| base_path = '/kaggle/input/csiro-biomass/' | |
| train_path = os.path.join(base_path, 'train') | |
| test_path = os.path.join(base_path, 'test') | |
| test_csv_path = os.path.join(base_path, 'test.csv') | |
| train_csv_path = os.path.join(base_path, 'train.csv') | |
| data_dir = os.path.join(base_path, "") | |
| nFolds = 5 | |
| seed = 42 | |
| pretrained = False | |
| pretrained_weights_path = os.path.join(base_path, "") | |
| best_model_dir = "/kaggle/input/dinov2-training" | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| epochs = 15 | |
| batch_size = 8 | |
| lr = 1e-3 | |
| eta_min = 1e-5 | |
| weight_decay = 1e-6 | |
| # image | |
| img_size_h = 448 # 224 | |
| img_size_w = 448 # 224 | |
| in_chans = 3 | |
| # target columns | |
| target_cols = [ | |
| "Dry_Clover_g", | |
| "Dry_Dead_g", | |
| "Dry_Green_g", | |
| "Dry_Total_g", | |
| "GDM_g" | |
| ] | |
| n_targets = 3 | |
| # we can predict multiple configs | |
| targets_configs = ["Dry_Clover_g", "Dry_Dead_g", "Dry_Green_g"] | |
| weights = np.array([0.1, 0.1, 0.1, 0.5, 0.2]) | |
| mapping = {"Dry_Clover_g": 0, "Dry_Dead_g": 1, "Dry_Green_g": 2, "Dry_Total_g": 3, "GDM_g": 4} |