Tensorboard eval.py IOU for object detection - tensorflow

Question

I used the ssd_mobilenet_v1_coco from detection model zoo in tensorflow object detection. I am currently training the model by running
python legacy/train.py --logtostderr --train_dir=trainingmobile/ --pipeline_config_path=trainingmobile/pipeline.config
I want to run an evaluation job by running eval.py to get other metrics like IOU and PR Curve but I don't know how to do that. I am able to run the command
python legacy/eval.py \
--logtostderr \
--checkpoint_dir= path/to/checkpoint \
--eval_dir= path/to/eval \
--pipeline_config_path= path/to/config
then I ran the command
tensorboard --logdir=path/to/eval
The tensorboard shows only the test image output. How can i get other metrics like IOU and PR Curve?

Answer 1

First of all, I'd highly recommend you to use the newer model_main.py script for training and evaluation combined. You can use it as shown below:
python object_detection/model_main.py \
--pipeline_config_path=path/to/config \
--model_dir=path/to/train_dir \
--num_train_steps=NUM_TRAIN_STEPS \
--num_eval_steps=NUM_EVAL_STEPS \
--alsologtostderr
It combines training and evaluation and you can enter tensorboard with
tensorboard -logdir=path/to/train_dir
Tensorboard will not only disply the training process, it will also show your progress over your validation set. They use the COCO metric as default metric!
To your original problem: Maybe you should change the eval settings in your config file to larger numbers:
eval_config: {
num_examples: 8000
# Note: The below line limits the evaluation process to 10 evaluations.
# Remove the below line to evaluate indefinitely.
max_evals: 10}
If you'll use the model_main.py script, the number of evaluation will be set by the flags.
Good to know: The info output of tnesorflow is disabled in the newer model_main.py script. You can enable it by adding
tf.logging.set_verbosity(tf.logging.INFO)
after the import section.