# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# 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" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

!tar xzf /kaggle/input/navires-2025/ships.tgz

!ls /kaggle/working

Lire les images

cf https://keras.io/api/data_loading/image/ et https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory

Résultat à soumettre

X_test = np.load('/kaggle/input/navires-2025/ships_competition.npz', allow_pickle=True)['X']
X_test = X_test.astype('float32') / 255

res = model.predict(X_test).argmax(axis=1)
df = pd.DataFrame({"Category":res})
df.to_csv("reco_nav.csv", index_label="Id")

!head reco_nav.csv

import os
os.chdir(r'/kaggle/working')
from IPython.display import FileLink
FileLink(r'reco_nav.csv')