2017-05-30 81 views
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我已经使用标准SVHN裁剪数字数据集来生成一个模型,该模型可分为10个可能的数字,测试集的准确率为89.89%。继续前进,我想检测图像上的多个数字。 (例如汽车登记牌上的号码)我会如何去做这件事?我是否需要重新训练我的模型以检测多个图像?Tensorflow - 在训练好的softmax分类模型上检测多个对象

#conv1 
W_conv1 = weight_variable([5, 5, 1, 32]) 
b_conv1 = bias_variable([32]) 
x_image = tf.reshape(x, [-1,32,32,1]) 
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 
h_pool1 = max_pool_2x2(h_conv1) 

#conv2 
W_conv2 = weight_variable([5, 5, 32, 64]) 
b_conv2 = bias_variable([64]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) 
h_pool2 = max_pool_2x2(h_conv2) 

#Densely 
W_fc1 = weight_variable([8 * 8 * 64, 1024]) 
b_fc1 = bias_variable([1024]) 

h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 

#Dropout 
keep_prob = tf.placeholder(tf.float32) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 

#Readout 
W_fc2 = weight_variable([1024, 10]) 
b_fc2 = bias_variable([10]) 
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 

#Train 
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) 
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
sess.run(tf.global_variables_initializer()) 
for i in range(40000): 
    batch = shvn_data.nextbatch(100) 
    if i%100 == 0: 
    train_accuracy = accuracy.eval(feed_dict={ 
     x:batch[0], y_: batch[1], keep_prob: 1.0}) 
    print("step %d, training accuracy %f"%(i, train_accuracy)) 
    train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) 

我的代码从这里修改为:https://www.tensorflow.org/get_started/mnist/pros。我的代码可以在这里找到:https://github.com/limwenyao/ComputerVision/blob/testing/CNN_MNIST.py#L216

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读一个数字在此处添加代码,不要让我们跟随外部链接 –

回答

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你会围绕你的网包装一个跨步系统。因此,您可以将图像与车牌一起拍摄下来,然后将其剪切成许多较小的图像,然后在每个较小的图像上运行数字检测并记录找到的数字,最后将它们放在一起并确认您的车牌号码。

将车牌图像切割成较小图像的过程通常也是训练有素的网络。所以,你将有两个网:

  • 一个学会了切好
  • 另获悉,从每个切割子图像
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理解,谢谢! –