Comments (2)
monoloxo
commented on May 15, 2024
Object Detection dataset not this structure:
image_dir/ <dataset split>/ <label>/ <image
so,does it also work on object detection dataset?
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cfezequiel
commented on May 15, 2024
@monoloxo The default schema used by the tool is intended for an image classification dataset. But you should be able to repurpose it to generate TFRecord files for object detection. You can use the Flexible Schema to specify the fields that would contain the bounding boxes in addition to the image/label/split info, e.g.
split, image_uri, label, x0, y0, x1, y1, ...
You would also need to create the input CSV/DataFrame for TFRecorder that contains the necessary information.
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Related Issues (20)
- Change TFRUtil to TFRecorder in Notebooks HOT 1
- Misleading example in readme HOT 2
- Improve Installation Process [setup.py file location] HOT 5
- Make Jupyter Notebooks code example executables in Colab HOT 1
- Add function to load a TF Dataset from TFRecords
- Add guard for non-image files in image directory input HOT 4
- Pull request template not showing
- Include TFRecord output directory in `create_tfrecords` return `dict`
- Use lower case names for split values HOT 3
- Add note that header needs to be included
- Automated testing for Jupyter notebooks
- Convert to TFRecords and load a dataset
- Add additional imports to __init__.py to help linked runtimes
- ImportError: cannot import name 'Parse' from 'avro.schema' HOT 4
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- Load and inspect from GCS ?
- Decode Error when trying to save structured df to tfrecord HOT 3
- 'CsvCoder' object has no attribute 'decode'
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