3.1. Prepare training dataset¶
It is necessary to prepare the training dataset before you start blueoil.
The dataset consists of each example that is pair consisting of an input image data and a desired output data (the supervisory signal). In addition to that, class (category) names etc. The information of the desired output data changes according to the target task (classification, object detection). In response to the desired task, blueoil supports several public dataset formats and the format output by Delta-Mark service.
3.1.1. Dataset format¶
126.96.36.199. training vs validation¶
With all dataset formats, you can select the root path of data for training and validation.
The path of training data is required, validation is optional. When you only select training data path, validation data are created from training data automatically. In the following description,
training_dataset_path is the root path of training data and
validation_dataset_path is the root path of validation data.
188.8.131.52. List of supported dataset format¶
184.108.40.206. Caltech 101¶
As following Caltech 101, a dataset format where category names are the names of the subdirectories.
The subdirectory under
validation_dataset_path becomes the class name and images are located under the subdirectory.
training_dataset_path ├── class_0 │ ├── 0001.jpg │ ├── xxxa.jpeg │ ├── yyyb.png │ ├── class_1 │ ├── 123.jpg │ ├── xxxa.jpeg │ ├── wwww.jpg # If you set `validation_dataset_path`, you can locate images as the same manner. validation_dataset_path ├── class_0 │ ├── 0002.jpg │ ├── class_1 │ ├── 1234.jpeg
220.127.116.11. DeLTA-Mark classification¶
You can download data of this format by using Delta-Mark service.
It is data format based on Open Images Dataset V4.
Place the following files and directory under
annotations-bbox.csv: The CSV that each row defines one bounding box. The field specifications are described in
Boxessection of Open Images Dataset V4 of Data Formats.
class-descriptions.csv: The CSV each row defines class name. It is not necessary for validation data. The field specifications are described in
Class Namessection of Open Images Dataset V4 of Data Formats.
images: All images are located under the directory.
training_dataset_path ├── annotations-bbox.csv ├── class-descriptions.csv └── images ├── 000002b66c9c498e.jpg ├── 000002b97e5471a0.jpg ├── 000002c707c9895e.jpg # If you set `validation_dataset_path`, you can locate images as the same manner. validation_dataset_path ├── annotations-bbox.csv └── images ├── 0001eeaf4aed83f9.jpg ├── 000595fe6fee6369.jpg ├── 00075905539074f2.jpg
ImageID,Source,LabelName,Confidence,XMin,XMax,YMin,YMax,IsOccluded,IsTruncated,IsGroupOf,IsDepiction,IsInside 000026e7ee790996,freeform,/m/07j7r,1,0.071905,0.145346,0.206591,0.391306,0,1,1,0,0 000026e7ee790996,freeform,/m/07j7r,1,0.439756,0.572466,0.264153,0.435122,0,1,1,0,0 000026e7ee790996,freeform,/m/07j7r,1,0.668455,1.000000,0.000000,0.552825,0,1,1,0,0 000062a39995e348,freeform,/m/015p6,1,0.205719,0.849912,0.154144,1.000000,0,0,0,0,0 000062a39995e348,freeform,/m/05s2s,1,0.137133,0.377634,0.000000,0.884185,1,1,0,0,0 0000c64e1253d68f,freeform,/m/07yv9,1,0.000000,0.973850,0.000000,0.043342,0,1,1,0,0 0000c64e1253d68f,freeform,/m/0k4j,1,0.000000,0.513534,0.321356,0.689661,0,1,0,0,0 0000c64e1253d68f,freeform,/m/0k4j,1,0.016515,0.268228,0.299368,0.462906,1,0,0,0,0 0000c64e1253d68f,freeform,/m/0k4j,1,0.481498,0.904376,0.232029,0.489017,1,0,0,0,0 ...
... /m/0pc9,Alphorn /m/0pckp,Robin /m/0pcm_,Larch /m/0pcq81q,Soccer player /m/0pcr,Alpaca /m/0pcvyk2,Nem /m/0pd7,Army /m/0pdnd2t,Bengal clockvine /m/0pdnpc9,Bushwacker /m/0pdnsdx,Enduro /m/0pdnymj,Gekkonidae ...