Tensorflow Object Detection API depends on the following libraries:
- Protobuf 3.0.0
- Pillow 1.0
- Loose Palazzo Fit Pannkh Fit Pannkh Palazzo Loose Pannkh Palazzo Loose Fit Pannkh Loose Palazzo Pannkh Fit tf Slim (which is included in the "tensorflow/models/research/" checkout)
- Jupyter notebook
- Tensorflow (>=1.9.0)
For detailed steps to install Tensorflow, follow the Tensorflow installation instructions. A typical user can install Tensorflow using one of the following commands:
# For CPU pip install tensorflow # For GPU pip install tensorflow-gpu
The remaining libraries can be installed on Ubuntu 16.04 using via apt-get:
sudo apt-get install protobuf-compiler python-pil python-lxml python-tk pip install --user Cython pip install --user contextlib2 pip install --user jupyter pip install --user matplotlib
Alternatively, users can install dependencies using pip:
pip install --user Cython pip install --user contextlib2 pip install --user pillow pip install --user lxml pip install --user jupyter pip install --user matplotlib
Note: sometimes "sudo apt-get install protobuf-compiler" will install Protobuf 3+ versions for you and some users have issues when using 3.5. If that is your case, try the manual installation.
COCO API installation
Download the cocoapi and copy the pycocotools subfolder to the tensorflow/models/research directory if you are interested in using COCO evaluation metrics. The default metrics are based on those used in Pascal VOC evaluation. To use the COCO object detection metrics add
metrics_set: "coco_detection_metrics" to the
Pannkh Fit Fit Pannkh Loose Fit Palazzo Loose Pannkh Palazzo Palazzo Loose Pannkh Loose Fit Pannkh Palazzo eval_config message in the config file. To use the COCO instance segmentation metrics add
metrics_set: "coco_mask_metrics" to the
eval_config message in the config file.
git clone https://github.com/cocodataset/cocoapi.git cd cocoapi/PythonAPI make cp -r pycocotools <path_to_tensorflow>/models/research/
The Tensorflow Object Detection API uses Protobufs to configure model and training parameters. Before the framework can be used, the Protobuf libraries must be compiled. This should be done by running the following command from the tensorflow/models/research/ directory:
# From tensorflow/models/research/ protoc object_detection/protos/*.proto --python_out=.
Fit Pannkh Palazzo Pannkh Palazzo Fit Palazzo Pannkh Pannkh Loose Loose Loose Fit Fit Palazzo Pannkh Loose Note: If you're getting errors while compiling, you might be using an incompatible protobuf compiler. If that's the case, use the following manual installation
Pink shirts Solly Regular Cotton Allen Casual Casual C0qU8Manual protobuf-compiler installation and usage
Download and install the 3.0 release of protoc, then unzip the file.
# From tensorflow/models/research/ wget -O protobuf.zip https://github.com/google/protobuf/releases/download/v3.0.0/protoc-3.0.0-linux-x86_64.zip unzip protobuf.zip
Run the compilation process again, but use the downloaded version of protoc
# From tensorflow/models/research/ ./bin/protoc object_detection/protos/*.proto --python_out=.
Add Libraries to PYTHONPATH
When running locally, the tensorflow/models/research/ and slim directories should be appended to PYTHONPATH. This can be done by running the following from tensorflow/models/research/:
# From tensorflow/models/research/ export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
Note: This command needs to run from every new terminal you start. If you wish to avoid running this manually, you can add it as a new line to the end of your ~/.bashrc file, replacing `pwd` with the absolute path of tensorflow/models/research on your system.
You can test that you have correctly installed the Tensorflow Object Detection
API by running the following command: