The next thing to do is edit the configs.json file
{
"max_trials": 1,
"max_instances_at_once": 1,
"model_priority_space": [10, 0, 0],
"task": "detection",
"data": {
"home_path": "../data/tiny_coco",
"annotation_type": "coco",
"dataset_name": ""
}
}
max_trials - defines the maximum total number of trials that will be tested
max_instances_at_once - defines the number of trials that will run simultaneously, i.e. in parallel to each other and must be smaller than the number of available gpus.
model_priority_space - define the model specs that best suits your priorities.
This is a 3 dimensional vector describing your model preferences in a euclidean vector space. Each element can occupy the space [0,10).
- axis 0 - accuracy
- axis 1 - inference speed
- axis 2 - memory
For example "model_priority_space": [2, 9, 10] indicates a very light but low accuracy model
task - i.e. detection vs classification vs instance segmentation (we currently only support detection)
data - This is an example of how to run on a Coco styled dataset.