是一个比较好的入门的例子熟悉搭建环境和如何训练

This is a good case for learn TF

几点说明 1.faster config的文件内容见下 ( I modify faster config ,example see below) 2.我在ubuntu 下运行ok，但是windows下提示step 不能是0. Under ubuntu 18 , it's worked, but under windows 10 ,the script display step 0 error 3.fix export model script python object_detection/export_inference_graph.py
--input_type image_tensor
--pipeline_config_path faster_rcnn_resnet101.config
--trained_checkpoint_prefix train/model.ckpt-20572
--output_directory output_inference_graph

Attaction: 3.1 After trained_checkpoint_prefix , you should add the floder name, "TRAIN" 3.2 In train folder, you will see a lot of ckpt file , I choice the second largest ckpt file. If I choice the largest ckp file ,the script report a error.

-rwxrwxrwx 1 vulgate vulgate 277 6月 8 11:55 checkpoint* -rwxrwxrwx 1 vulgate vulgate 15306196 6月 7 09:30 events.out.tfevents.1528334980.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 14753252 6月 7 09:33 events.out.tfevents.1528335171.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 14753248 6月 7 09:34 events.out.tfevents.1528335230.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 19438109 6月 7 09:46 events.out.tfevents.1528335490.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 17017802 6月 7 10:40 events.out.tfevents.1528339039.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 16158208 6月 7 10:42 events.out.tfevents.1528339320.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 37822065 6月 7 13:10 events.out.tfevents.1528344840.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 15580677 6月 7 13:18 events.out.tfevents.1528348710.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 16292127 6月 7 13:23 events.out.tfevents.1528348860.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 24053813 6月 7 17:14 events.out.tfevents.1528361564.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 17893916 6月 8 08:40 events.out.tfevents.1528418212.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 45646638 6月 8 09:58 events.out.tfevents.1528418558.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 59457203 6月 8 11:58 events.out.tfevents.1528423520.vulgatepc* -rwxrwxrwx 1 vulgate vulgate 14852195 6月 8 10:05 graph.pbtxt* -rwxrwxrwx 1 vulgate vulgate 438600352 6月 8 11:15 model.ckpt-16928.data-00000-of-00001* -rwxrwxrwx 1 vulgate vulgate 40511 6月 8 11:15 model.ckpt-16928.index* -rwxrwxrwx 1 vulgate vulgate 8285444 6月 8 11:15 model.ckpt-16928.meta* -rwxrwxrwx 1 vulgate vulgate 438600352 6月 8 11:25 model.ckpt-18139.data-00000-of-00001* -rwxrwxrwx 1 vulgate vulgate 40511 6月 8 11:25 model.ckpt-18139.index* -rwxrwxrwx 1 vulgate vulgate 8285444 6月 8 11:25 model.ckpt-18139.meta* -rwxrwxrwx 1 vulgate vulgate 438600352 6月 8 11:35 model.ckpt-19353.data-00000-of-00001* -rwxrwxrwx 1 vulgate vulgate 40511 6月 8 11:35 model.ckpt-19353.index* -rwxrwxrwx 1 vulgate vulgate 8285444 6月 8 11:35 model.ckpt-19353.meta* -rwxrwxrwx 1 vulgate vulgate 438600352 6月 8 11:45 model.ckpt-20572.data-00000-of-00001* -rwxrwxrwx 1 vulgate vulgate 40511 6月 8 11:45 model.ckpt-20572.index* -rwxrwxrwx 1 vulgate vulgate 8285444 6月 8 11:45 model.ckpt-20572.meta* -rwxrwxrwx 1 vulgate vulgate 438600352 6月 8 11:55 model.ckpt-21784.data-00000-of-00001* -rwxrwxrwx 1 vulgate vulgate 40511 6月 8 11:55 model.ckpt-21784.index* -rwxrwxrwx 1 vulgate vulgate 8285444 6月 8 11:55 model.ckpt-21784.meta*

############start facter RCNN config#######################

Faster R-CNN with Resnet-101 (v1) configured for the Oxford-IIIT Pet Dataset.

Users should configure the fine_tune_checkpoint field in the train config as

well as the label_map_path and input_path fields in the train_input_reader and

eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that

should be configured.

model { faster_rcnn { num_classes: 2 image_resizer { keep_aspect_ratio_resizer { min_dimension: 600 max_dimension: 1024 } } feature_extractor { type: 'faster_rcnn_resnet101' first_stage_features_stride: 16 } first_stage_anchor_generator { grid_anchor_generator { scales: [0.25, 0.5, 1.0, 2.0] aspect_ratios: [0.5, 1.0, 2.0] height_stride: 16 width_stride: 16 } } first_stage_box_predictor_conv_hyperparams { op: CONV regularizer { l2_regularizer { weight: 0.0 } } initializer { truncated_normal_initializer {

      stddev: 0.01
    }
  }
}
first_stage_nms_score_threshold: 0.0
first_stage_nms_iou_threshold: 0.7
first_stage_max_proposals: 300
first_stage_localization_loss_weight: 2.0
first_stage_objectness_loss_weight: 1.0
initial_crop_size: 14
maxpool_kernel_size: 2
maxpool_stride: 2
second_stage_box_predictor {
  mask_rcnn_box_predictor {
    use_dropout: false
    dropout_keep_probability: 1.0
    fc_hyperparams {
      op: FC
      regularizer {
        l2_regularizer {
          weight: 0.0
        }
      }
      initializer {
        variance_scaling_initializer {
          factor: 1.0
          uniform: true
          mode: FAN_AVG
        }
      }
    }
  }
}
second_stage_post_processing {
  batch_non_max_suppression {
    score_threshold: 0.0
    iou_threshold: 0.6
    max_detections_per_class: 100
    max_total_detections: 300
  }
  score_converter: SOFTMAX
}
second_stage_localization_loss_weight: 2.0
second_stage_classification_loss_weight: 1.0

} }

train_config: { batch_size: 1 optimizer { momentum_optimizer: { learning_rate: { manual_step_learning_rate { initial_learning_rate: 0.0003 schedule { step: 0 learning_rate: .0003 } schedule { step: 900000 learning_rate: .00003 } schedule { step: 1200000 learning_rate: .000003 } } } momentum_optimizer_value: 0.9 } use_moving_average: false } gradient_clipping_by_norm: 10.0 ######################################修改了下面这个这个 modify blow ###################################### #fine_tune_checkpoint: "model.ckpt" from_detection_checkpoint: true

Note: The below line limits the training process to 200K steps, which we

empirically found to be sufficient enough to train the pets dataset. This

effectively bypasses the learning rate schedule (the learning rate will

never decay). Remove the below line to train indefinitely.

num_steps: 200000 data_augmentation_options { random_horizontal_flip { } } }

train_input_reader: { tf_record_input_reader { input_path: "train.record" } label_map_path: "annotations/label_map.pbtxt" }

eval_config: { num_examples: 2000

Note: The below line limits the evaluation process to 10 evaluations.

Remove the below line to evaluate indefinitely.

max_evals: 10 }

eval_input_reader: { tf_record_input_reader { input_path: "val.record" } label_map_path: "annotations/label_map.pbtxt" shuffle: false num_readers: 1 }

############end facter RCNN config#######################

Custom Object Detection with TensorFlow

Object detection allows for the recognition, detection, and localization of multiple objects within an image. It provides us a much better understanding of an image as a whole as apposed to just visual recognition.

Why Object Detection?

Installation

First, with python and pip installed, install the scripts requirements:

pip install -r requirements.txt

Then you must compile the Protobuf libraries:

protoc object_detection/protos/*.proto --python_out=.

Add models and models/slim to your PYTHONPATH:

export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

Note: This must be ran every time you open terminal, or added to your ~/.bashrc file.

Usage

1) Create the TensorFlow Records

Run the script:

python object_detection/create_tf_record.py

Once the script finishes running, you will end up with a train.record and a val.record file. This is what we will use to train the model.

2) Download a Base Model

Training an object detector from scratch can take days, even when using multiple GPUs! In order to speed up training, we’ll take an object detector trained on a different dataset, and reuse some of it’s parameters to initialize our new model.

You can find models to download from this model zoo. Each model varies in accuracy and speed. I used faster_rcnn_resnet101_coco for the demo.

Extract the files and move all the model.ckpt to our models directory.

Note: If you don't use faster_rcnn_resnet101_coco, replace faster_rcnn_resnet101.config with the corresponding config file.

3) Train the Model

Run the following script to train the model:

python object_detection/train.py \
        --logtostderr \
        --train_dir=train \
        --pipeline_config_path=faster_rcnn_resnet101.config

4) Export the Inference Graph

When you model is ready depends on your training data, the more data, the more steps you’ll need. My model was pretty solid at ~4.5k steps. Then, at about ~20k steps, it peaked. I even went on and trained it for 200k steps, but it didn’t get any better.

Note: If training takes way to long, read this.

I recommend testing your model every ~5k steps to make sure you’re on the right path.

You can find checkpoints for your model in Custom-Object-Detection/train.

Move the model.ckpt files with the highest number to the root of the repo:

model.ckpt-STEP_NUMBER.data-00000-of-00001
model.ckpt-STEP_NUMBER.index
model.ckpt-STEP_NUMBER.meta

In order to use the model, you first need to convert the checkpoint files (model.ckpt-STEP_NUMBER.*) into a frozen inference graph by running this command:

python object_detection/export_inference_graph.py \
        --input_type image_tensor \
        --pipeline_config_path faster_rcnn_resnet101.config \
        --trained_checkpoint_prefix model.ckpt-STEP_NUMBER \
        --output_directory output_inference_graph

You should see a new output_inference_graph directory with a frozen_inference_graph.pb file.

5) Test the Model

Just run the following command:

python object_detection/object_detection_runner.py

It will run your object detection model found at output_inference_graph/frozen_inference_graph.pb on all the images in the test_images directory and output the results in the output/test_images directory.

Results

Here’s what I got from running my model over all the frames in this clip from Star Wars: The Force Awakens.

License

MIT

vulgatecnn/Custom-Object-Detection