<jupyter_start><jupyter_text>Training Object detector.<jupyter_code>from google.colab import drive
drive.mount('/content/drive')
# !rm -rf "/content/drive/My Drive/src/img"
%cd "/content/drive/My Drive/src/"
!unzip "img.zip"
!nvidia-smi
import pandas as pd
df = pd.read_csv("/content/drive/My Drive/src/final_data.csv")
df.head()
import numpy as np
df = df.drop(["Unnamed: 0", "target"], axis=1)
df
print(df['final_target'][0].dtype)
print(np.unique(df['final_target']))
# Shuffle the dataframe and sample 1/4 of it.
# df = df.sample(frac=1).reset_index(drop=True)
# df = df[:10000]
# df
# df_val = df[int(len(df) * 9 // 10): ]
# df_val.to_csv("df_val2.csv", index=False)
# df.to_csv("df_train2.csv", index=False)
# df.to_csv("df_train2.csv", index=False)
%cd "/content/drive/My Drive/src"
%pwd
!python train.py<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.187871581450654, 'fraction_numerical': 0.020214030915576695, 'mean_word_length': 5.5271317829457365, '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': '109105', 'license': 'Apache-2.0', 'n_tokens_mistral': 349, 'n_tokens_neox': 338, 'n_words': 66, 'path': 'fashion_intel/cloth_detector/Fashion_Object_detector.ipynb', 'repo_name': '1UC1F3R616/fashion_intel', 'size': '841'}