https://github.com/dataxujing/ssd-tensorflow
:art::art::art: SSD-Tensorflow目标检测基于医学影像的消化内镜活检钳
https://github.com/dataxujing/ssd-tensorflow
cv detection python3 ssd tensorflow
Last synced: 11 months ago
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:art::art::art: SSD-Tensorflow目标检测基于医学影像的消化内镜活检钳
- Host: GitHub
- URL: https://github.com/dataxujing/ssd-tensorflow
- Owner: DataXujing
- Created: 2019-12-07T10:25:18.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-07T10:59:22.000Z (over 6 years ago)
- Last Synced: 2025-04-04T13:22:47.826Z (about 1 year ago)
- Topics: cv, detection, python3, ssd, tensorflow
- Language: Python
- Homepage:
- Size: 361 KB
- Stars: 4
- Watchers: 2
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## **SSD**目标检测内镜活检钳
**XuJing**
### 1.:bookmark:数据标注及准备
数据来源公司业务数据(**因涉及数据安全及隐私数据，不能公开！！！**),我们使用[LabelImg](https://github.com/tzutalin/labelImg)标注我们的数据。标注好的数据我们存放在`data`文件夹下，其文件结构如下：
+ step1: 数据准备
```
data
├─tfrecord # 生成的tfrecord
└─train # 准备的VOC样式的数据集
├─Annotations # 标注的训练集的标注文件
└─JPEGImages # 标注的图像文件
```
+ step2: 修改和数据相关的代码
```
#修改dataset/convert_common.py
VOC_LABELS = {
'Background': (0, 'Background'),
'biopsy forceps': (1, 'biopsy forceps')
}
data_splits_num = {
'train': 22136,
'val': 4952,
}
```
修改train.py
# hardware related configuration
tf.app.flags.DEFINE_integer(
'num_readers', 8,
'The number of parallel readers that read data from the dataset.')
tf.app.flags.DEFINE_integer(
'num_preprocessing_threads', 24,
'The number of threads used to create the batches.')
tf.app.flags.DEFINE_integer(
'num_cpu_threads', 0,
'The number of cpu cores used to train.')
tf.app.flags.DEFINE_float(
'gpu_memory_fraction', 0.9, 'GPU memory fraction to use.')
# scaffold related configuration
tf.app.flags.DEFINE_string(
'data_dir', './data/tfrecord',
'The directory where the dataset input data is stored.')
tf.app.flags.DEFINE_integer( # 类别个数包含背景
'num_classes', 2, 'Number of classes to use in the dataset.')
tf.app.flags.DEFINE_string(
'model_dir', './logs/',
'The directory where the model will be stored.')
tf.app.flags.DEFINE_integer(
'log_every_n_steps', 10,
'The frequency with which logs are printed.')
tf.app.flags.DEFINE_integer(
'save_summary_steps', 500,
'The frequency with which summaries are saved, in seconds.')
tf.app.flags.DEFINE_integer(
'save_checkpoints_secs', 7200,
'The frequency with which the model is saved, in seconds.')
# model related configuration
tf.app.flags.DEFINE_integer(
'train_image_size', 300,
'The size of the input image for the model to use.')
tf.app.flags.DEFINE_integer(
'train_epochs', None,
'The number of epochs to use for training.')
tf.app.flags.DEFINE_integer(
'max_number_of_steps', 120000,
'The max number of steps to use for training.')
tf.app.flags.DEFINE_integer(
'batch_size', 32,
'Batch size for training and evaluation.')
tf.app.flags.DEFINE_string(
'data_format', 'channels_first', # 'channels_first' or 'channels_last'
'A flag to override the data format used in the model. channels_first '
'provides a performance boost on GPU but is not always compatible '
'with CPU. If left unspecified, the data format will be chosen '
'automatically based on whether TensorFlow was built for CPU or GPU.')
tf.app.flags.DEFINE_float(
'negative_ratio', 3., 'Negative ratio in the loss function.')
tf.app.flags.DEFINE_float(
'match_threshold', 0.5, 'Matching threshold in the loss function.')
tf.app.flags.DEFINE_float(
'neg_threshold', 0.5, 'Matching threshold for the negtive examples in the loss function.')
# optimizer related configuration
tf.app.flags.DEFINE_integer(
'tf_random_seed', 20180503, 'Random seed for TensorFlow initializers.')
tf.app.flags.DEFINE_float(
'weight_decay', 5e-4, 'The weight decay on the model weights.')
tf.app.flags.DEFINE_float(
'momentum', 0.9,
'The momentum for the MomentumOptimizer and RMSPropOptimizer.')
tf.app.flags.DEFINE_float('learning_rate', 1e-3, 'Initial learning rate.')
tf.app.flags.DEFINE_float(
'end_learning_rate', 0.000001,
'The minimal end learning rate used by a polynomial decay learning rate.')
# for learning rate piecewise_constant decay
tf.app.flags.DEFINE_string(
'decay_boundaries', '2000, 80000, 100000',
'Learning rate decay boundaries by global_step (comma-separated list).')
tf.app.flags.DEFINE_string(
'lr_decay_factors', '0.1, 1, 0.1, 0.01',
'The values of learning_rate decay factor for each segment between boundaries (comma-separated list).')
# checkpoint related configuration
tf.app.flags.DEFINE_string(
'checkpoint_path', './model',
'The path to a checkpoint from which to fine-tune.')
tf.app.flags.DEFINE_string(
'checkpoint_model_scope', 'vgg_16',
'Model scope in the checkpoint. None if the same as the trained model.')
tf.app.flags.DEFINE_string(
'model_scope', 'ssd300',
'Model scope name used to replace the name_scope in checkpoint.')
tf.app.flags.DEFINE_string(
'checkpoint_exclude_scopes', 'ssd300/multibox_head, ssd300/additional_layers, ssd300/conv4_3_scale',
'Comma-separated list of scopes of variables to exclude when restoring from a checkpoint.')
tf.app.flags.DEFINE_boolean(
'ignore_missing_vars', True,
'When restoring a checkpoint would ignore missing variables.')
tf.app.flags.DEFINE_boolean(
'multi_gpu', True,
'Whether there is GPU to use for training.')
+ setp3: 生成tfrecord
```
python3 dataset/convert_tfrecords.py --dataset_directory=data
--output_directory=./data/tfrecord
```
如果想训练自己的数据集可以将标注的数据存放在上述文件结构中，这样SSD的训练模型的数据集即准备完成。
### 2.:hammer:模型准备
我们使用[SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325) ,使用Tensorflow 1.0深度学习框架。如果你想使用原paper的代码，请参考[Official Code](https://github.com/weiliu89/caffe/tree/ssd). 该框架提供了：
+ VGG-16的预训练模型(SSD300-VGG16)
+ 使用Tensorflow的高级API [tf.estimator](https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator)，并且我们推荐你使用该高级API.
+ 该框架全部使用Tensorflow实现.
+ 使用和官方相同的数据增强策略.
+ 使用[tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers)，类似于Pytorch一样的易读.
+ 支持多GPU并行.
+ 使用Bounding Box的绝对坐标而不是标准化后的坐标[here](https://github.com/HiKapok/SSD.TensorFlow/tree/AbsoluteCoord).
> :fire: 下载预训练的模型[VGG-16](https://github.com/DataXujing/SSD-Tensorflow/releases/tag/v1.0)
把下载的预训练的VGG16拷贝到`./model/pretrain`下，这样我们就准备好训练自己数据的全部过程。
### 3.:chart_with_upwards_trend:模型训练
```
python3 train_ssd.py
```
### 4.:whale:模型测试
+ 图像测试
```
python3 test_img.py
```
+ 视频测试
```
python3 test_video.py
```
### 5.:globe_with_meridians:DEMO
### 6.:fire:目前存在的一些问题
+ 目前并不支持SSD-512
+ 目前并不支持其他的backbone
+ 训练过程中Nan Loss： 增加warm-up
```
tf.app.flags.DEFINE_string(
'decay_boundaries', '2000, 80000, 100000',
'Learning rate decay boundaries by global_step (comma-separated list).')
tf.app.flags.DEFINE_string(
'lr_decay_factors', '0.1, 1, 0.1, 0.01',
'The values of learning_rate decay factor for each segment between boundaries (comma-separated list).')
```
### 6.:tada:致谢
|Name | Url|
|:-----|:------|
|:white_check_mark:Changan Wang|https://github.com/HiKapok/SSD.TensorFlow|
|:white_check_mark:Medcare|http://www.medicon.com.cn/|