<jupyter_start><jupyter_text>Install convert2Yolo<jupyter_code>!git clone https://github.com/ssaru/convert2Yolo
%cd /content/convert2Yolo/
## DO NOT RESTART RUNTIME. IT WILL CRASH.

!pip install -r requirements.txt<jupyter_output>Collecting Pillow==7.2.0
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[K     |█▉                              |[...]<jupyter_text>Instruction*   output directory will contain yolo formatted txt files*   label directory will contain voc xml files*   img directory will contain jpg files*   workspace directory will contain voc.names files<jupyter_code>!mkdir /content/workspace
!mkdir /content/output
!mkdir /content/label
!mkdir /content/img
!echo -e "cat\ndog\nbird" > "/content/workspace/voc.names"
!python example.py --datasets VOC --img_path "/content/img" --label "/content/label" --convert_output_path "/content/output" --img_type ".jpg" --manifest_path ./ --cls_list_file "/content/workspace/voc.names"<jupyter_output>VOC Parsing:  |----------------------------------------| 0.0% (0/1)  Complete

VOC Parsing:   |████████████████████████████████████████| 100.0% (1/1)  Complete


YOLO Generating:|----------------------------------------| 0.0% (0/1)  Complete

YOLO Generating:|████████████████████████████████████████| 100.0% (1/1)  Complete


YOLO Saving:  |----------------------------------------| 0.0% (0/1)  Complete

YOLO Saving:   |████████████████████████████████████████| 100.0% (1/1)  Complete<jupyter_text>Conversion with demo image and label<jupyter_code>!python example.py --datasets VOC --img_path "/content/convert2Yolo/example/voc/JPEG" --label "/content/convert2Yolo/example/voc/label" --convert_output_path "/content/output" --img_type ".jpg" --manifest_path ./ --cls_list_file "/content/convert2Yolo/example/voc/names.txt"<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.21650629163582533, 'fraction_numerical': 0.07327905255366396, 'mean_word_length': 1.9508733624454149, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 12, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 1}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 22, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '586772', 'license': 'MIT', 'n_tokens_mistral': 1195, 'n_tokens_neox': 989, 'n_words': 182, 'path': 'computer_vision/Convert_VOC_to_YOLO.ipynb', 'repo_name': 'quickgrid/code-lab', 'size': '2702'}