TensorPack is a neural net training interface on TensorFlow, with focus on speed + flexibility

There are many examples of classic models and tasks provided in the TensorPack repository. This section shows how to use mmeval.COCODetection for evaluation in TensorPack-FasterRCNN, and the related code can be found at mmeval/examples/tensorpack.

First you need to install TensorFlow and TensorPack, then follow the preparation steps in the TensorPack-FasterRCNN example to install the dependencies and prepare the COCO dataset, as well as download the pre-trained model weights to be evaluated.

Scripts for model evaluation are provided in, and the model can be evaluated with the following commands:

./ --evaluate output.json --load /path/to Trained-Model-Checkpoint --config SAME-AS-TRAINING

MMEval provides a evaluation tools for TensorPack-FasterRCNN that use mmeval.COCODetection. This evaluation script needs to be placed in the TensorPack-FasterRCNN example directory, and then the evaluation can be executed with the following command.

# run evaluation
python --load <model_path>

# launch multi-gpus evaluation by mpirun
mpirun -np 8 python --load <model_path>

We tested this evaluation script on COCO-MaskRCNN-R50C41x and got the same evaluation results as the TensorPack report.

Model mAP (box) mAP (mask) Configurations
COCO-MaskRCNN-R50C41x 36.2 31.8 MODE_FPN=False
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