update

Author: xieyufei

.DS_Store

@@ -1,2 +1,38 @@
-# FOTS
-An Implementation of the FOTS: Fast Oriented Text Spotting with a Unified Network
+# FOTS: Fast Oriented Text Spotting with a Unified Network
+
+### Introduction
+This is a pytorch re-implementation of [FOTS: Fast Oriented Text Spotting with a Unified Network](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/1699.pdf).
+The features are summarized blow:
+
++ Only **detection** part is implemented.
+
+### Contents
+1. [Installation](#installation)
+2. [Download](#download)
+3. [Train](#train)
+4. [Test](#test)
+
+
+### Installation
+1. Any version of torch version >= 0.3.1 should be ok.
+
+### Download
+1. Models trained on ICDAR 2015 (training set) + ICDAR 2017 (training set)
+
+### Train
+If you want to train the model, you should provide the dataset path, in the dataset path, a separate gt text file should be provided for each image
+and run
+
+```
+python main_train.py
+
+```
+
+### Test
+run
+```
+python eval.py
+```
+
+a text file will be then written to the output path.
+

README.md

@@ -0,0 +1,60 @@
+import warnings
+class DefaultConfig(object):
+
+    initial_epoch = 0
+    epoch_num = 30000
+    lr = 1e-3
+    decay = 5e-4
+    use_gpu = True
+    batch_size = 64
+    num_workers = 10
+    optmizer = 'RMSprop' # RMSprop,Adam # SGD, SGD
+    betas = (0.5,0.999)
+    epsilon = 1e-4
+    shrink_side_ratio = 0.6
+    shrink_ratio = 0.2
+    model = 'FOTS'
+
+    patience = 2
+    load_weights = False
+    lambda_inside_score_loss = 4.0
+    lambda_side_vertex_code_loss = 1.0
+    lambda_side_vertex_coord_loss = 1.0
+
+    load_model_path = "checkpoints/model.pth"
+    save_path = "save_model/"
+
+
+    total_img = 16243
+
+    validation_split_ratio = 0.1
+    max_train_img_size = 736
+    max_predict_img_size = 2400
+
+    assert max_train_img_size in [256, 384, 512, 640, 736], 'max_train_img_size must in [256~736]'
+
+
+
+
+def parse(self,kwargs):
+    '''
+    update the config params
+    :param self:
+    :param kwargs:
+    :return:
+    '''
+    for k,v in kwargs.items():
+        if not hasattr(self,k):
+            warnings.warn("Warning:opt has not attribute ^s" %k)
+
+        setattr(self,k,v)
+
+    print('use config:')
+    for k,v in self.__class__.__dict__.items():
+        if not k.startswith('__'):
+            print(k,getattr(self,k))
+    print("end the parse!!!")
+
+
+DefaultConfig.parse=parse
+opt=DefaultConfig()