<jupyter_start><jupyter_text>Run detection on images<jupyter_code>%%time

output_rows = []

for img_path in input_images:
    print(img_path)
    if 'DS_Store' not in str(img_path):
        img = Image.open(img_path)
        output_path = Path(path_output_dir, img_path.name)
        out_img, _, _, classes, coords, _ = model.detect_image(img.copy())

        output_rows += [get_row(img_path, coords, classes)]
        out_img.save(str(output_path))
        print('')

with open(path_detection_output, 'w+') as output_file:
    for row in output_rows:
        output_file.write(row + '\n')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12258064516129032, 'fraction_numerical': 0.0, 'mean_word_length': 3.776923076923077, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '213896', 'license': 'MIT', 'n_tokens_mistral': 224, 'n_tokens_neox': 221, 'n_words': 41, 'path': 'notebook/2.detection.ipynb', 'repo_name': 'chrisPiemonte/keras-yolo3', 'size': '620'}