diff --git a/README.md b/README.md
index fdef93b..950f4aa 100644
--- a/README.md
+++ b/README.md
@@ -6,8 +6,8 @@ This implementation has been tested on the CamVid and Cityscapes datasets. Curre
| Dataset | Classes 1 | Input resolution | Batch size | Epochs | Mean IoU (%) | GPU memory (GiB) | Training time (hours)2 |
| :------------------------------------------------------------------: | :------------------: | :--------------: | :--------: | :----: | :---------------: | :--------------: | :-------------------------------: |
-| [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) | 11 | 480x360 | 10 | 300 | 51.083 | 4.2 | 1 |
-| [Cityscapes](https://www.cityscapes-dataset.com/) | 19 | 1024x512 | 4 | 300 | 59.034 | 5.4 | 20 |
+| [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) | 11 | 480x360 | 10 | 300 | 52.13 | 4.2 | 1 |
+| [Cityscapes](https://www.cityscapes-dataset.com/) | 19 | 1024x512 | 4 | 300 | 59.54 | 5.4 | 20 |
1 When referring to the number of classes, the void/unlabeled class is always excluded.
2 These are just for reference. Implementation, datasets, and hardware changes can lead to very different results. Reference hardware: Nvidia GTX 1070 and an AMD Ryzen 5 3600 3.6GHz. You can also train for 100 epochs or so and get similar mean IoU (± 2%).
diff --git a/save/ENet_CamVid/ENet b/save/ENet_CamVid/ENet
index b5782c3..2960b91 100644
Binary files a/save/ENet_CamVid/ENet and b/save/ENet_CamVid/ENet differ
diff --git a/save/ENet_CamVid/ENet_summary.txt b/save/ENet_CamVid/ENet_summary.txt
index f37fa67..c11f412 100644
--- a/save/ENet_CamVid/ENet_summary.txt
+++ b/save/ENet_CamVid/ENet_summary.txt
@@ -1,7 +1,7 @@
ARGUMENTS
batch_size: 10
dataset: camvid
-dataset_dir: ../Datasets/CamVid/
+dataset_dir: ../CamVid/
device: cuda
epochs: 300
height: 360
@@ -21,5 +21,5 @@ width: 480
workers: 4
BEST VALIDATION
-Epoch: 260
-Mean IoU: 0.6407776379897647
+Epoch: 280
+Mean IoU: 0.6518655444842216
diff --git a/save/ENet_Cityscapes/ENet b/save/ENet_Cityscapes/ENet
index 44bc91e..44ede15 100644
Binary files a/save/ENet_Cityscapes/ENet and b/save/ENet_Cityscapes/ENet differ
diff --git a/save/ENet_Cityscapes/ENet_summary.txt b/save/ENet_Cityscapes/ENet_summary.txt
index 41604f5..daadacb 100644
--- a/save/ENet_Cityscapes/ENet_summary.txt
+++ b/save/ENet_Cityscapes/ENet_summary.txt
@@ -1,7 +1,7 @@
ARGUMENTS
batch_size: 4
dataset: cityscapes
-dataset_dir: ../Datasets/Cityscapes/1024x512/
+dataset_dir: ../Cityscapes/1024x512/
device: cuda
epochs: 300
height: 512
@@ -14,12 +14,12 @@ mode: train
name: ENet
print_step: False
resume: False
-save_dir: save/new_cityscapes/
+save_dir: save/cityscapes_2/
weighing: ENet
weight_decay: 0.0002
width: 1024
workers: 4
BEST VALIDATION
-Epoch: 230
-Mean IoU: 0.5921075030966009
+Epoch: 250
+Mean IoU: 0.5949690267526815
diff --git a/save/README.md b/save/README.md
index 8c8d053..72730db 100644
--- a/save/README.md
+++ b/save/README.md
@@ -2,20 +2,20 @@
| Dataset | Classes 1 | Input resolution | Batch size | Epochs | Mean IoU (%) | GPU memory (GiB) | Training time (hours)2 |
| :------------------------------------------------------------------: | :------------------: | :--------------: | :--------: | :----: | :---------------: | :--------------: | :-------------------------------: |
-| [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) | 11 | 480x360 | 10 | 300 | 51.083 | 4.2 | 1 |
-| [Cityscapes](https://www.cityscapes-dataset.com/) | 19 | 1024x512 | 4 | 300 | 59.034 | 5.4 | 20 |
+| [CamVid](http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) | 11 | 480x360 | 10 | 300 | 52.13 | 4.2 | 1 |
+| [Cityscapes](https://www.cityscapes-dataset.com/) | 19 | 1024x512 | 4 | 300 | 59.54 | 5.4 | 20 |
## Per-class IoU: CamVid3
| | Sky | Building | Pole | Road | Pavement | Tree | Sign Symbol | Fence | Car | Pedestrian | Bicyclist |
| :-----: | :---: | :------: | :---: | :---: | :------: | :---: | :---------: | :---: | :---: | :--------: | :-------: |
-| IoU (%) | 89.8 | 68.2 | 19.9 | 90.1 | 71.6 | 62.7 | 17.8 | 15.1 | 65.9 | 25.8 | 34.3 |
+| IoU (%) | 90.2 | 68.6 | 22.6 | 91.5 | 73.2 | 63.6 | 19.3 | 16.7 | 65.1 | 27.2 | 35.0 |
## Per-class IoU: Cityscapes4
| | Road | Sidewalk | Building | Wall | Fence | Pole | Traffic light | Traffic Sign | Vegetation | Terrain | Sky | Person | Rider | Car | Truck | Bus | Train | Motorcycle | Bicycle |
| :-----: | :---: | :------: | :------: | :---: | :---: | :---: | :-----------: | :----------: | :--------: | :-----: | :---: | :----: | :---: | :---: | :---: | :---: | :---: | :--------: | :-----: |
-| IoU (%) | 96.1 | 73.9 | 86.0 | 39.5 | 41.8 | 45.6 | 43.6 | 54.9 | 88.1 | 53.5 | 90.1 | 62.5 | 41.4 | 88.3 | 41.3 | 56.9 | 34.6 | 24.1 | 59.6 |
+| IoU (%) | 96.1 | 73.3 | 85.8 | 44.1 | 40.5 | 45.3 | 42.5 | 53.9 | 87.9 | 53.5 | 90.1 | 62.3 | 44.3 | 87.6 | 46.6 | 58.2 | 34.8 | 25.8 | 57.9 |
1 When referring to the number of classes, the void/unlabeled class is always excluded.
2 These are just for reference. Implementation, datasets, and hardware changes can lead to very different results. Reference hardware: Nvidia GTX 1070 and an AMD Ryzen 5 3600 3.6GHz. You can also train for 100 epochs or so and get similar mean IoU (± 2%).