I am using the Tensorflow Object Detection API from here Object Detection Models. Based on this tutorial Tensorflow tutorial, I am able to get it running in my local system. However being very slow I decided to try it out on FloydHubs GPU servers. When I issued the run command from the object detection folder, I got the Code size too large restraint
. I then uploaded the data(which is essentially the labels and the tfrecords for the images) as a separate folder. Despite this I need a config file which is used by tensorflow to pick various parameters and in that I need to specify the checkpoint file which is by default 104MB. If I upload this separately, how do I specify the path on the server in my config file. How do I also specify the data path in the server inside my config file since I only need to run the train.py
file using this cmd locally
python train.py --logtostderr --train_dir=training\ --pipeline_config_path=training\sssd_mobilenet_v1_pets.config
These are relevant lines in the config file:
fine_tune_checkpoint: "ssd_mobilenet_v1_coco_11_06_2017/model.ckpt"
eval_input_reader: {
tf_record_input_reader {
input_path: "data/test.record"
}
label_map_path: "data/flower_detect.pbtxt"
shuffle: false
num_readers: 1
}
This is my first time and I would really appreciate a guiding hand since the environment is new to me
Update:
Changed the paths to: https://www.floydhub.com/ashwin4ever/datasets/ssd_mobilenet_v1_coco_11_06_2017/1/training/model.ckpt
. Not sure if this is going to work.
I used the command line version of python train.py
but get an error of the nature ImportError: No module named 'object_detection'
For this to work, the following command must be executed export PYTHONPATH=$PYTHONPATH:pwd:pwd/slim
. I am not sure how to do that in Floydhub. Would really appreciate any response