--- a
+++ b/utils/logging.py
@@ -0,0 +1,38 @@
+import pandas as pd
+import torch
+
+
+def log(fname, columns, values):
+    # TODO convert to numpy for lower space requirements
+    try:
+        df = pd.read_csv(fname)
+    except FileNotFoundError:
+        df = pd.DataFrame(columns=columns)
+    df = df.append(dict(zip(columns, values)), ignore_index=True)
+    df.to_csv(fname, index=False)
+
+
+def log_iou(fname, epoch, ious: torch.Tensor):
+    try:
+        df = pd.read_csv(fname)
+    except FileNotFoundError:
+        df = pd.DataFrame(columns=["epoch", "iou"])
+    serialized_ious = ious.flatten().numpy()
+    for iou in serialized_ious:
+        df = df.append(dict(zip(["epoch", "iou"], [epoch, iou])), ignore_index=True)
+    df.to_csv(fname, index=False)
+
+
+def log_full(epoch, id, config, result_dict, type):
+    data = {**config, **result_dict}
+
+    try:
+        df = pd.read_csv(f"logs/{type}/{config['model']}/{id}.csv")
+    except FileNotFoundError:
+        print("File not found, creating new")
+        df = pd.DataFrame(columns=data)
+    data["epoch"] = epoch
+
+    # data now contains all scores for every sample, so iterate over samples
+    new_df = df.append(data, ignore_index=True)
+    new_df.to_csv(f"logs/{type}/{config['model']}/{id}.csv", index=False)