diff --git a/detection/data/__init__.py b/detection/data/__init__.py
index 4be478b..c8349fc 100644
--- a/detection/data/__init__.py
+++ b/detection/data/__init__.py
@@ -1,3 +1,4 @@
+from pathlib import Path
 import pandas as pd
 import os
 
@@ -8,6 +9,13 @@ from . import constants as C
 
 
 def get_dataset(split="train"):
-    meta = pd.read_csv("../data/meta.csv")
-    info = DatasetInfo.load("../data/info.yaml")
+    data_dir = Path(__file__).parent.parent.parent / "data"
+    # here = os.path.dirname(__path__)
+    # data_dir = here + '/../../data/'
+    
+    meta = pd.read_csv(data_dir / "non-empty-meta.csv")
+    info = DatasetInfo.load(data_dir / "info.yaml")
+
+    # meta['image_path'] = [f'data/image/{panoid}_{heading}.jpg' for panoid, heading in zip(meta['panoid'], meta['heading'])]
+
     return BaseDataset(info, meta)[split]
diff --git a/detection/eval/evaluator.py b/detection/eval/evaluator.py
index 8dbcf2d..e63f4ce 100644
--- a/detection/eval/evaluator.py
+++ b/detection/eval/evaluator.py
@@ -108,7 +108,7 @@ class DetectionEvaluator(DatasetEvaluator):
                         format=detection.BBFormat.XYX2Y2)
                     self._bbox.addBoundingBox(bb)
 
-    def evaluate(self):
+    def evaluate(self, save_dir):
         results = self._evaluator.GetPascalVOCMetrics(self._bbox, self._iou_thresh)
         if isinstance(results, dict):
             results = [results]
@@ -118,7 +118,7 @@ class DetectionEvaluator(DatasetEvaluator):
             metrics[f'AP_{result["class"]}'] = result['AP']
             APs.append(result['AP'])
         metrics['mAP'] = np.nanmean(APs)
-        self._evaluator.PlotPrecisionRecallCurve(self._bbox, savePath="./plots/", showGraphic=False)
+        self._evaluator.PlotPrecisionRecallCurve(self._bbox, savePath=save_dir, showGraphic=False)
         return metrics
 
 class DatasetEvaluators(DatasetEvaluator):
diff --git a/detection/lightning/detection.py b/detection/lightning/detection.py
index 43fc7d6..71875d3 100644
--- a/detection/lightning/detection.py
+++ b/detection/lightning/detection.py
@@ -30,6 +30,12 @@ class DetectionTask(pl.LightningModule, TFLogger):
         self.save_hyperparameters(params)
         self.model = get_model(params)
         self.evaluator = DetectionEvaluator()
+        self._plot_save_dir = params['save_dir'] + f"/{params['exp_name']}/plots"
+        self._output_save_dir = params['save_dir'] + f"/{params['exp_name']}/outputs"
+        if not os.path.exists(self._plot_save_dir):
+            os.mkdir(self._plot_save_dir)
+        if not os.path.exists(self._output_save_dir):
+            os.mkdir(self._output_save_dir)
 
     def training_step(self, batch, batch_nb):
         losses = self.model.forward(batch)
@@ -46,7 +52,7 @@ class DetectionTask(pl.LightningModule, TFLogger):
     def validation_epoch_end(self, outputs):
         avg_loss = torch.stack(outputs).mean()
         self.log("val_loss", avg_loss)
-        metrics = self.evaluator.evaluate()
+        metrics = self.evaluator.evaluate(save_dir=self._plot_save_dir)
         nni.report_intermediate_result(metrics['mAP']) 
         self.evaluator.reset()
         self.log_dict(metrics, prog_bar=True)
@@ -124,12 +130,12 @@ class DetectionTask(pl.LightningModule, TFLogger):
                         with open(json_save_path, 'w') as fp:
                                 json.dump(data, fp)
                     else:
-                        img_box.save(f"outputs/{batch_nb}_{i}.jpg")
+                        img_box.save(f"{self._output_save_dir}/{batch_nb}_{i}.jpg")
                     
         self.evaluator.process(batch, preds)
 
     def test_epoch_end(self, outputs):
-        metrics = self.evaluator.evaluate()
+        metrics = self.evaluator.evaluate(save_dir=self._plot_save_dir)
         nni.report_final_result(metrics['mAP'])
         self.log_dict(metrics)