Upload dinov2-2streams.yaml

dinov2-2streams.yaml

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+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}