diff --git a/Project.toml b/Project.toml
index 6c24a0ef51bab96179f28750585a9308e16c33fa..6f89aa7072211e08db91e5ed664764695e816bee 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "LarvaTagger"
 uuid = "8b3b36f1-dfed-446e-8561-ea19fe966a4d"
 authors = ["François Laurent", "Institut Pasteur"]
-version = "0.14"
+version = "0.14.1"
 
 [deps]
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
diff --git a/scripts/larvatagger.jl b/scripts/larvatagger.jl
index 5c93792031ec30d1450bdd69e651738a7c0291e8..794bc59cb7bf32b3ee186706a45d57750e8bbbe7 100755
--- a/scripts/larvatagger.jl
+++ b/scripts/larvatagger.jl
@@ -25,8 +25,8 @@ LarvaTagger.jl
 Usage:
   larvatagger.jl open <file-path> [--backends=<path>] [--port=<number>] [--quiet] [--viewer] [--browser] [--manual-label=<label>]
   larvatagger.jl import <input-path> [<output-file>] [--id=<id>] [--framerate=<fps>] [--pixelsize=<μm>] [--overrides=<comma-separated-list>] [--default-label=<label>] [--manual-label=<label>] [--decode]
-  larvatagger.jl train <backend-path> <data-path> <model-instance> [--pretrained-model=<instance>] [--labels=<comma-separated-list>] [--sample-size=<N>] [--balancing-strategy=<strategy>] [--class-weights=<csv>] [--manual-label=<label>] [--layers=<N>] [--iterations=<N>]
-  larvatagger.jl predict <backend-path> <model-instance> <data-path> [--make-dataset] [--skip-make-dataset] [--data-isolation]
+  larvatagger.jl train <backend-path> <data-path> <model-instance> [--pretrained-model=<instance>] [--labels=<comma-separated-list>] [--sample-size=<N>] [--balancing-strategy=<strategy>] [--class-weights=<csv>] [--manual-label=<label>] [--layers=<N>] [--iterations=<N>] [--seed=<seed>]
+  larvatagger.jl predict <backend-path> <model-instance> <data-path> [--output=<filename>] [--make-dataset] [--skip-make-dataset] [--data-isolation]
   larvatagger.jl merge <input-path> <input-file> [<output-file>] [--manual-label=<label>] [--decode]
   larvatagger.jl -V | --version
   larvatagger.jl -h | --help
@@ -48,6 +48,7 @@ Options:
   --sample-size=<N>    Sample only N track segments from the data repository.
   --layers=<N>         (MaggotUBA) Number of layers of the classifier.
   --iterations=<N>     (MaggotUBA) Number of training iterations (can be two integers separated by a comma).
+  --seed=<seed>        Seed for the backend's random number generators.
   --decode             Do not encode the labels into integer indices.
   --default-label=<label>             Label all untagged data as <label>.
   --manual-label=<label>              Secondary label for manually labelled data [default: edited].
@@ -56,6 +57,7 @@ Options:
   --pretrained-model=<instance>       Name of the pretrained encoder (from `pretrained_models` registry).
   --balancing-strategy=<strategy>     Any of `auto`, `maggotuba`, `none` [default: auto].
   --overrides=<comma-separated-list>  Comma-separated list of key:value pairs.
+  -o <filename> --output=<filename>   Predicted labels filename.
 
 
 Commands: