diff --git a/README.md b/README.md
index 2f35d2d..69880b4 100644
--- a/README.md
+++ b/README.md
@@ -48,7 +48,7 @@ Because of the duration of the preprocessing, we save the preprocessed files as
 ./preprocess_data.sh /BASE/FOLDER
 ```
 
-The script will create a new folder `DCASEfewshot` containing three subfolders (`train`, `validate` and `evaluate`). Each of these folder contains the processed data in the form of `numpy arrays`.
+The script will create a new folder `DCASEfewshot` containing three subfolders (`train`, `validate` and `evaluate`). Each of these folder **contains a  subfolder with a hash as a name**. **The hash has been created based on the processing parameters**. The processed data in the form of `numpy arrays`.
 
 :black_nib: You can change the parameters for preprocessing the data in the [CONFIG.yaml file](/CONFIG.yaml)
 
@@ -70,13 +70,15 @@ The training script should create a `log` folder in the base folder (`lightning_
 
 :black_nib: Update the `status` parameter of the [CONFIG.yaml file](/CONFIG.yaml) to the dataset you want to use the model on. Change `status` to either **validate** or **evaluate**.
 
+:black_nib: Also update the `model_path` in the [CONFIG.yaml file](/CONFIG.yaml) to the checkpoints (`ckpt`) that has been trained in the previous step (stored in `lightning_logs`)
+
 To run the prediction use the script `test_model`. 
 
 ```bash
 ./test_model.sh /BASE/FOLDER
 ```
 
-`test_model.sh` creates a result file `eval_out.csv` in the `BASE/FOLDER` containing all the detections made by the model. 
+`test_model.sh` creates a result file `eval_out.csv` in the folder containing the processed `validation` data. **The full path is printed in the console**
 
 Note that there are other advanced options. For instance, if `--wav_save` is specified, the script will also return a `.wav` file for all files containing additional channels: the ground truth labels, the predicted labels, the distance to the POS prototype and finally the p-values. The `.wav` file can be opened in [Audacity](https://www.audacityteam.org/) to be inspected more closely.
 
@@ -85,33 +87,33 @@ Note that there are other advanced options. For instance, if `--wav_save` is spe
 Once the `eval_out.csv` has been created, it is possible to get the results for our approach. Note that the metrics can only be computed for the `Validation_Set` as it contains all ground truth labels as opposed to the `Evaluation_Set` for which only the 5 first samples of the POS class are labelled.
 
 ```bash
-./compute_metrics.sh /BASE/FOLDER
+./compute_metrics.sh /BASE/FOLDER /PATH/TO/eval_out.csv
 ```
 
 Here are the results we obtain using our pipeline described in our [Technical Report](https://dcase.community/documents/challenge2023/technical_reports/DCASE2023_Gelderblom_SINTEF_t5.pdf)
 
 ```
-Evaluation for: BEATs VAL
-BUK1_20181011_001004.wav {'TP': 15, 'FP': 35, 'FN': 16, 'total_n_pos_events': 31}
-BUK1_20181013_023504.wav {'TP': 2, 'FP': 258, 'FN': 22, 'total_n_pos_events': 24}
-BUK4_20161011_000804.wav {'TP': 1, 'FP': 30, 'FN': 46, 'total_n_pos_events': 47}
-BUK4_20171022_004304a.wav {'TP': 7, 'FP': 17, 'FN': 10, 'total_n_pos_events': 17}
-BUK5_20161101_002104a.wav {'TP': 31, 'FP': 7, 'FN': 57, 'total_n_pos_events': 88}
-BUK5_20180921_015906a.wav {'TP': 4, 'FP': 24, 'FN': 19, 'total_n_pos_events': 23}
-ME1.wav {'TP': 9, 'FP': 18, 'FN': 2, 'total_n_pos_events': 11}
-ME2.wav {'TP': 41, 'FP': 27, 'FN': 0, 'total_n_pos_events': 41}
-R4_cleaned recording_13-10-17.wav {'TP': 19, 'FP': 14, 'FN': 0, 'total_n_pos_events': 19}
-R4_cleaned recording_16-10-17.wav {'TP': 30, 'FP': 8, 'FN': 0, 'total_n_pos_events': 30}
-R4_cleaned recording_17-10-17.wav {'TP': 36, 'FP': 9, 'FN': 0, 'total_n_pos_events': 36}
-R4_cleaned recording_TEL_19-10-17.wav {'TP': 52, 'FP': 12, 'FN': 2, 'total_n_pos_events': 54}
-R4_cleaned recording_TEL_20-10-17.wav {'TP': 64, 'FP': 8, 'FN': 0, 'total_n_pos_events': 64}
-R4_cleaned recording_TEL_23-10-17.wav {'TP': 84, 'FP': 8, 'FN': 0, 'total_n_pos_events': 84}
-R4_cleaned recording_TEL_24-10-17.wav {'TP': 99, 'FP': 14, 'FN': 0, 'total_n_pos_events': 99}
-R4_cleaned recording_TEL_25-10-17.wav {'TP': 99, 'FP': 9, 'FN': 0, 'total_n_pos_events': 99}
-file_423_487.wav {'TP': 57, 'FP': 13, 'FN': 0, 'total_n_pos_events': 57}
-file_97_113.wav {'TP': 11, 'FP': 27, 'FN': 109, 'total_n_pos_events': 120}
-
-Overall_scores: {'precision': 0.2911279078300433, 'recall': 0.4938446186969832, 'fmeasure (percentage)': 36.631}
+Evaluation for: TeamBEATs VAL
+BUK1_20181011_001004.wav {'TP': 13, 'FP': 22, 'FN': 18, 'total_n_pos_events': 31}
+BUK1_20181013_023504.wav {'TP': 3, 'FP': 206, 'FN': 21, 'total_n_pos_events': 24}
+BUK4_20161011_000804.wav {'TP': 1, 'FP': 22, 'FN': 46, 'total_n_pos_events': 47}
+BUK4_20171022_004304a.wav {'TP': 6, 'FP': 15, 'FN': 11, 'total_n_pos_events': 17}
+BUK5_20161101_002104a.wav {'TP': 39, 'FP': 7, 'FN': 49, 'total_n_pos_events': 88}
+BUK5_20180921_015906a.wav {'TP': 4, 'FP': 9, 'FN': 19, 'total_n_pos_events': 23}
+ME1.wav {'TP': 10, 'FP': 21, 'FN': 1, 'total_n_pos_events': 11}
+ME2.wav {'TP': 41, 'FP': 35, 'FN': 0, 'total_n_pos_events': 41}
+R4_cleaned recording_13-10-17.wav {'TP': 19, 'FP': 23, 'FN': 0, 'total_n_pos_events': 19}
+R4_cleaned recording_16-10-17.wav {'TP': 30, 'FP': 9, 'FN': 0, 'total_n_pos_events': 30}
+R4_cleaned recording_17-10-17.wav {'TP': 36, 'FP': 6, 'FN': 0, 'total_n_pos_events': 36}
+R4_cleaned recording_TEL_19-10-17.wav {'TP': 52, 'FP': 29, 'FN': 2, 'total_n_pos_events': 54}
+R4_cleaned recording_TEL_20-10-17.wav {'TP': 64, 'FP': 10, 'FN': 0, 'total_n_pos_events': 64}
+R4_cleaned recording_TEL_23-10-17.wav {'TP': 84, 'FP': 5, 'FN': 0, 'total_n_pos_events': 84}
+R4_cleaned recording_TEL_24-10-17.wav {'TP': 99, 'FP': 13, 'FN': 0, 'total_n_pos_events': 99}
+R4_cleaned recording_TEL_25-10-17.wav {'TP': 99, 'FP': 8, 'FN': 0, 'total_n_pos_events': 99}
+file_423_487.wav {'TP': 57, 'FP': 7, 'FN': 0, 'total_n_pos_events': 57}
+file_97_113.wav {'TP': 11, 'FP': 30, 'FN': 109, 'total_n_pos_events': 120}
+
+Overall_scores: {'precision': 0.348444259075038, 'recall': 0.525770811091538, 'fmeasure (percentage)': 41.912}
 ```
 
 ## Taking the idea further:
diff --git a/compute_metrics.sh b/compute_metrics.sh
old mode 100644
new mode 100755
index 639cd25..7328bcc
--- a/compute_metrics.sh
+++ b/compute_metrics.sh
@@ -1,14 +1,16 @@
 #!/bin/bash
 
 BASE_FOLDER=$1
+EVAL_CSV_PATH=$2
 CONFIG_PATH="/app/CONFIG.yaml"
 
 docker run -v $PWD:/app \
-            -v $BASE_FOLDER:/data \  
+            -v $BASE_FOLDER:/data \
+            -v $EVAL_CSV_PATH:/eval_folder/eval_out.csv \
             --gpus all \
             beats \
-            poetry run python evaluation/evaluation_metrics/evaluation.py \
-            -pred_file /data/eval_out.csv \
+            poetry run python /app/evaluate/evaluation_metrics/evaluation.py \
+            -pred_file /eval_folder/eval_out.csv \
             -ref_files_path /data/DCASE/Development_Set_annotations/Validation_Set \
             -team_name TeamBEATs \
             -dataset VAL \
diff --git a/dcase_setup.sh b/dcase_setup.sh
index 8d0d034..5d6d1e0 100755
--- a/dcase_setup.sh
+++ b/dcase_setup.sh
@@ -29,7 +29,7 @@ mkdir -p $TARGET_FOLDER
 ############################
 # Download the BEATs model #
 ############################
-MODEL_FOLDER=$BASE_FOLDER/BEATs
+MODEL_FOLDER=$BASE_FOLDER/model/BEATs
 mkdir -p $MODEL_FOLDER
 wget -O "$MODEL_FOLDER/BEATs_iter3_plus_AS2M.pt" "https://valle.blob.core.windows.net/share/BEATs/BEATs_iter3_plus_AS2M.pt?sv=2020-08-04&st=2023-03-01T07%3A51%3A05Z&se=2033-03-02T07%3A51%3A00Z&sr=c&sp=rl&sig=QJXmSJG9DbMKf48UDIU1MfzIro8HQOf3sqlNXiflY1I%3D" 
 
@@ -47,18 +47,21 @@ download_and_unzip "https://zenodo.org/record/6482837/files/Development_Set_anno
 # Acoustic data
 download_and_unzip "https://zenodo.org/record/6482837/files/Development_Set.zip?download=1" "$TARGET_FOLDER"
 
-###############################
-# Download the evaluation set #
-###############################
-mkdir -p "$TARGET_FOLDER/Development_Set/Evaluation_Set"
+#####################################################################
+# Download the evaluation set - OUTDATED AS THIS WAS FOR DCASE 2023 #
+#####################################################################
 
-download_and_unzip "https://zenodo.org/record/7879692/files/Annotations_only.zip?download=1" "$TARGET_FOLDER"
-mv "$TARGET_FOLDER/Annotations_only" "$TARGET_FOLDER/Development_Set_annotations/Evaluation_Set"
+
+
+#mkdir -p "$TARGET_FOLDER/Development_Set/Evaluation_Set"
+
+#download_and_unzip "https://zenodo.org/record/7879692/files/Annotations_only.zip?download=1" "$TARGET_FOLDER"
+#mv "$TARGET_FOLDER/Annotations_only" "$TARGET_FOLDER/Development_Set_annotations/Evaluation_Set"
 
 # Acoustic data
-for i in {1..3}
-do
-    download_and_unzip "https://zenodo.org/record/7879692/files/eval_$i.zip?download=1" "$TARGET_FOLDER/Development_Set/Evaluation_Set"
-done
+#for i in {1..3}
+#do
+#    download_and_unzip "https://zenodo.org/record/7879692/files/eval_$i.zip?download=1" "$TARGET_FOLDER/Development_Set/Evaluation_Set"
+#done
 
 
diff --git a/evaluate/evaluateDCASE.py b/evaluate/evaluateDCASE.py
index 0943572..cd68baa 100644
--- a/evaluate/evaluateDCASE.py
+++ b/evaluate/evaluateDCASE.py
@@ -72,7 +72,12 @@ def train_model(
 
     if pretrained_model:
         # Load the pretrained model
-        pretrained_model = ProtoBEATsModel.load_from_checkpoint(pretrained_model)
+        try:
+            pretrained_model = ProtoBEATsModel.load_from_checkpoint(pretrained_model)
+        except KeyError:
+            print("Failed to load the pretrained model. Please check the checkpoint file.")
+            return None
+
 
     # train the model
     trainer.fit(model, datamodule=datamodule_class)
@@ -569,6 +574,16 @@ def write_wav(
     training_config_path = os.path.join(version_path, "config.yaml")
     version_name = os.path.basename(version_path)
 
+    # Select 'set_type' depending on chosen status
+    if cfg["data"]["status"]=="train":
+        cfg["data"]["set_type"] = "Training_Set"
+
+    elif cfg["data"]["status"]=="validate":
+        cfg["data"]["set_type"] = "Validation_Set"
+
+    else:
+        cfg["data"]["set_type"] = "Evaluation_Set"
+
     # Get correct paths to dataset
     my_hash_dict = {
         "resample": cfg["data"]["resample"],
diff --git a/train_model.sh b/train_model.sh
index 1e37d51..2b1c64c 100755
--- a/train_model.sh
+++ b/train_model.sh
@@ -3,8 +3,10 @@
 BASE_FOLDER=$1
 CONFIG_PATH="/app/CONFIG.yaml"
 
-docker run -v $BASE_FOLDER:/data \
-            -v $PWD:/app \
-            --gpus all \
-            beats \
-            poetry run prototypicalbeats/trainer.py fit --config $CONFIG_PATH 
\ No newline at end of file
+# Check if BASE_FOLDER is not set or empty
+if [ -z "$BASE_FOLDER" ]; then
+    echo "Error: BASE_FOLDER is not specified."
+    exit 1
+fi
+
+docker run -v $BASE_FOLDER:/data -v $PWD:/app --gpus all beats poetry run prototypicalbeats/trainer.py fit --config $CONFIG_PATH 
\ No newline at end of file