{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Whatever I submit, I always get the same result, 0.177, which is the result of the sample submission, according to the leaderboard. This notebook generates a submission file labeling every test image as \"healthy\", yet it still returns the same score.","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n\n#tqdm is a progress bar\nfrom tqdm import tqdm\n\n#Plotting\n%matplotlib inline\nimport matplotlib.pyplot as plt\n\n#To manipulate images\nimport cv2\n\n#Data preprocessing\n# from sklearn.preprocessing import MinMaxScaler\n# from sklearn.model_selection import train_test_split\n\n#Torch stuff\n# import torch\n# import torch.nn as nn\n# from torch.nn import functional as F\n# import torch.optim as optim\n# from torch.utils.data import Dataset, DataLoader\n\n#!pip list","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Can I load the data?","metadata":{}},{"cell_type":"code","source":"train_image_path = '../input/plant-pathology-2021-fgvc8/train_images/'\n\ntrain_file = '../input/plant-pathology-2021-fgvc8/train.csv'\n\nsubmission_file = '../working/submission.csv'\n\ntest_image_path = '../input/plant-pathology-2021-fgvc8/test_images/'","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now I get all filenames from training and test sets","metadata":{}},{"cell_type":"code","source":"test_images = os.listdir(test_image_path)\ntrain_images  = os.listdir(train_image_path)\n\nos.listdir(\"/kaggle/working\")\nif os.path.exists(\"/kaggle/working/submission.csv\"):\n    os.remove(\"submission.csv\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub = pd.DataFrame(test_images, columns=['image'])\nsub['labels'] = 'frog_eye_leaf_spot'\nprint(sub)\nprint(len(sub))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub.to_csv('submission.csv',index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}