diff --git a/.gitattributes b/.gitattributes
deleted file mode 100644
index dfe07704..00000000
--- a/.gitattributes
+++ /dev/null
@@ -1,2 +0,0 @@
-# Auto detect text files and perform LF normalization
-* text=auto
diff --git a/.gitignore b/.gitignore
index 774b1577..979315f3 100755
--- a/.gitignore
+++ b/.gitignore
@@ -12,7 +12,7 @@
 *.pt
 *.tif.txt
 !zidane_result.jpg
-!coco_training_loss.png
+#!coco_training_loss.png
 !images/*
 
 checkpoints
diff --git a/README.md b/README.md
index 0c0f689f..401e0f7e 100755
--- a/README.md
+++ b/README.md
@@ -19,8 +19,8 @@ Python 3.6 or later with the following `pip3 install -U -r requirements.txt` pac
 
 # Running
 
-Run `train.py` to begin training. Each epoch trains on 120,000 images from the train and validate sets, and validates on 5000 images in the validation set. An Nvidia GTX 1080 Ti will run about 16 epochs per day. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here.
-![Alt](https://github.com/ultralytics/yolov3/blob/master/data/xview_training_loss.png "training loss")
+Run `train.py` to begin training. Each epoch trains on 120,000 images from the train and validate sets, and validates on 5000 images in the validation set. An Nvidia GTX 1080 Ti will run about 16 epochs per day. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here (coming soon)
+![Alt](https://github.com/ultralytics/yolov3/blob/master/data/coco_training_loss.png "training loss")
 
 Checkpoints will be saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here.
 ![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "example")