diff --git a/docs/docker.md b/docs/docker.md
index 466adf6..e47f2d5 100644
--- a/docs/docker.md
+++ b/docs/docker.md
@@ -1,4 +1,8 @@
-# 1. ocrd resource manager
+## Inference with Docker
+
+    docker pull ghcr.io/qurator-spk/eynollah:latest
+
+### 1. ocrd resource manager
 (just once, to get the models and install them into a named volume for later re-use)
 
     vol_models=ocrd-resources:/usr/local/share/ocrd-resources
@@ -6,19 +10,34 @@
 
 Now, each time you want to use Eynollah, pass the same resources volume again.
 Also, bind-mount some data directory, e.g. current working directory $PWD (/data is default working directory in the container).
+
 Either use standalone CLI (2) or OCR-D CLI (3):
 
-# 2. standalone CLI (follow self-help, cf. readme)
+### 2. standalone CLI 
+(follow self-help, cf. readme)
 
     docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah binarization --help
     docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah layout --help
     docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah eynollah ocr --help
 
-# 3. OCR-D CLI (follow self-help, cf. readme and https://ocr-d.de/en/spec/cli)
+### 3. OCR-D CLI 
+(follow self-help, cf. readme and https://ocr-d.de/en/spec/cli)
 
     docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-eynollah-segment -h
     docker run --rm -v $vol_models -v $PWD:/data ocrd/eynollah ocrd-sbb-binarize -h
 
 Alternatively, just "log in" to the container once and use the commands there:
 
-    docker run --rm -v $vol_models -v $PWD:/data -it ocrd/eynollah bash
\ No newline at end of file
+    docker run --rm -v $vol_models -v $PWD:/data -it ocrd/eynollah bash
+
+## Training with Docker
+
+Build the Docker image
+
+    cd train
+    docker build -t model-training .
+
+Run the Docker image
+
+    cd train
+    docker run --gpus all -v $PWD:/entry_point_dir model-training
diff --git a/train/README.md b/train/README.md
index 5f6d326..d270542 100644
--- a/train/README.md
+++ b/train/README.md
@@ -41,19 +41,3 @@ each class will be defined with a RGB value and beside images, a text file of cl
 > Convert COCO GT or results for a single image to a segmentation map and write it to disk.
 * [`ocrd-segment-extract-pages`](https://github.com/OCR-D/ocrd_segment/blob/master/ocrd_segment/extract_pages.py)
 > Extract region classes and their colours in mask (pseg) images. Allows the color map as free dict parameter, and comes with a default that mimics PageViewer's coloring for quick debugging; it also warns when regions do overlap.
-
-### Train using Docker
-
-Build the Docker image:
-
-```bash
-cd train
-docker build -t model-training .
-```
-
-Run Docker image 
-
-```bash
-cd train
-docker run --gpus all -v $PWD:/entry_point_dir model-training
-```