--- a
+++ b/experiments/train_augmented_pipeline.py
@@ -0,0 +1,51 @@
+from training.consistency_trainers import *
+from training.inductivenet_trainers import InductiveNetConsistencyTrainer, InductiveNetEnsembleTrainer
+import sys
+
+if __name__ == '__main__':
+    id = sys.argv[1]
+    model = sys.argv[2]
+    config = {"model": model,
+              "device": "cuda",
+              "lr": 0.00001,
+              "batch_size": 8,
+              "epochs": 250,
+              "use_inpainter": False}
+    trainer = ConsistencyTrainer(id, config)
+    trainer.train()
+
+    # config = {"model": "FPN",
+    #           "device": "cuda",
+    #           "lr": 0.00001,
+    #           "batch_size": 8,
+    #           "epochs": 250,
+    #           "use_inpainter": False}
+    # trainer = ConsistencyTrainer(id=f"{sys.argv[1]}", config=config)
+    # trainer.train()
+    # config = {"model": "Unet",
+    #           "device": "cuda",
+    #           "lr": 0.00001,
+    #           "batch_size": 8,
+    #           "epochs": 250,
+    #           "use_inpainter": False}
+    # trainer = StrictConsistencyTrainer(id=f"dual_jaccard-{sys.argv[1]}", config=config)
+    # trainer.train()
+
+    # trainer = ConsistencyTrainerUsingAugmentation(id=f"augmentation-{sys.argv[1]}", config=config)
+    # trainer.train()
+    # config = {"model": "Unet",
+    #           "device": "cuda",
+    #           "lr": 0.00001,
+    #           "batch_size": 8,
+    #           "epochs": 250,
+    #           "use_inpainter": False}
+    # trainer = ConsistencyTrainerUsingControlledAugmentation("aug_test", config)
+    # trainer.train()
+    # config = {"model": "Unet",
+    #           "device": "cuda",
+    #           "lr": 0.00001,
+    #           "batch_size": 8,
+    #           "epochs": 250,
+    #           "use_inpainter": False}
+    # trainer = AdversarialConsistencyTrainer(id=f"adversarial-{sys.argv[1]}", config=config)
+    # trainer.train()