Github: https://github.com/pengpaiSH/Kaggle_NCFM
Step1. Download dataset from https://www.kaggle.com/c/the-nature-conservancy-fisheries-monitoring/data
Step2. Use split_train_val.py to split the labed data into training and validation. Usually, 80% for training and 20% for validation is a good start.
Step3. Use train.py to train a Inception_V3 network. The best model and its weights will be saved as "weights.h5".
Step4. Use predict.py to predict labels for testing images and generating the submission file "submit.csv". Note that such submission results in a 50% ranking in the leaderboard.
Step5. In order to improve our ranking, we use data augmentation for testing images. The intuition behind is similar to multi-crops, which makes use of voting ideas. predict_average_augmentation.py implements such idea and results in a 10% ranking (Public Score: 1.09) in the leaderboard.
Step 6. Note that there is still plenty of room for improvement. For example, we could split data into defferent training and valition data by cross-validation, e.g. k-fold. Then we train k models based on these splitted data. We average the predictions output by the k models as the final submission. This strategy will result a 5% ranking (Public Score: 1.02) in the leaderboard. We will leave the implementation as a practice for readers :)
Step 7: if you wanna to improve ranking further, object detection is your next direction!
Update and Note: In order to use
Step1. Download dataset from https://www.kaggle.com/c/the-nature-conservancy-fisheries-monitoring/data
Step2. Use split_train_val.py to split the labed data into training and validation. Usually, 80% for training and 20% for validation is a good start.
Step3. Use train.py to train a Inception_V3 network. The best model and its weights will be saved as "weights.h5".
Step4. Use predict.py to predict labels for testing images and generating the submission file "submit.csv". Note that such submission results in a 50% ranking in the leaderboard.
Step5. In order to improve our ranking, we use data augmentation for testing images. The intuition behind is similar to multi-crops, which makes use of voting ideas. predict_average_augmentation.py implements such idea and results in a 10% ranking (Public Score: 1.09) in the leaderboard.
Step 6. Note that there is still plenty of room for improvement. For example, we could split data into defferent training and valition data by cross-validation, e.g. k-fold. Then we train k models based on these splitted data. We average the predictions output by the k models as the final submission. This strategy will result a 5% ranking (Public Score: 1.02) in the leaderboard. We will leave the implementation as a practice for readers :)
Step 7: if you wanna to improve ranking further, object detection is your next direction!
Update and Note: In order to use
flow_from_directory(), you should create a folder named test_stg1 and put the original test_stg1 inside it.
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