Mmdetection: mmdet - ERROR - The testing results of the whole dataset is empty

[Prakhar-97]

I was trying to train a CascadeMaskRCNN Hrnet model on a custom dataset in my local system with COCO style annotations.

This is the message that I get while training my dataset. On further looking I found that when running the single_gpu_test for validation data, the results are all empty, I am not able to identify the reason for the same. I am new to this library and any help would be appreciated.

[hellock]

There can be many reasons for that since you train the model on a custom dataset, and we cannot debug for you. E.g, the model has not converged, it suffers from overfitting, the threshold is high, the train/test set has much differences.

[Rajat-Mehta]

I am getting the same error while training on custom dataset in coco format

Loading and preparing results...
2020-06-14 23:36:16,926 - mmdet - ERROR - The testing results of the whole dataset is empty.

what might be the reason and how to solve this?

[Prakhar-97]

As you are using a custom dataset in the coco format make sure that you mention about the classes in the config files. This might be one of the reasons
...

dataset settings

dataset_type = 'CocoDataset'
classes = ('a', 'b', 'c', 'd', 'e')
...
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
classes=classes,
ann_file='path/to/your/train/data',
...),
val=dict(
type=dataset_type,
classes=classes,
ann_file='path/to/your/val/data',
...),
test=dict(
type=dataset_type,
classes=classes,
ann_file='path/to/your/test/data',
...))
...

[muhammadabdullah34907]

@Prakhar-97 very well said. thank you

[anonymoussss]

There can be many reasons for that since you train the model on a custom dataset, and we cannot debug for you. E.g, the model has not converged, it suffers from overfitting, the threshold is high, the train/test set has much differences.

Hello, I am using the coco dataset to train a detection model. I want to know why the error "The testing results of the whole dataset is empty" is reported when the model does not converge?

[tetsu-kikuchi]

In my case, I got this message in the evaluation at the 5th epoch, but not after the 10th epoch (val is done at every 5 epochs). I used my custom dataset with 45 train data and 5 val data. The train/val loss was not nan at the 5th epoch. I used faster_rcnn_r50_fpn_2x_coco.py.
Maybe it is better if the message is modified so that its meaning is clearer.