b/models/ddrnet.py
index 7184b63..e777dae 100644
--- a/models/ddrnet.py
+++ b/models/ddrnet.py
@@ -52,6 +52,11 @@ def forward(self, x, is_training=False):
         x = self.seg_head(x)
         x = F.interpolate(x, size, mode='bilinear', align_corners=True)
 
+        if torch.onnx.is_in_onnx_export():
+            # output_data = x.softmax(dim=1)
+            max_probs, predictions = x.max(1, keepdim=True)
+            return predictions.to(torch.int8)
+
         if self.use_aux and is_training:
             return x, (x_aux,)
         else:
diff --git a/models/stdc.py b/models/stdc.py
index 259d9a8..45ab052 100644
--- a/models/stdc.py
+++ b/models/stdc.py
@@ -87,6 +87,11 @@ def forward(self, x, is_training=False):
         x = self.seg_head(x)
         x = F.interpolate(x, size, mode='bilinear', align_corners=True)
 
+        if torch.onnx.is_in_onnx_export():
+            # output_data = x.softmax(dim=1)
+            max_probs, predictions = x.max(1, keepdim=True)
+            return predictions.to(torch.int8)
+
         if self.use_detail_head and is_training:
             x_detail = self.detail_head(x3)
             return x, x_detail

From bfc309826ded1081cd8f1deebe821057ae6bfa9a Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Tue, 2 Apr 2024 17:15:09 +0800
Subject: [PATCH 04/11] Feature: Add ResizeToSquare transforms

---
 utils/transforms.py | 38 +++++++++++++++++++++++++++++++++++++-
 1 file changed, 37 insertions(+), 1 deletion(-)

diff --git a/utils/transforms.py b/utils/transforms.py
index f0db895..e22188c 100644
--- a/utils/transforms.py
+++ b/utils/transforms.py
@@ -1,5 +1,6 @@
 import numpy as np
 import albumentations as AT
+import torch.nn.functional as F
 
 
 def to_numpy(array):
@@ -29,4 +30,39 @@ def __call__(self, image, mask=None):
             augmented = aug(image=img)
         else:
             augmented = aug(image=img, mask=msk)
-        return augmented
\ No newline at end of file
+        return augmented
+
+
+class ResizeToSquare:
+    def __init__(self, size, interpolation=1, p=1, is_testing=False):
+        self.size = size
+        self.interpolation = interpolation
+        self.p = p
+        self.is_testing = is_testing
+
+    def __call__(self, image, mask=None):
+        img = to_numpy(image)
+
+        h, w, _ = img.shape
+        max_wh = np.max([w, h])
+        hp = int((max_wh - w) / 2)
+        vp = int((max_wh - h) / 2)
+        padding = ((vp, vp), (hp, hp), (0, 0))
+        img = np.pad(img, padding, mode='constant', constant_values=0)
+
+        if not self.is_testing:
+            msk = to_numpy(mask)
+            msk_h, msk_w, _ = img.shape
+            msk_max_wh = np.max([msk_w, msk_h])
+            msk_hp = int((msk_max_wh - msk_w) / 2)
+            msk_vp = int((msk_max_wh - msk_h) / 2)
+            msk_padding = ((msk_vp, msk_vp), (msk_hp, msk_hp))
+            msk = np.pad(msk, msk_padding, mode='constant', constant_values=0)
+
+        aug = AT.Resize(height=self.size, width=self.size, interpolation=self.interpolation, p=self.p)
+
+        if self.is_testing:
+            augmented = aug(image=img)
+        else:
+            augmented = aug(image=img, mask=msk)
+        return augmented

From ffa5740a7efdf1234350327548efa9a649b7429e Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Tue, 2 Apr 2024 17:16:05 +0800
Subject: [PATCH 05/11] Feature: Add support for importing BaseConfig

---
 configs/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/configs/__init__.py b/configs/__init__.py
index c7dc363..9e2a940 100644
--- a/configs/__init__.py
+++ b/configs/__init__.py
@@ -1,2 +1,3 @@
+from .base_config import BaseConfig
 from .my_config import MyConfig
 from .parser import load_parser
\ No newline at end of file

From 13bc83b8670a34fe7fb0aab7b8b916d430b081bc Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Tue, 2 Apr 2024 17:19:05 +0800
Subject: [PATCH 06/11] Feature: Add support for creating save_dir

---
 core/seg_trainer.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/core/seg_trainer.py b/core/seg_trainer.py
index 841a080..63f15d3 100644
--- a/core/seg_trainer.py
+++ b/core/seg_trainer.py
@@ -158,6 +158,10 @@ def predict(self, config):
             
         self.logger.info('\nStart predicting...\n')
 
+        save_images_dir = os.path.join(config.save_dir, 'predicts')
+        if not os.path.exists(save_images_dir):
+            os.makedirs(save_images_dir)
+
         self.model.eval() # Put model in evalation mode
 
         for (images, images_aug, img_names) in tqdm(self.test_loader):
@@ -171,7 +175,7 @@ def predict(self, config):
 
             # Saving results
             for i in range(preds.shape[0]):
-                save_path = os.path.join(config.save_dir, img_names[i])
+                save_path = os.path.join(save_images_dir, img_names[i])
                 save_suffix = img_names[i].split('.')[-1]
                  
                 pred = Image.fromarray(preds[i].astype(np.uint8))

From 73cd07e1b8692bbdcfd0050dc0d39c5f4c38a229 Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Tue, 2 Apr 2024 17:21:40 +0800
Subject: [PATCH 07/11] Feature: Add support for custom datasets

---
 configs/parser.py    |  2 +-
 datasets/__init__.py |  6 +++-
 datasets/custom.py   | 85 ++++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 91 insertions(+), 2 deletions(-)
 create mode 100644 datasets/custom.py

diff --git a/configs/parser.py b/configs/parser.py
index da285c8..b51240f 100644
--- a/configs/parser.py
+++ b/configs/parser.py
@@ -16,7 +16,7 @@ def load_parser(config):
 def get_parser():
     parser = argparse.ArgumentParser()
     # Dataset
-    parser.add_argument('--dataset', type=str, default=None, choices=['cityscapes'],
+    parser.add_argument('--dataset', type=str, default=None, choices=['cityscapes', 'custom'],
         help='choose which dataset you want to use')
     parser.add_argument('--dataroot', type=str, default=None, 
         help='path to your dataset')
diff --git a/datasets/__init__.py b/datasets/__init__.py
index 5a1b07c..90bf60e 100644
--- a/datasets/__init__.py
+++ b/datasets/__init__.py
@@ -1,7 +1,11 @@
 from torch.utils.data import DataLoader
 from .cityscapes import Cityscapes
+from .custom import Custom
 
-dataset_hub = {'cityscapes':Cityscapes,}
+dataset_hub = {
+    'cityscapes':Cityscapes,
+    'custom': Custom,
+}
 
 
 def get_dataset(config):
diff --git a/datasets/custom.py b/datasets/custom.py
new file mode 100644
index 0000000..8cd73ef
--- /dev/null
+++ b/datasets/custom.py
@@ -0,0 +1,85 @@
+import os
+from collections import namedtuple
+import yaml
+import numpy as np
+from PIL import Image
+from torch.utils.data import Dataset
+import albumentations as AT
+from albumentations.pytorch import ToTensorV2
+from utils import transforms
+
+
+class Custom(Dataset):
+    '''
+    demo for load custom datasets.
+    '''
+
+    def __init__(self, config, mode='train'):
+        data_root = os.path.expanduser(config.data_root)
+        dataset_config_filepath = os.path.join(data_root, 'data.yaml')
+        if not os.path.exists(dataset_config_filepath):
+            raise Exception(f"{dataset_config_filepath} not exists.")
+        with open(dataset_config_filepath, 'r', encoding='utf-8') as yaml_file:
+            dataset_config = yaml.safe_load(yaml_file)
+        print('dataset_config: ', dataset_config)
+        data_root = dataset_config['path']
+        # self.num_classes = len(dataset_config['names'])
+        self.id_to_train_id = dict()
+        for i in range(len(dataset_config['names'])):
+            self.id_to_train_id[i] = i
+        # self.train_id_to_name = dict()
+        # for k, v in dataset_config['names'].items():
+        #     self.train_id_to_name[k] = str(v)
+            
+        img_dir = os.path.join(data_root, mode, 'imgs')
+        msk_dir = os.path.join(data_root, mode, 'masks')
+
+        if not os.path.isdir(img_dir):
+            raise RuntimeError(f'Image directory: {img_dir} does not exist.')
+            
+        if not os.path.isdir(msk_dir):
+            raise RuntimeError(f'Mask directory: {msk_dir} does not exist.')
+        
+        if mode == 'train':
+            self.transform = AT.Compose([
+                transforms.ResizeToSquare(size=config.train_size),
+                transforms.Scale(scale=config.scale),
+                AT.RandomScale(scale_limit=config.randscale),
+                AT.PadIfNeeded(min_height=config.crop_h, min_width=config.crop_w, value=(114,114,114), mask_value=(0,0,0)),
+                AT.RandomCrop(height=config.crop_h, width=config.crop_w),
+                AT.ColorJitter(brightness=config.brightness, contrast=config.contrast, saturation=config.saturation),
+                AT.HorizontalFlip(p=config.h_flip),
+                AT.Normalize(mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)),
+                ToTensorV2(),                
+            ])
+            
+        elif mode == 'val':
+            self.transform = AT.Compose([
+                transforms.ResizeToSquare(size=config.test_size),
+                transforms.Scale(scale=config.scale),
+                AT.Normalize(mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)),
+                ToTensorV2(),
+            ])
+
+        self.images = []
+        self.masks = []
+
+        for img_file_name in os.listdir(img_dir):
+            img_file_basename = os.path.splitext(img_file_name)[0]
+
+            self.images.append(os.path.join(img_dir, img_file_name))
+            self.masks.append(os.path.join(msk_dir, img_file_basename + '.png'))
+
+    def __len__(self):
+        return len(self.images)
+
+    def __getitem__(self, index):
+        image = np.asarray(Image.open(self.images[index]).convert('RGB'))
+        mask = np.asarray(Image.open(self.masks[index]).convert('L'))
+        
+        # Perform augmentation and normalization
+        augmented = self.transform(image=image, mask=mask)
+        image, mask = augmented['image'], augmented['mask']
+        
+        return image, mask
+  
\ No newline at end of file

From cf383aa40f0e6020fab3b4a064cd274e597e7a9d Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Wed, 3 Apr 2024 14:05:35 +0800
Subject: [PATCH 08/11] update requirements.txt

---
 requirements.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/requirements.txt b/requirements.txt
index b1feabe..051e7f2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,3 +4,7 @@ torchmetrics
 albumentations
 loguru
 tqdm
+tensorboard
+onnx
+onnxsim
+onnxruntime

From 68821dfedbd36bc8542123b0b9963c08abee7413 Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Wed, 3 Apr 2024 14:12:51 +0800
Subject: [PATCH 09/11] Fix: Segformer backbone compatibility with mit_b*
 encoders

---
 models/__init__.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/models/__init__.py b/models/__init__.py
index daf6056..8cd38c9 100644
--- a/models/__init__.py
+++ b/models/__init__.py
@@ -68,7 +68,15 @@ def get_model(config):
         if config.decoder not in decoder_hub:
             raise ValueError(f"Unsupported decoder type: {config.decoder}")
 
-        model = decoder_hub[config.decoder](encoder_name=config.encoder, 
+        if config.encoder.startswith('mit_b') and config.decoder in ['pan']:
+                model = decoder_hub[config.decoder](encoder_name=config.encoder, 
+                                                encoder_weights=config.encoder_weights, 
+                                                encoder_output_stride=32,
+                                                in_channels=3, classes=config.num_class)
+        elif config.encoder.startswith('mit_b') and config.decoder in ['deeplabv3', 'deeplabv3p', 'linknet', 'unetpp']:
+            raise ValueError("Encoder `{}` is not supported for `{}".format(config.encoder, config.decoder))
+        else:
+            model = decoder_hub[config.decoder](encoder_name=config.encoder, 
                                             encoder_weights=config.encoder_weights, 
                                             in_channels=3, classes=config.num_class)
 

From 154d4db5013e51dc79e4e90bf2708aaf1f2195f8 Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Wed, 3 Apr 2024 14:15:49 +0800
Subject: [PATCH 10/11] Feature: Add script to convert Labelme labels to custom
 dataset format

---
 requirements.txt        |   1 +
 utils/check_datasets.py | 112 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 113 insertions(+)
 create mode 100644 utils/check_datasets.py

diff --git a/requirements.txt b/requirements.txt
index 051e7f2..6ea7569 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,3 +8,4 @@ tensorboard
 onnx
 onnxsim
 onnxruntime
+labelme
diff --git a/utils/check_datasets.py b/utils/check_datasets.py
new file mode 100644
index 0000000..529709a
--- /dev/null
+++ b/utils/check_datasets.py
@@ -0,0 +1,112 @@
+import os
+import shutil
+import argparse
+import random
+random.seed(0)
+
+import cv2
+from tqdm import tqdm
+import numpy as np
+import labelme
+from PIL import Image
+
+
+def check_semantic_segmentation_datasets(datasets_path):
+    out_path = datasets_path
+    labels_path = os.path.join(out_path, 'labels')
+    if not os.path.exists(labels_path):
+        print('Error: %s not found' % (labels_path))
+        return
+    datasets_root = os.path.join(datasets_path, 'out')
+
+    datasets_train = os.path.join(datasets_root, 'train')
+    datasets_val = os.path.join(datasets_root, 'val')
+
+    datasets_train_imgs = os.path.join(datasets_train, 'imgs')
+    datasets_train_masks = os.path.join(datasets_train, 'masks')
+    datasets_val_imgs = os.path.join(datasets_val, 'imgs')
+    datasets_val_masks = os.path.join(datasets_val, 'masks')
+
+    if os.path.exists(datasets_root):
+        shutil.rmtree(datasets_root)
+
+    for d in [datasets_root, datasets_train, datasets_val, datasets_train_imgs, datasets_train_masks, datasets_val_imgs, datasets_val_masks]:
+        if not os.path.exists(d):
+            os.makedirs(d)
+
+    all_data = [i for i in os.listdir(labels_path) if os.path.splitext(i)[1] == '.json']
+    print('all_data: ', len(all_data))
+
+    print(all_data[:5])
+    random.shuffle(all_data)
+    print(all_data[:5])
+
+    train_factor = 0.95
+    train_num = round(train_factor * len(all_data))
+
+    class_name_to_id = {'_background': 0}
+
+    for i in tqdm(all_data):
+        filename = os.path.splitext(os.path.basename(i))[0]
+        label_file = labelme.LabelFile(filename=os.path.join(labels_path, i))
+        for shape in label_file.shapes:
+            if shape.get('shape_type', '') == 'polygon':
+                if shape.get('label', 'None') not in class_name_to_id.keys():
+                    class_name_to_id[shape.get('label', 'None')] = len(class_name_to_id)
+    print(class_name_to_id)
+
+    for i in tqdm(all_data[:train_num]):
+        filename = os.path.splitext(os.path.basename(i))[0]
+        label_file = labelme.LabelFile(filename=os.path.join(labels_path, i))
+        img = labelme.utils.img_data_to_arr(label_file.imageData)
+        if img.ndim == 3:
+            img = img[:, :, ::-1]
+        lbl, _ = labelme.utils.shapes_to_label(
+            img_shape=img.shape,
+            shapes=label_file.shapes,
+            label_name_to_value=class_name_to_id,
+        )
+        #cv2.imwrite(os.path.join(imgages_path, filename+'.png'), img)
+        cv2.imencode('.png', img)[1].tofile(os.path.join(datasets_train_imgs, filename+'.png'))
+        #cv2.imwrite(os.path.join(masks_path, filename+'.png'), lbl)
+        cv2.imencode('.png', lbl)[1].tofile(os.path.join(datasets_train_masks, filename+'.png'))
+    
+    for i in tqdm(all_data[train_num:]):
+        filename = os.path.splitext(os.path.basename(i))[0]
+        label_file = labelme.LabelFile(filename=os.path.join(labels_path, i))
+        img = labelme.utils.img_data_to_arr(label_file.imageData)
+        if img.ndim == 3:
+            img = img[:, :, ::-1]
+        lbl, _ = labelme.utils.shapes_to_label(
+            img_shape=img.shape,
+            shapes=label_file.shapes,
+            label_name_to_value=class_name_to_id,
+        )
+        #cv2.imwrite(os.path.join(imgages_path, filename+'.png'), img)
+        cv2.imencode('.png', img)[1].tofile(os.path.join(datasets_val_imgs, filename+'.png'))
+        #cv2.imwrite(os.path.join(masks_path, filename+'.png'), lbl)
+        cv2.imencode('.png', lbl)[1].tofile(os.path.join(datasets_val_masks, filename+'.png'))
+    
+    with open(os.path.join(datasets_root, 'data.yaml'), 'w+', encoding='UTF-8') as yaml_file:
+        yaml_file.write('path: ')
+        yaml_file.write(os.path.abspath(datasets_root))
+        yaml_file.write('\n')
+        
+        yaml_file.write('names: \n')
+        for i in range(len(class_name_to_id)):
+            for k, v in class_name_to_id.items():
+                if v == i:
+                    yaml_file.write('  %d: %s\n' % (i, k))
+
+
+def parse_opt():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--datasets_root', type=str, default="", help="path to datasets root dir.")
+    
+    opt = parser.parse_args()
+    return opt
+
+
+if __name__ == '__main__':
+    opt = parse_opt()
+    check_semantic_segmentation_datasets(opt.datasets_root)
\ No newline at end of file

From 2470fa3a7118bd63b81f13db3bb6d0f15911565a Mon Sep 17 00:00:00 2001
From: acai66 <1779864536@qq.com>
Date: Wed, 3 Apr 2024 14:18:40 +0800
Subject: [PATCH 11/11] Fix: fix typo

---
 README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 4c31fa5..60c7636 100644
--- a/README.md
+++ b/README.md
@@ -196,11 +196,11 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py
 
 ## Knowledge distillation
 
-| Model | Encoder       | Decoder                 | kd_training | mIoU(200 epoch) | mIoU(800 epoch) |
-|:-----:|:-------------:|:-----------------------:|:-----------:|:---------------:|:---------------:|
-| SMP   | DeepLabv3Plus | ResNet-101 <br> teacher | -           | 78.10           | 79.20           |
-| SMP   | DeepLabv3Plus | ResNet-18 <br> student  | False       | 73.97           | 75.90           |
-| SMP   | DeepLabv3Plus | ResNet-18 <br> student  | True        | 75.20           | 76.41           |
+| Model | Encoder                 | Decoder       | kd_training | mIoU(200 epoch) | mIoU(800 epoch) |
+|:-----:|:-----------------------:|:-------------:|:-----------:|:---------------:|:---------------:|
+| SMP   | ResNet-101 <br> teacher | DeepLabv3Plus | -           | 78.10           | 79.20           |
+| SMP   | ResNet-18 <br> student  | DeepLabv3Plus | False       | 73.97           | 75.90           |
+| SMP   | ResNet-18 <br> student  | DeepLabv3Plus | True        | 75.20           | 76.41           |
 
 # Prepare the dataset