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convert/README.md → tools/README.md
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# Convert the label files to CSV
# Additional tools
## Introduction
## Convert the label files to CSV
### Introduction
To train the images on [Google Cloud AutoML](https://cloud.google.com/automl), we should prepare the specific csv files follow [this format](https://cloud.google.com/vision/automl/object-detection/docs/csv-format).
`label_to_csv.py` can convert the `txt` or `xml` label files to csv file. The labels files should strictly follow to below structure.
## Structures
* Images
### Structures
* Images
To train the object detection tasks, all the images should upload to the cloud storage and access it by its name. All the images should stay in the **same buckets** in cloud storage. Also, different classes should have their own folder as below.
```
<bucket_name> (on the cloud storage)
@@ -21,7 +23,7 @@ To train the images on [Google Cloud AutoML](https://cloud.google.com/automl), w
| ...
```
Note, URI of the `class1_01.jpg` is `gs://<bucket_name>/class1/class1_01.jpg`
* Labels
* Labels
There are four types of training data - `TRAINING`, `VALIDATION`, `TEST` and `UNASSIGNED`. To assign different categories, we should create four directories.
Inside each folder, users should create the class folders with the same name in cloud storage (see below structure).
```
@@ -33,7 +35,7 @@ To train the images on [Google Cloud AutoML](https://cloud.google.com/automl), w
| | -- class2
| | | -- class2_01.txt (or .xml)
| | | ...
| | ...
| | ...
| -- VALIDATION
| | -- class1
| | | -- class1_02.txt (or .xml)
@@ -41,14 +43,14 @@ To train the images on [Google Cloud AutoML](https://cloud.google.com/automl), w
| | -- class2
| | | -- class2_02.txt (or .xml)
| | | ...
| | ...
| | ...
| -- TEST
| | (same as TRAINING and VALIDATION)
| -- UNASSIGNED
| | (same as TRAINING and VALIDATION)
```
## Usage
### Usage
To see the argument of `label_to_csv.py`,
```commandline
@@ -82,4 +84,4 @@ python label_to_csv.py \
```
The output file is `res.csv` by default. Afterwards, upload the csv file to the cloud storage and you can start training!

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convert/label_to_csv.py → tools/label_to_csv.py Normal file → Executable file
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