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38
config.py
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38
config.py
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"""
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网络相关配置文件
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"""
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# 预训练权重
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weight_model_path = './vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'
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# 输入图像大小，52*52*3
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input_shape = (52,52,3)
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# 如果不使用GPU训练，则传入列表，多个GPU则传入多个编号
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gpu = []
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# gpu = [0,1,2,3]
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# 训练ecoph数
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epochs = 3000
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# 批次大小
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batch_size = 128
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# 学习率
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lr = 1e-3
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# 是否开启tensorboard
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tensorboard = True
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# tensorboard日志目录
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tensorboard_log_dir = './logs'
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# 验证集比例
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valid_rate = 0.05
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sample_path = './sample'
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auto_best_checkpoint_path = './checkpoint/best.h5'
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auto_epoch_checkpoint_path = './checkpoint/epoch_{epoch:03d}.h5'
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model_save_path = './model.h5'
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pre.py
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pre.py
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import cv2
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import numpy as np
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from keras.models import load_model
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from keras.layers import Lambda
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from keras import backend as K
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import os
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import random
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from PIL import Image
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input1 = input('请输入图片名称1：')
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input2 = input('请输入图片名称2：')
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output = Lambda(lambda x: K.abs(x[0] - x[1]))
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weight = "./best.h5"
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# 加载模型
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# model = load_model(weight, custom_objects={'contrastive_loss': contrastive_loss, 'binary_accuracy': binary_accuracy})
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model = load_model(weight, custom_objects={'output': output})
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resize = 52
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img1 = cv2.imread(input1)
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img2 = cv2.imread(input2)
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img1 = cv2.resize(img1, (resize, resize)) / 255
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img2 = cv2.resize(img2, (resize, resize)) / 255
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img1 = np.expand_dims(img1, axis=0)
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img2 = np.expand_dims(img2, axis=0)
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result = model.predict([img1, img2])
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print(result)
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182
predict.py
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predict.py
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import cv2
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import numpy as np
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from keras.models import load_model
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from keras.layers import Lambda
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from keras import backend as K
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import os
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import random
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from PIL import Image
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# 要预测的图片
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# image_path = "./sample/1691156257961.jpg"
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image_path = os.listdir("./data")
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# 随机选取一张图片
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# inp = input('请输入图片名称：')
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image_path = "./data/" + random.choice(image_path)
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# image_path = './sample/' + inp
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# print(image_path)
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"""
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YOLOv3 分割模型
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"""
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weight = "./yolov3-tiny_17000.weights"
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cfg = "./yolov3-tiny.cfg"
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# 加载模型
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net = cv2.dnn.readNet(weight, cfg)
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"""
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孪生网络 对比模型
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"""
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resize_height, resize_width,channel = 52,52,3
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# 自定义的损失和精度
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output = Lambda(lambda x: K.abs(x[0] - x[1]))
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def contrastive_loss(y_true, y_pred):
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margin = 1
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return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
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def binary_accuracy(y_true, y_pred):
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return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))
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weight = "./best.h5"
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# 加载模型
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# model = load_model(weight, custom_objects={'contrastive_loss': contrastive_loss, 'binary_accuracy': binary_accuracy})
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model = load_model(weight, custom_objects={'output': output})
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classes = ["text"]
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img = cv2.imread(image_path)
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cv2.namedWindow('display')
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"""
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YOLO 分割出内容
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"""
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height, width, channels = img.shape
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blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
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net.setInput(blob)
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outs = net.forward(net.getUnconnectedOutLayersNames())
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class_ids = []
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confidences = []
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boxes = []
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for out in outs:
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for detection in out:
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scores = detection[5:]
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class_id = np.argmax(scores)
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confidence = scores[class_id]
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if confidence > 0.1:
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center_x = int(detection[0] * width)
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center_y = int(detection[1] * height)
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w = int(detection[2] * width)
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h = int(detection[3] * height)
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x = int(center_x - w / 2)
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y = int(center_y - h / 2)
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boxes.append([x, y, w, h])
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confidences.append(float(confidence))
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class_ids.append(class_id)
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indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.1, 0.1)
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thickness = 2
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color = (0, 255, 0)
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font = cv2.FONT_HERSHEY_PLAIN
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new_boxes = []
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for i in range(len(boxes)):
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if i in indexes:
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x, y, w, h = boxes[i]
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new_boxes.append([x, y, w, h])
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total = len(new_boxes)
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print('>>> 检测出：', total,'个字符')
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top_total = total / 2
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# 如果取出来有小数
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if top_total % 1 != 0:
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print('>>> YOLO分割有误，顶部字符数量与点选数量不匹配')
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exit()
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top_total = int(top_total)
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# 取出w最大的（需要点选的）
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w_max_boxes = sorted(new_boxes, key=lambda x: x[2], reverse=True)[:top_total]
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# 取出剩下的（需要对比的）
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w_min_box = sorted(new_boxes, key=lambda x: x[2], reverse=True)[top_total:]
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# 按照从左到右排序w_min_box
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w_min_box = sorted(w_min_box, key=lambda x: x[0])
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w_max_image = []
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w_min_image = []
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# 分割出具体图像
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for i in range(top_total):
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x, y, w, h = w_max_boxes[i]
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p = cv2.resize(img[y:y+h, x:x+w], (resize_height, resize_width))
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# cv2.imwrite('./1/1_{}.jpg'.format(i), p)
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w_max_image.append(p)
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for i in range(len(w_min_box)):
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x, y, w, h = w_min_box[i]
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p = cv2.resize(img[y:y+h, x:x+w], (resize_height, resize_width))
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# cv2.imwrite('./1/2_{}.jpg'.format(i), p)
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w_min_image.append(p)
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# print(w_max_boxes)
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w_max_image_np = np.array(w_max_image) / 255
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w_min_image_np = np.array(w_min_image) / 255
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select_index = []
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# 开始挨个对比，取出最相似的
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for i in range(len(w_max_image_np)):
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print('>>> 开始对比第', i+1, '个字符')
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cv2.imwrite('./1/1_{}.jpg'.format(i), w_max_image_np[i] * 255)
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left_x = w_max_image_np[i]
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num_index = 0
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cache_rate = 0
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for k in range(len(w_min_image_np)):
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left_y = w_min_image_np[k]
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predict = model.predict([left_x.reshape(1, resize_height, resize_width, channel), left_y.reshape(1, resize_height, resize_width, channel)])
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rate = predict[0][0]
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if rate > cache_rate:
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cv2.imwrite('./1/2_{}.jpg'.format(i), w_min_image_np[k] * 255)
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num_index = k
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cache_rate = rate
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select_index.append(num_index)
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print('>>> 对比完成，结果为：', select_index)
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location = []
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for i in range(len(select_index)):
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x, y, w, h = w_max_boxes[select_index[i]]
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cv2.rectangle(img, (x, y), (x+w, y+h), color, thickness)
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cv2.putText(img, str(i+1), (x, y+h), font, 1, color, thickness)
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# 转换为图片坐标中心点
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location.append([x+w/2, y+h/2])
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print('>>> 位置坐标：', location)
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cv2.imshow('display', img)
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cv2.waitKey(0)
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BIN
requirement.txt
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BIN
requirement.txt
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siamese.py
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siamese.py
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from tensorflow.keras.layers import Input,Lambda,Dense
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from tensorflow.keras.models import Model
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from vgg16 import VGG16
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def siamese(input_shape):
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# 基础VGG，因为两个网络共享参数，所以需要是同一个实例
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base_network = VGG16()
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left_input = Input(shape = input_shape)
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right_input = Input(shape = input_shape)
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left_output = base_network.call(left_input)
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right_output = base_network.call(right_input)
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# l1距离
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output = Lambda(lambda x:abs(x[0]-x[1]))([left_output,right_output])
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# 全连接层1
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output = Dense(512,activation = 'relu')(output)
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# 全连接层2
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output = Dense(1,activation = 'sigmoid')(output)
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return Model([left_input,right_input],output)
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train.py
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train.py
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import os
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import config
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import tensorflow as tf
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from siamese import siamese
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from tensorflow.python.keras.utils.multi_gpu_utils import multi_gpu_model
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from tensorflow.keras.optimizers import SGD
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from tensorflow.keras.callbacks import *
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import cv2
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import random
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import numpy as np
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import utils
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if len(config.gpu) >= 1:
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# 设置显存分配方式
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gpus = tf.config.experimental.list_physical_devices('GPU')
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(i) for i in config.gpu)
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print('>>> GPU:',os.environ['CUDA_VISIBLE_DEVICES'])
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input_shape = config.input_shape
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model = siamese(input_shape=(input_shape[0],input_shape[1],input_shape[2]))
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if config.weight_model_path != False:
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"""
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by_name=True：设置为 True 时，表示根据层的名称进行权重的匹配和加载。这意味着只有具有相同名称的层才会被加载权重，其他层将被忽略。这在微调模型时非常有用，可以只加载与预训练权重文件中的层名称匹配的部分权重。
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skip_mismatch=True：设置为 True 时，表示如果层数量不匹配或找不到对应的层，则跳过权重加载的错误。这在模型的结构发生变化时很有用，可以避免由于层数不匹配而导致的加载错误。请注意，在层数量不匹配的情况下，任何未匹配的层都不会加载权重。
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"""
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model.load_weights(config.weight_model_path,by_name=True,skip_mismatch=True)
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# 如果是多个GPU需要设置
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if len(config.gpu) > 1:
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model = multi_gpu_model(model,gpus=len(config.gpu))
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opt = SGD(lr=config.lr,momentum=0.9)
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min_lr = config.lr * 0.01
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nbs = 64
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lr_limit_max = 1e-3
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lr_limit_min = 3e-4
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Init_lr_fit = min(max(config.batch_size / nbs * config.lr, lr_limit_min), lr_limit_max)
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Min_lr_fit = min(max(config.batch_size / nbs * min_lr, lr_limit_min * 1e-2), lr_limit_max * 1e-2)
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# 准备数据
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sample = os.listdir(config.sample_path)
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data = []
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hasOne = []
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for i in sample:
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t = i.split('_')[0]
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if t not in hasOne:
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hasOne.append(t)
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data.append([1, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + t + '_2.jpg'])
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# 需要做数据增强的部分，到时候会把2转换成1
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data.append([2, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + t + '_2.jpg'])
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data.append([2, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + t + '_2.jpg'])
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data.append([2, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + t + '_2.jpg'])
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# 随机负样本
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for j in range(3):
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f = random.choice(sample)
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while f.split('_')[0] == t:
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f = random.choice(sample)
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data.append([0, config.sample_path + '/' + t + '_1.jpg', config.sample_path + '/' + f])
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# 打乱数据
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random.shuffle(data)
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train_x_left = np.zeros((len(data),input_shape[0],input_shape[1],input_shape[2]))
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train_x_right = np.zeros((len(data),input_shape[0],input_shape[1],input_shape[2]))
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train_y = np.zeros((len(data),1))
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total = len(data)
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now = 0
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for i in data:
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now += 1
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left = cv2.imread(i[1])
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left = cv2.resize(left,(input_shape[0],input_shape[1]))
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right = cv2.imread(i[2])
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right = cv2.resize(right,(input_shape[0],input_shape[1]))
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label = i[0]
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# 数据增强操作
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if label == 2:
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label = 1
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# 随机翻转
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if random.random() > 0.5:
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left = cv2.flip(left,1)
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right = cv2.flip(right,1)
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# 随机噪声
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for h in range(5):
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# 加上随机线条
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if random.random() > 0.5:
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cv2.line(left,(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),(0,0,0),random.randint(1,3))
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cv2.line(right,(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),(0,0,0),random.randint(1,3))
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# 加上随机点
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if random.random() > 0.5:
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cv2.circle(left,(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),random.randint(1,3),(0,0,0),-1)
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cv2.circle(right,(random.randint(0,input_shape[0]),random.randint(0,input_shape[1])),random.randint(1,3),(0,0,0),-1)
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# 色域变换
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if random.random() > 0.5:
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left = cv2.cvtColor(left,cv2.COLOR_BGR2HSV)
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right = cv2.cvtColor(right,cv2.COLOR_BGR2HSV)
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left[:,:,0] = left[:,:,0] + random.randint(-10,10)
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right[:,:,0] = right[:,:,0] + random.randint(-10,10)
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left = cv2.cvtColor(left,cv2.COLOR_HSV2BGR)
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right = cv2.cvtColor(right,cv2.COLOR_HSV2BGR)
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if not os.path.exists('./1.jpg'):
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cv2.imwrite('./1.jpg',left)
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cv2.imwrite('./2.jpg',right)
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# 归一化
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left = left / 255.0
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right = right / 255.0
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train_x_left[now-1] = left
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train_x_right[now-1] = right
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train_y[now-1] = label
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print('>>> 正在处理第',now,'/',total,'张图片',end='\r')
|
||||
|
||||
|
||||
lr_scheduler_func = utils.get_lr_scheduler('cos', Init_lr_fit, Min_lr_fit, config.epochs)
|
||||
|
||||
if config.tensorboard:
|
||||
tb = TensorBoard(log_dir=config.tensorboard_log_dir,write_graph=True,write_images=True)
|
||||
|
||||
best_checkpoint = ModelCheckpoint(config.auto_best_checkpoint_path,save_weights_only=False,save_best_only=True,verbose=1,period=1)
|
||||
epoch_checkpoint = ModelCheckpoint(config.auto_epoch_checkpoint_path,save_weights_only=False,save_best_only=False,verbose=1,period=10)
|
||||
lr_scheduler = LearningRateScheduler(lr_scheduler_func, verbose=1)
|
||||
|
||||
callback = [best_checkpoint,epoch_checkpoint,lr_scheduler]
|
||||
|
||||
if config.tensorboard:
|
||||
callback.append(tb)
|
||||
|
||||
|
||||
model.compile(loss='binary_crossentropy',optimizer=opt,metrics=['binary_accuracy'])
|
||||
|
||||
|
||||
model.fit([train_x_left,train_x_right],train_y,batch_size=config.batch_size,epochs=config.epochs,validation_split=config.valid_rate,callbacks=callback)
|
||||
model.save_weights(config.model_save_path)
|
||||
41
utils.py
Normal file
41
utils.py
Normal file
@ -0,0 +1,41 @@
|
||||
import math
|
||||
from functools import partial
|
||||
|
||||
def get_lr_scheduler(lr_decay_type, lr, min_lr, total_iters, warmup_iters_ratio = 0.05, warmup_lr_ratio = 0.1, no_aug_iter_ratio = 0.05, step_num = 10):
|
||||
def yolox_warm_cos_lr(lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter, iters):
|
||||
if iters <= warmup_total_iters:
|
||||
# lr = (lr - warmup_lr_start) * iters / float(warmup_total_iters) + warmup_lr_start
|
||||
lr = (lr - warmup_lr_start) * pow(iters / float(warmup_total_iters), 2
|
||||
) + warmup_lr_start
|
||||
elif iters >= total_iters - no_aug_iter:
|
||||
lr = min_lr
|
||||
else:
|
||||
lr = min_lr + 0.5 * (lr - min_lr) * (
|
||||
1.0
|
||||
+ math.cos(
|
||||
math.pi
|
||||
* (iters - warmup_total_iters)
|
||||
/ (total_iters - warmup_total_iters - no_aug_iter)
|
||||
)
|
||||
)
|
||||
return lr
|
||||
|
||||
def step_lr(lr, decay_rate, step_size, iters):
|
||||
if step_size < 1:
|
||||
raise ValueError("step_size must above 1.")
|
||||
n = iters // step_size
|
||||
out_lr = lr * decay_rate ** n
|
||||
return out_lr
|
||||
|
||||
if lr_decay_type == "cos":
|
||||
warmup_total_iters = min(max(warmup_iters_ratio * total_iters, 1), 3)
|
||||
warmup_lr_start = max(warmup_lr_ratio * lr, 1e-6)
|
||||
no_aug_iter = min(max(no_aug_iter_ratio * total_iters, 1), 15)
|
||||
func = partial(yolox_warm_cos_lr ,lr, min_lr, total_iters, warmup_total_iters, warmup_lr_start, no_aug_iter)
|
||||
else:
|
||||
decay_rate = (min_lr / lr) ** (1 / (step_num - 1))
|
||||
step_size = total_iters / step_num
|
||||
func = partial(step_lr, lr, decay_rate, step_size)
|
||||
|
||||
return func
|
||||
|
||||
72
vgg16.py
Normal file
72
vgg16.py
Normal file
@ -0,0 +1,72 @@
|
||||
from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D
|
||||
"""
|
||||
VGG16代码来源于https://github.com/bubbliiiing/Siamese-keras
|
||||
|
||||
也可以使用tensorflow.keras.applications.VGG16实现
|
||||
"""
|
||||
|
||||
class VGG16:
|
||||
def __init__(self):
|
||||
# 第一个卷积部分
|
||||
# 105, 105, 3 -> 105, 105, 64 -> 52, 52, 64
|
||||
self.block1_conv1 = Conv2D(64, (3,3), activation = 'relu', padding = 'same',name = 'block1_conv1')
|
||||
self.block1_conv2 = Conv2D(64, (3,3), activation = 'relu', padding = 'same',name = 'block1_conv2')
|
||||
self.block1_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block1_pool')
|
||||
|
||||
# 第二个卷积部分
|
||||
# 52, 52, 64 -> 52, 52, 128 -> 26, 26, 128
|
||||
self.block2_conv1 = Conv2D(128, (3,3), activation = 'relu', padding = 'same',name = 'block2_conv1')
|
||||
self.block2_conv2 = Conv2D(128, (3,3), activation = 'relu', padding = 'same',name = 'block2_conv2')
|
||||
self.block2_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block2_pool')
|
||||
|
||||
# 第三个卷积部分
|
||||
# 26, 26, 128-> 26, 26, 256 -> 13, 13, 256
|
||||
self.block3_conv1 = Conv2D(256, (3,3), activation = 'relu', padding = 'same',name = 'block3_conv1')
|
||||
self.block3_conv2 = Conv2D(256, (3,3), activation = 'relu', padding = 'same',name = 'block3_conv2')
|
||||
self.block3_conv3 = Conv2D(256, (3,3), activation = 'relu', padding = 'same',name = 'block3_conv3')
|
||||
self.block3_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block3_pool')
|
||||
|
||||
# 第四个卷积部分
|
||||
# 13, 13, 256-> 13, 13, 512 -> 6, 6, 512
|
||||
self.block4_conv1 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block4_conv1')
|
||||
self.block4_conv2 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block4_conv2')
|
||||
self.block4_conv3 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block4_conv3')
|
||||
self.block4_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block4_pool')
|
||||
|
||||
# 第五个卷积部分
|
||||
# 6, 6, 512-> 6, 6, 512 -> 3, 3, 512
|
||||
self.block5_conv1 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block5_conv1')
|
||||
self.block5_conv2 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block5_conv2')
|
||||
self.block5_conv3 = Conv2D(512, (3,3), activation = 'relu', padding = 'same', name = 'block5_conv3')
|
||||
self.block5_pool = MaxPooling2D((2,2), strides = (2,2), name = 'block5_pool')
|
||||
|
||||
# 3*3*512 = 4500 + 90 + 18 = 4608
|
||||
self.flatten = Flatten(name = 'flatten')
|
||||
|
||||
def call(self, inputs):
|
||||
x = inputs
|
||||
x = self.block1_conv1(x)
|
||||
x = self.block1_conv2(x)
|
||||
x = self.block1_pool(x)
|
||||
|
||||
x = self.block2_conv1(x)
|
||||
x = self.block2_conv2(x)
|
||||
x = self.block2_pool(x)
|
||||
|
||||
x = self.block3_conv1(x)
|
||||
x = self.block3_conv2(x)
|
||||
x = self.block3_conv3(x)
|
||||
x = self.block3_pool(x)
|
||||
|
||||
x = self.block4_conv1(x)
|
||||
x = self.block4_conv2(x)
|
||||
x = self.block4_conv3(x)
|
||||
x = self.block4_pool(x)
|
||||
|
||||
x = self.block5_conv1(x)
|
||||
x = self.block5_conv2(x)
|
||||
x = self.block5_conv3(x)
|
||||
x = self.block5_pool(x)
|
||||
|
||||
outputs = self.flatten(x)
|
||||
return outputs
|
||||
182
yolov3-tiny.cfg
Normal file
182
yolov3-tiny.cfg
Normal file
@ -0,0 +1,182 @@
|
||||
[net]
|
||||
# Testing
|
||||
# batch=1
|
||||
# subdivisions=1
|
||||
# Training
|
||||
batch=64
|
||||
subdivisions=8
|
||||
width=416
|
||||
height=416
|
||||
channels=3
|
||||
momentum=0.9
|
||||
decay=0.0005
|
||||
angle=0
|
||||
saturation = 1.5
|
||||
exposure = 1.5
|
||||
hue=.1
|
||||
|
||||
learning_rate=0.001
|
||||
burn_in=1000
|
||||
max_batches = 30000
|
||||
policy=steps
|
||||
steps=400000,450000
|
||||
scales=.1,.1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=16
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=32
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=64
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=2
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[maxpool]
|
||||
size=2
|
||||
stride=1
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=1024
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
###########
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=512
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
|
||||
|
||||
[yolo]
|
||||
mask = 3,4,5
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=1
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
|
||||
[route]
|
||||
layers = -4
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=128
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[upsample]
|
||||
stride=2
|
||||
|
||||
[route]
|
||||
layers = -1, 8
|
||||
|
||||
[convolutional]
|
||||
batch_normalize=1
|
||||
filters=256
|
||||
size=3
|
||||
stride=1
|
||||
pad=1
|
||||
activation=leaky
|
||||
|
||||
[convolutional]
|
||||
size=1
|
||||
stride=1
|
||||
pad=1
|
||||
filters=18
|
||||
activation=linear
|
||||
|
||||
[yolo]
|
||||
mask = 0,1,2
|
||||
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
|
||||
classes=1
|
||||
num=6
|
||||
jitter=.3
|
||||
ignore_thresh = .7
|
||||
truth_thresh = 1
|
||||
random=1
|
||||
Loading…
Reference in New Issue
Block a user