Update README.md

README.md

@@ -30,6 +30,28 @@ This results in two distinct caveats:
 
 To train the model, we used a data cohort from 2020, consisting of 333 images of Blue whiting, manually labelled using RoboFlow, and split it into Training (80%) / Validation (10%) / Holdout-Test (10%).
 
-Additionally, after training, we performed inference on the cohorts from 2021 and 2022, consisting of a total of 2018 images, and visually validated their quality as they had no labels.
-
-All images are the same size, 1280x960. 
+Additionally, after training, we performed inference on the cohorts from 2021 and 2022, consisting of a total of `2018` images, and visually validated their quality as they had no labels.
+
+All images are the same size, `1280x960`. To keep the aspect ratio we define the image size parameter as `[1920x1080]`
+
+These were the parameters used to train the model:
+
+```bash
+yolo detect train \
+    data=$data \
+    model=yolov8n.pt \
+    epochs=1000 \
+    imgsz=[1920,1080] \
+    rect=True \
+    batch=64 \
+    save=True \
+    save_period=1 \
+    cos_lr=True \
+    optimizer="auto" \
+    warmup_epochs=5 \
+    plots=True \
+    seed=42 \
+    device=0 \
+    project=otolith_detection \
+    name=2020_normal
+```