<jupyter_start><jupyter_text><jupyter_code>!nvidia-smi
from google.colab import drive
drive.mount('/content/gdrive')
!git clone https://github.com/Shakil-1501/Deep-Learning.git
!wget https://s3-eu-west-1.amazonaws.com/densedepth/nyu.h5 -O ./Deep-Learning/depth_estimation/nyu.h5
import shutil
shutil.move('gdrive/My Drive/nyu_data.zip','Deep-Learning/depth_estimation')
cd Deep-Learning/depth_estimation
ls
!python train.py --data nyu --bs 4 --full --dnetVersion small
!python test.py<jupyter_output>Using TensorFlow backend.
Loading model...

Model loaded (nyu.h5).

Loaded (10) images of size (480, 640, 3).
It took:  25.734498262405396
{'fraction_non_alphanumeric': 0.1426332288401254, 'fraction_numerical': 0.05642633228840126, 'mean_word_length': 4.916666666666667, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 2, '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': '326755', 'license': 'MIT', 'n_tokens_mistral': 271, 'n_tokens_neox': 243, 'n_words': 49, 'path': 'S14-15/DenseDepth.ipynb', 'repo_name': 'Shakil-1501/TSAI', 'size': '638'}