| # data-config | |
| import numpy as np | |
| train_data_path = './dataset/train/' | |
| train_batch_size_per_gpu = 14 # 14 | |
| num_workers = 24 # 24 | |
| gpu_ids = [0] # [0,1,2,3] | |
| gpu = 1 # 4 | |
| input_size = 512 # 预处理后归一化后图像尺寸 | |
| background_ratio = 3. / 8 # 纯背景样本比例 | |
| random_scale = np.array([0.5, 1, 2.0, 3.0]) # 提取多尺度图片信息 | |
| geometry = 'RBOX' # 选择使用几何特征图类型 | |
| max_image_large_side = 1280 | |
| max_text_size = 800 | |
| min_text_size = 10 | |
| min_crop_side_ratio = 0.1 | |
| means=[100, 100, 100] | |
| pretrained = True # 是否加载基础网络的预训练模型 | |
| pretrained_basemodel_path = 'IndicPhotoOCR/detection/East/tmp/backbone_net/mobilenet_v2.pth.tar' | |
| pre_lr = 1e-4 # 基础网络的初始学习率 | |
| lr = 1e-3 # 后面网络的初始学习率 | |
| decay_steps = 50 # decayed_learning_rate = learning_rate * decay_rate ^ (global_epoch / decay_steps) | |
| decay_rate = 0.97 | |
| init_type = 'xavier' # 网络参数初始化方式 | |
| resume = True # 整体网络是否恢复原来保存的模型 | |
| checkpoint = 'IndicPhotoOCR/detection/East/tmp/epoch_990_checkpoint.pth.tar' # 指定具体路径及文件名 | |
| max_epochs = 1000 # 最大迭代epochs数 | |
| l2_weight_decay = 1e-6 # l2正则化惩罚项权重 | |
| print_freq = 10 # 每10个batch输出损失结果 | |
| save_eval_iteration = 50 # 每10个epoch保存一次模型,并做一次评价 | |
| save_model_path = './tmp/' # 模型保存路径 | |
| test_img_path = './dataset/full_set' # demo测试样本路径'./demo/test_img/'，数据集测试为'./dataset/test/' | |
| res_img_path = 'results' # demo结果存放路径'./demo/result_img/'，数据集测试为 './dataset/test_result/' | |
| write_images = True # 是否输出图像结果 | |
| score_map_thresh = 0.8 # 置信度阈值 | |
| box_thresh = 0.1 # 文本框中置信度平均值的阈值 | |
| nms_thres = 0.2 # 局部非极大抑制IOU阈值 | |
| compute_hmean_path = './dataset/test_compute_hmean/' | |