I am using Tensorflow's object detection framework. Training and evaluation jobs are going well, but in tensorboard I am only able to see 10 images for the evaluation job. Is there a way to increase this number to look at more images? I tried changing the config file:
eval_config: {
num_examples: 1000
max_evals: 50
}
eval_input_reader: {
tf_record_input_reader {
input_path: "xxx/eval.record"
}
label_map_path: "xxx/label_map.pbtxt"
shuffle: false
num_readers: 1
}
I thought the max_eval
parameter would change this but it doesn't.
This is the command i'm running for the evaluation job:
python ../models/research/object_detection/eval.py \
--logtostderr \
--pipeline_config_path=xxx/ssd.config \
--checkpoint_dir="xxx/train/" \
--eval_dir="xxx/eval"
It should be the
num_visualizations
parameter in youreval_config
(cf.eval.proto
code).I've been able to get this to work in Tensorboard 1.11.0 by editing the object_detection/protos/eval.proto file, then re-running protoc (see the Tensorflow docs). For example, this line in eval.proto would enable 100 examples (instead of the default 10):
This probably has an impact on system memory, browser performance, eval performance, etc.. so use with caution.