diff --git a/.github/workflows/staging.yml b/.github/workflows/staging.yml
new file mode 100644
index 00000000..9684f24e
--- /dev/null
+++ b/.github/workflows/staging.yml
@@ -0,0 +1,75 @@
+name: staging-model
+
+on:
+  push:
+    branches:
+      - 'staging*'
+
+env:
+  nexus_server: 10.128.81.69:8082
+  cicd_path: /var/data/cicd/CICD_github_assets/
+  model_id: 20230930_60k_basic_targetted_epoch37_Myria3DV3.4.0
+
+jobs:
+  staging-model:
+    runs-on: self-hosted
+
+    steps:
+
+    - name: Checkout branch
+      uses: actions/checkout@v3
+
+    - name: Build docker image
+      run: docker build -t myria3d .
+
+    - name: Inference on validation set of 20211001_building_val-lidar-prod-optimization
+      run: >
+        docker run
+        -v ${cicd_path}:/cicd/
+        --ipc=host
+        --shm-size=2gb
+        myria3d
+        python run.py
+        task.task_name=predict
+        predict.src_las=/cicd/20211001_building_val-lidar-prod-optimization/val/*.las
+        predict.output_dir=/cicd/lidar_prod_optimization/${model_id}/preds-valset/
+        predict.interpolator.probas_to_save=[building]
+
+    - name: Inference on validation set of 20211001_building_val-lidar-prod-optimization
+      run: >
+        docker run
+        -v ${cicd_path}:/cicd/
+        --ipc=host
+        --shm-size=2gb
+        myria3d
+        python run.py
+        task.task_name=predict
+        predict.src_las=/cicd/20211001_building_val-lidar-prod-optimization/test/*.las
+        predict.output_dir=/cicd/lidar_prod_optimization/${model_id}/preds-testset/
+        predict.interpolator.probas_to_save=[building]
+
+    # NB: we run the new, tagged image at least once so that is it not prunned
+    # by mistake when using docker system prune.
+    - name: Tag the docker image with branch name
+      run: |
+        docker tag myria3d:latest myria3d:${{github.ref_name}} 
+        docker run myria3d:${{github.ref_name}} bash
+
+    # get version number and date, to tag the image pushed to nexus
+    - name: get version number
+      id: tag
+      run: |
+        echo "::set-output name=version::$(docker run myria3d grep '__version__' package_metadata.yaml| cut -d\" -f2)"
+        echo "::set-output name=date::$(date '+%Y.%m.%d')"
+
+    - name: push branch docker on nexus (tagged with the branch name)
+      # we push on nexus an image from a branch when it's pushed
+      if: ((github.event_name == 'push') && (github.ref_name != 'main'))
+      run: |
+        docker tag myria3d $nexus_server/lidar_hd/myria3d:${{steps.tag.outputs.version}}-${{github.ref_name}}
+        docker login $nexus_server --username svc_lidarhd --password ${{ secrets.PASSWORD_SVC_LIDARHD }}
+        docker push $nexus_server/lidar_hd/myria3d:${{steps.tag.outputs.version}}-${{github.ref_name}}
+
+    - name: Clean dangling docker images
+      if: always()  # always do it, even if something failed
+      run: docker system prune --force # remove dangling docker images, without asking user for confirmation
diff --git a/trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0.ckpt b/trained_model_assets/20230930_60k_basic_targetted_epoch37_Myria3DV3.4.0.ckpt
similarity index 61%
rename from trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0.ckpt
rename to trained_model_assets/20230930_60k_basic_targetted_epoch37_Myria3DV3.4.0.ckpt
index e1bbdf46..9db16e96 100644
Binary files a/trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0.ckpt and b/trained_model_assets/20230930_60k_basic_targetted_epoch37_Myria3DV3.4.0.ckpt differ
diff --git a/trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.5.0.yaml b/trained_model_assets/20230930_60k_basic_targetted_epoch37_Myria3DV3.4.0_predict_config_V3.5.0.yaml
similarity index 89%
rename from trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.5.0.yaml
rename to trained_model_assets/20230930_60k_basic_targetted_epoch37_Myria3DV3.4.0_predict_config_V3.5.0.yaml
index 009293d6..1ff32293 100644
--- a/trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.5.0.yaml
+++ b/trained_model_assets/20230930_60k_basic_targetted_epoch37_Myria3DV3.4.0_predict_config_V3.5.0.yaml
@@ -5,20 +5,15 @@ print_config: true
 ignore_warnings: true
 trainer:
   _target_: pytorch_lightning.Trainer
-  gpus:
-  - 0
-  - 1
-  min_epochs: 100
-  max_epochs: 150
+  gpus: 0
+  min_epochs: 300
+  max_epochs: 400
   log_every_n_steps: 1
   weights_summary: null
   progress_bar_refresh_rate: 1
   auto_lr_find: false
   num_sanity_val_steps: 2
   accumulate_grad_batches: 3
-  strategy: ddp
-  accelerator: gpu
-  num_processes: 2
 datamodule:
   transforms:
     preparations:
@@ -118,9 +113,9 @@ datamodule:
     preparations_predict_list: '${oc.dict.values: datamodule.transforms.preparations.predict}'
     normalizations_list: '${oc.dict.values: datamodule.transforms.normalizations}'
   _target_: myria3d.pctl.datamodule.hdf5.HDF5LidarDataModule
-  data_dir: null
-  split_csv_path: null
-  hdf5_file_path: /var/data/cgaydon/data/20220607_151_dalles_proto.myria3d-V2.4.hdf5
+  data_dir: /mnt/store-lidarhd/projet-LHD/IA/PACASAM-SHARED-WORKSPACE/CGaydon/20230930_60k_basic_targetted/data/
+  split_csv_path: /mnt/store-lidarhd/projet-LHD/IA/PACASAM-SHARED-WORKSPACE/CGaydon/20230930_60k_basic_targetted/data/split.csv
+  hdf5_file_path: /var/data/CGaydon/myria3d_datasets/20230930_60k_basic_targetted.hdf5
   points_pre_transform:
     _target_: functools.partial
     _args_:
@@ -130,7 +125,7 @@ datamodule:
     _args_:
     - ${get_method:myria3d.pctl.dataset.utils.pre_filter_below_n_points}
     min_num_nodes: 1
-  tile_width: 1000
+  tile_width: 50
   subtile_width: 50
   subtile_overlap_train: 0
   subtile_overlap_predict: ${predict.subtile_overlap}
@@ -142,30 +137,10 @@ dataset_description:
   classification_preprocessing_dict:
     3: 5
     4: 5
-    160: 64
-    161: 64
-    162: 64
     0: 1
-    7: 1
-    46: 1
-    47: 1
-    48: 1
-    49: 1
-    50: 1
-    51: 1
-    52: 1
-    53: 1
-    54: 1
-    55: 1
-    56: 1
-    57: 1
-    58: 1
-    64: 1
-    66: 1
-    67: 1
-    77: 1
-    155: 1
-    204: 1
+    66: 65
+    100: 1
+    101: 1
   classification_dict:
     1: unclassified
     2: ground
@@ -174,14 +149,6 @@ dataset_description:
     9: water
     17: bridge
     64: lasting_above
-  class_weights:
-  - 0.25
-  - 0.1
-  - 0.1
-  - 0.5
-  - 2.0
-  - 2.0
-  - 2.0
   d_in: 9
   num_classes: 7
 callbacks:
@@ -228,9 +195,11 @@ model:
     patience: 20
     cooldown: 5
     verbose: true
+    min_lr: 0.001
   criterion:
     _target_: torch.nn.CrossEntropyLoss
     label_smoothing: 0.0
+    ignore_index: 65
   _target_: myria3d.models.model.Model
   d_in: ${dataset_description.d_in}
   num_classes: ${dataset_description.num_classes}
@@ -259,21 +228,21 @@ logger:
     api_key: ${oc.env:COMET_API_TOKEN}
     workspace: ${oc.env:COMET_WORKSPACE}
     project_name: ${oc.env:COMET_PROJECT_NAME}
-    experiment_name: '[V3.0.2-BS10xMAX40000pts] RandLaNet_base_run_FR_pyg_randla_net_NoRS'
+    experiment_name: 20230930_60k_basic_targetted
     auto_log_co2: false
     disabled: false
 task:
-  task_name: predict
+  task_name: fit
 predict:
   src_las: /path/to/input.las
   output_dir: /path/to/output_dir/
-  ckpt_path: trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0.ckpt
-  subtile_overlap: 0
+  ckpt_path: /path/to/lightning_model.ckpt
   gpus: 0
+  subtile_overlap: 0
   interpolator:
     _target_: myria3d.models.interpolation.Interpolator
     interpolation_k: 10
     classification_dict: ${dataset_description.classification_dict}
-    probas_to_save: [building,ground]
-    predicted_classification_channel: confidence
+    probas_to_save: all
+    predicted_classification_channel: PredictedClassification
     entropy_channel: entropy