diff --git a/.devcontainer/devcontainer-all-packages.sh b/.devcontainer/devcontainer-all-packages.sh
index 8634bf3..b1e2960 100755
--- a/.devcontainer/devcontainer-all-packages.sh
+++ b/.devcontainer/devcontainer-all-packages.sh
@@ -10,6 +10,7 @@ main() {
         build-essential
         ca-certificates
         ccache
+        cm-super
         curl
         gawk
         gnupg
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ef92958..22fe9d0 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -12,7 +12,7 @@ jobs:
 
     strategy:
       matrix:
-        ci_script: [pr_unittest]
+        ci_script: [pr_unittest, pr_evaluate_networks, pr_train_networks]
 
     steps:
       - name: Checkout
diff --git a/ci/pr_evaluate_networks.bash b/ci/pr_evaluate_networks.bash
new file mode 100755
index 0000000..b87d740
--- /dev/null
+++ b/ci/pr_evaluate_networks.bash
@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# Install radarmeetsvision
+pip install -e .
+
+RESULTS_DIR=tests/resources/results
+if [ -d $RESULTS_DIR ]; then
+    rm -r $RESULTS_DIR
+fi
+rm -rf tests/resources/*.pkl
+
+# Run the evaluation script
+python3 scripts/evaluation/evaluate_networks.py --dataset tests/resources --config tests/resources/test_evaluation.json --output tests/resources --network tests/resources
+
+TEX_FILE="tests/resources/results/results_table0.tex"
+if [[ -f "$TEX_FILE" && -s "$TEX_FILE" ]]; then
+    echo "Evaluation script successful, .tex table exists and is not empty."
+else
+    echo "Evaluation script failed, .tex table does not exist or is empty."
+    exit 1
+fi
diff --git a/ci/pr_train_networks.bash b/ci/pr_train_networks.bash
new file mode 100755
index 0000000..d93b07e
--- /dev/null
+++ b/ci/pr_train_networks.bash
@@ -0,0 +1,55 @@
+#!/bin/bash
+
+checkpoints=tests/resources
+save_path=tests/resources
+datasets=tests/resources
+
+config_path=tests/resources
+config_radar=$config_path/test_train_radar.json
+config_metric=$config_path/test_train_metric.json
+config_relative=$config_path/test_train_relative.json
+
+# Install radarmeetsvision
+pip install -e .
+
+# RADAR TRAINING (depth prior + 2 output channels)
+python3 scripts/train.py \
+--checkpoints $checkpoints \
+--config $config_radar \
+--datasets $datasets \
+--results ""
+
+if [ $? -eq 0 ]; then
+    echo "Radar training script successful"
+else
+    echo "Training script failed"
+    exit 1
+fi
+
+# RGB TRAINING (no depth prior + 1 output channel)
+python3 scripts/train.py \
+--checkpoints $checkpoints \
+--config $config_metric \
+--datasets $datasets \
+--results ""
+
+if [ $? -eq 0 ]; then
+    echo "RGB training script successful"
+else
+    echo "Training script failed"
+    exit 1
+fi
+
+# Relative RGB TRAINING (no depth prior + 1 output channel)
+python3 scripts/train.py \
+--checkpoints $checkpoints \
+--config $config_relative \
+--datasets $datasets \
+--results ""
+
+if [ $? -eq 0 ]; then
+    echo "Relative RGB training script successful"
+else
+    echo "Training script failed"
+    exit 1
+fi
diff --git a/ci/pr_unittest.bash b/ci/pr_unittest.bash
index 63a60d3..731f6c8 100755
--- a/ci/pr_unittest.bash
+++ b/ci/pr_unittest.bash
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-if pip install .
+if pip install -e .
 then
     echo "Installation successful"
 else
diff --git a/scripts/evaluation/config.json b/scripts/evaluation/config.json
new file mode 100644
index 0000000..cc25b37
--- /dev/null
+++ b/scripts/evaluation/config.json
@@ -0,0 +1,77 @@
+{
+    "scenarios": {
+        "Industrial Hall": "maschinenhalle0",
+        "Agricultural Field": "outdoor0",
+        "Rhône Glacier": "rhone_flight"
+    },
+    "networks": {
+        "Metric Depth \\cite{depthanythingv2}-S": "rgb_s_bs8_e9.pth",
+        "Metric Depth \\cite{depthanythingv2}-B": "rgb_b_bs4_e8.pth",
+        "Scaled Relative Depth \\cite{depthanythingv2}-S": "relrgb_s_bs8_e9.pth",
+        "Scaled Relative Depth \\cite{depthanythingv2}-B": "relrgb_b_bs4_e9.pth",
+        "Ours-S": "radar_s_bs8_e19.pth",
+        "Ours-B": "radar_b_bs4_e21.pth"
+    },
+    "Metric Depth \\cite{depthanythingv2}-S": {
+        "use_depth_prior": false,
+        "output_channels": 1,
+        "relative_depth": 0,
+        "depth_min": 0.19983673095703125,
+        "depth_max": 120.49285888671875,
+        "encoder": "vits",
+        "marker": "X",
+        "plot_prediction": 0
+    },
+    "Metric Depth \\cite{depthanythingv2}-B": {
+        "use_depth_prior": false,
+        "output_channels": 1,
+        "relative_depth": 0,
+        "depth_min": 0.19983673095703125,
+        "depth_max": 120.49285888671875,
+        "encoder": "vitb",
+        "marker": "X",
+        "plot_prediction": 0
+    },
+    "Scaled Relative Depth \\cite{depthanythingv2}-S": {
+        "use_depth_prior": false,
+        "output_channels": 1,
+        "relative_depth": 1,
+        "depth_min": 0.0,
+        "depth_max": 1.0,
+        "encoder": "vits",
+        "marker": "D",
+        "plot_prediction": 0
+    },
+    "Scaled Relative Depth \\cite{depthanythingv2}-B": {
+        "use_depth_prior": false,
+        "output_channels": 1,
+        "relative_depth": 1,
+        "depth_min": 0.0,
+        "depth_max": 1.0,
+        "encoder": "vitb",
+        "marker": "D",
+        "plot_prediction": 0
+    },
+    "Ours-S": {
+        "use_depth_prior": true,
+        "output_channels": 2,
+        "relative_depth": 0,
+        "depth_min": 0.19983673095703125,
+        "depth_max": 120.49285888671875,
+        "encoder": "vits",
+        "marker": "o",
+        "plot_prediction": 0
+    },
+    "Ours-B": {
+        "use_depth_prior": true,
+        "output_channels": 2,
+        "relative_depth": 0,
+        "depth_min": 0.19983673095703125,
+        "depth_max": 120.49285888671875,
+        "encoder": "vitb",
+        "marker": "o",
+        "plot_prediction": 1
+    },
+    "height": 480,
+    "width": 640
+}
diff --git a/scripts/evaluation/create_scatter_plot.py b/scripts/evaluation/create_scatter_plot.py
new file mode 100644
index 0000000..c4319f2
--- /dev/null
+++ b/scripts/evaluation/create_scatter_plot.py
@@ -0,0 +1,98 @@
+######################################################################
+#
+# Copyright (c) 2024 ETHZ Autonomous Systems Lab. All rights reserved.
+#
+######################################################################
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+
+from matplotlib.gridspec import GridSpec
+from pathlib import Path
+from PIL import Image
+
+mpl.rcParams['font.size'] = 8
+mpl.rcParams['figure.figsize'] = [9, 2.7]
+mpl.rcParams['lines.linewidth'] = 0.6
+mpl.rcParams['grid.linewidth'] = 0.5
+mpl.rcParams['axes.linewidth'] = 0.6
+mpl.rcParams['axes.labelsize'] = 8
+mpl.rcParams['axes.titlesize'] = 8
+mpl.rcParams['legend.fontsize'] = 8
+mpl.rcParams['xtick.labelsize'] = 8
+mpl.rcParams['ytick.labelsize'] = 8
+mpl.rcParams['text.usetex'] = True
+mpl.rcParams['font.family'] = 'serif'
+
+def create_scatter_plot(results_per_scenario_sample, config, output_dir, subsample=10):
+    scenario_color = {
+        'Industrial Hall': 'k',
+        'Agricultural Field': 'green',
+        'Rhône Glacier': 'navy'
+    }
+    label_dict = {
+        "Metric Depth \\cite{depthanythingv2}-B": "Metric Depth-B",
+        "Ours-B": "Ours-B"
+    }
+    samples = [['00000_rgb.jpg', '00000_dp.jpg'],
+               ['00050_rgb.jpg', '00050_dp.jpg'],
+               ['00250_rgb.jpg', '00250_dp.jpg']]
+
+    sample_dir = Path('scripts/evaluation/samples')
+    img_out = None
+    for i, sample in enumerate(samples):
+        rgb_file = sample_dir / sample[0]
+        dp_file = sample_dir / sample[1]
+
+        rgb = np.array(Image.open(rgb_file))
+        dp = np.array(Image.open(dp_file))
+        img = np.concatenate((rgb, dp), axis=1)
+        if img_out is None:
+            img_out = img
+        else:
+            img_out = np.concatenate((img_out, img), axis=0)
+    border=5
+    img_out[:border, :] = (0.0, 0.0, 0.0)
+    img_out[-border:, :] = (0.0, 0.0, 0.0)
+    img_out[:, :border] = (0.0, 0.0, 0.0)
+    img_out[:, -border:] = (0.0, 0.0, 0.0)
+
+
+    fig = plt.figure(figsize=(9, 2.7))  # Adjust the figure size if needed
+    gs = GridSpec(1, 2, width_ratios=[1, 2])  # Set the width ratios to 1:2
+
+    ax0 = fig.add_subplot(gs[0])
+    ax0.imshow(img_out)
+    ax0.axis('off')
+
+    ax1 = fig.add_subplot(gs[1])
+    for scenario_key in config['scenarios'].keys():
+        for i, network_key in enumerate(config['networks'].keys()):
+            if '-B' in network_key and not 'Scaled' in network_key:
+                average_depths = results_per_scenario_sample[scenario_key][network_key]['average_depth']
+                abs_rel_values = results_per_scenario_sample[scenario_key][network_key]['abs_rel']
+                average_depths_subsampled = average_depths[::subsample]
+                abs_rel_values_subsampled = abs_rel_values[::subsample]
+                label = label_dict[network_key] + ' ' + (scenario_key if 'Metric' in network_key else '')
+                ax1.scatter(average_depths_subsampled, abs_rel_values_subsampled, label=label, marker=config[network_key]['marker'], c=scenario_color[scenario_key], s=25, alpha=0.5)
+
+
+    # Set axis labels, title, and legend
+    plt.xlabel('Average Scene Depth [m]')
+    plt.ylabel('Absolute Relative Error [ ]')
+    plt.legend(loc='upper right')
+    ax1.grid()
+    plt.tight_layout()
+
+    # Save the plot
+    output_file = Path(output_dir) / f'results_overview.png'
+    plt.savefig(str(output_file), transparent=True, bbox_inches='tight', dpi=400)
+    plt.close()
+
+    # Post-process the saved image to crop any unnecessary white space
+    img = Image.open(str(output_file))
+    img = img.convert("RGBA")
+    bbox = img.getbbox()
+    cropped_img = img.crop(bbox)
+    cropped_img.save(str(output_file))
diff --git a/scripts/evaluation/evaluate_networks.py b/scripts/evaluation/evaluate_networks.py
new file mode 100644
index 0000000..1049fca
--- /dev/null
+++ b/scripts/evaluation/evaluate_networks.py
@@ -0,0 +1,121 @@
+######################################################################
+#
+# Copyright (c) 2024 ETHZ Autonomous Systems Lab. All rights reserved.
+#
+######################################################################
+
+import argparse
+import logging
+import pickle
+import radarmeetsvision as rmv
+
+from pathlib import Path
+from results_table_template import generate_tables
+from create_scatter_plot import create_scatter_plot
+
+logger = logging.getLogger(__name__)
+
+
+class Evaluation:
+    def __init__(self, config, scenario_key, network_key, args):
+        self.results_per_sample = {}
+        self.results_dict = {}
+        self.interface = rmv.Interface()
+        self.networks_dir = Path(args.network)
+        self.datasets_dir = args.dataset
+        self.results, self.results_per_sample = None, None
+        self.setup_interface(config, scenario_key, network_key)
+        self.run(network_key)
+
+    def run(self, network_key):
+        self.interface.validate_epoch(0, self.loader)
+        self.results, self.results_per_sample = self.interface.get_results()
+        self.results['method'] = network_key
+
+    def setup_interface(self, config, scenario_key, network_key):
+        self.interface.set_epochs(1)
+
+        network_config = config[network_key]
+        self.interface.set_encoder(network_config['encoder'])
+
+        depth_min, depth_max = network_config['depth_min'], network_config['depth_max']
+        self.interface.set_depth_range((depth_min, depth_max))
+        self.interface.set_output_channels(network_config['output_channels'])
+        self.interface.set_use_depth_prior(network_config['use_depth_prior'])
+
+        network_file = config['networks'][network_key]
+        if network_file is not None:
+            network_file = self.networks_dir / network_file
+        self.interface.load_model(pretrained_from=network_file)
+
+        self.interface.set_size(config['height'], config['width'])
+        self.interface.set_batch_size(1)
+        self.interface.set_criterion()
+
+        dataset_list = [config['scenarios'][scenario_key]]
+        self.loader, _ = self.interface.get_dataset_loader('val_all', self.datasets_dir, dataset_list)
+
+    def get_results_per_sample(self):
+        return self.results_per_sample
+
+    def get_results(self):
+        return self.results
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Create evaluation results for paper')
+    parser.add_argument('--config', type=str, default='scripts/evaluation/config.json', help='Path to the JSON config file.')
+    parser.add_argument('--dataset', type=str, required=True, help='Path to the dataset directory')
+    parser.add_argument('--output', type=str, required=True, help='Path to the output directory')
+    parser.add_argument('--network', type=str, help='Path to the network directory')
+    args = parser.parse_args()
+
+    rmv.setup_global_logger()
+
+    config = rmv.load_config(args.config)
+
+    results_per_scenario = {}
+    results_per_scenario_sample = {}
+    results_per_scenario_file = Path(args.output) / "results_per_scenario.pkl"
+    results_per_scenario_sample_file = Path(args.output) / "results_per_scenario_sample.pkl"
+    if not results_per_scenario_file.is_file() or not results_per_scenario_sample_file.is_file():
+        for scenario_key in config['scenarios'].keys():
+            results_per_scenario[scenario_key] = []
+            results_per_sample = {}
+
+            for network_key in config['networks'].keys():
+                logger.info(f'Evaluation: {scenario_key} {network_key}')
+                eval_obj = Evaluation(config, scenario_key, network_key, args)
+                results_per_sample[network_key] = eval_obj.get_results_per_sample()
+
+                results_dict = eval_obj.get_results()
+                if results_dict:
+                    # Used to generate main results table
+                    results_per_scenario[scenario_key].append(results_dict.copy())
+                    logger.info(f'{scenario_key} {network_key} {results_dict["abs_rel"]:.3f}')
+
+            # Used to generate visual grid
+            results_per_scenario_sample[scenario_key] = results_per_sample
+
+        with results_per_scenario_file.open('wb') as f:
+            pickle.dump(results_per_scenario, f)
+
+        with results_per_scenario_sample_file.open('wb') as f:
+            pickle.dump(results_per_scenario_sample, f)
+
+    else:
+        with results_per_scenario_file.open('rb') as f:
+            results_per_scenario = pickle.load(f)
+
+        with results_per_scenario_sample_file.open('rb') as f:
+            results_per_scenario_sample = pickle.load(f)
+
+    # Generate the main results table
+    if results_per_scenario:
+        generate_tables(args.output, results_per_scenario)
+
+    create_scatter_plot(results_per_scenario_sample, config, args.output)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/evaluation/results_table_template.py b/scripts/evaluation/results_table_template.py
new file mode 100644
index 0000000..05cfc62
--- /dev/null
+++ b/scripts/evaluation/results_table_template.py
@@ -0,0 +1,136 @@
+######################################################################
+#
+# Copyright (c) 2024 ETHZ Autonomous Systems Lab. All rights reserved.
+#
+######################################################################
+
+from datetime import datetime
+from pathlib import Path
+
+def generate_table_top(scenarios):
+    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    header = f"% DO NOT MODIFY, AUTOGENERATED AT {current_time}\n"
+    header += "\\renewcommand{\\thetable}{\\arabic{table}}\n\\captionsetup[table]{labelformat=simple, labelsep=colon, name=Tab.}\n"
+    header += "\\begin{table*}[t]\n\\centering\n\\sisetup{detect-all}\n\\NewDocumentCommand{\\B}{}{\\fontseries{b}\\selectfont}\n"
+    
+    # Define the custom underline command
+    header += (
+        "\\def\\Decimal{.000}% Corresponds to the \".2\" of \"table-format\"\n"
+        "\\def\\Uline#1{\\Ulinehelp#1 }\n"
+        "\\def\\Ulinehelp#1.#2 {%\n"
+        "  #1.#2\\setbox0=\\hbox{#1\\Decimal}\\hspace{-\\wd0}{\\if\\relax#2\\relax%\n"
+        "    \\uline{\\phantom{#1.0}}\\else\\uline{\\phantom{#1.#2}}\\fi}%\n}\n"
+    )
+
+    column_format = "\n@{}\nl\n" + "".join([f"S[table-format=2.3]\nS[table-format=2.3]\nS[table-format=2.3]\n" for _ in scenarios]) + "@{}\n"
+    header += f"\\begin{{tabular}}{{{column_format}}}\n\\toprule\n"
+
+    scenario_headers = " & " + " & ".join([f"\\multicolumn{{3}}{{c}}{{{scenario}}}" for scenario in scenarios]) + " \\\\\n"
+    metric_headers = " ".join([f"\\cmidrule(lr){{{3*i+2}-{3*i+4}}}" for i in range(len(scenarios))]) + "\n"
+    metrics = "Models & " + " & ".join(["{AbsRel $(\\downarrow)$} & {$\\delta_1 (\\uparrow)$} & {RMSE $(\\downarrow)$}" for _ in scenarios]) + " \\\\\n\\midrule\n"
+
+    return header + scenario_headers + metric_headers + metrics
+
+def generate_table_bottom():
+    return "\\bottomrule\n\\end{tabular}\n\\label{tab:results}\\caption{Comparison of metric and scaled relative Depth Anything V2 \\cite{depthanythingv2} with our approach. The suffix -S and -B indicate the pre-trained network size, small and base respectively. The best values are in bold, and the second-best values are underlined.}\n\\end{table*}"
+
+def get_best_values(result_dict, scenarios):
+    num_scenarios = len(scenarios)
+
+    # Initialize best and second-best values for `AbsRel`, `d1`, and `RMSE`
+    best_abs_rel = [float('inf')] * num_scenarios
+    second_best_abs_rel = [float('inf')] * num_scenarios
+    best_d1 = [-float('inf')] * num_scenarios
+    second_best_d1 = [-float('inf')] * num_scenarios
+    best_rmse = [float('inf')] * num_scenarios
+    second_best_rmse = [float('inf')] * num_scenarios
+
+    # Iterate through each scenario
+    for j, scenario in enumerate(scenarios):
+        for result in result_dict[scenario]:
+            abs_rel = result['abs_rel']
+            d1 = result['d1']
+            rmse = result['rmse']
+
+            # Update best and second-best for AbsRel
+            if abs_rel < best_abs_rel[j]:
+                second_best_abs_rel[j] = best_abs_rel[j]
+                best_abs_rel[j] = abs_rel
+            elif abs_rel < second_best_abs_rel[j]:
+                second_best_abs_rel[j] = abs_rel
+
+            # Update best and second-best for d1
+            if d1 > best_d1[j]:
+                second_best_d1[j] = best_d1[j]
+                best_d1[j] = d1
+            elif d1 > second_best_d1[j]:
+                second_best_d1[j] = d1
+
+            # Update best and second-best for RMSE
+            if rmse < best_rmse[j]:
+                second_best_rmse[j] = best_rmse[j]
+                best_rmse[j] = rmse
+            elif rmse < second_best_rmse[j]:
+                second_best_rmse[j] = rmse
+
+    return best_abs_rel, second_best_abs_rel, best_d1, second_best_d1, best_rmse, second_best_rmse
+
+def get_model_lines(result_dict, scenarios):
+    results_table_template_models_line = "{} " + " ".join(["& {} & {} & {}" for _ in range(len(scenarios))]) + " \\\\\n"
+
+    best_abs_rel, second_best_abs_rel, best_d1, second_best_d1, best_rmse, second_best_rmse = get_best_values(result_dict, scenarios)
+
+    model_lines = []
+    methods = [result['method'] for result in result_dict[scenarios[0]]]
+
+    for i, method in enumerate(methods):
+        metric_list = [method]
+        for j, scenario in enumerate(scenarios):
+            abs_rel = result_dict[scenario][i]['abs_rel']
+            d1 = result_dict[scenario][i]['d1']
+            rmse = result_dict[scenario][i]['rmse']
+
+            # Mark best and second-best values with \B and \Uline respectively
+            if abs_rel == best_abs_rel[j]:
+                abs_rel = "\\B " + f"{abs_rel:.3f}"
+            elif abs_rel == second_best_abs_rel[j]:
+                abs_rel = "\\Uline " + f"{abs_rel:.3f}"
+            else:
+                abs_rel = f"{abs_rel:.3f}"
+
+            if d1 == best_d1[j]:
+                d1 = "\\B " + f"{d1:.3f}"
+            elif d1 == second_best_d1[j]:
+                d1 = "\\Uline " + f"{d1:.3f}"
+            else:
+                d1 = f"{d1:.3f}"
+
+            if rmse == best_rmse[j]:
+                rmse = "\\B " + f"{rmse:.3f}"
+            elif rmse == second_best_rmse[j]:
+                rmse = "\\Uline " + f"{rmse:.3f}"
+            else:
+                rmse = f"{rmse:.3f}"
+
+            metric_list.append(abs_rel)
+            metric_list.append(d1)
+            metric_list.append(rmse)
+
+        line = results_table_template_models_line.format(*metric_list)
+        model_lines.append(line)
+
+    return model_lines
+
+def generate_tables(output_dir, result_dict):
+    scenarios = list(result_dict.keys())
+
+    results_table = generate_table_top(scenarios)
+    results_table += "".join(get_model_lines(result_dict, scenarios))
+    results_table += generate_table_bottom()
+
+    results_dir = Path(output_dir) / 'results'
+    results_dir.mkdir(exist_ok=True)
+    filename = results_dir / 'results_table0.tex'
+    with filename.open('w') as f:
+        f.write(results_table)
diff --git a/scripts/evaluation/samples/00000_dp.jpg b/scripts/evaluation/samples/00000_dp.jpg
new file mode 100644
index 0000000..aa63dba
Binary files /dev/null and b/scripts/evaluation/samples/00000_dp.jpg differ
diff --git a/scripts/evaluation/samples/00000_rgb.jpg b/scripts/evaluation/samples/00000_rgb.jpg
new file mode 100644
index 0000000..ce7f7b7
Binary files /dev/null and b/scripts/evaluation/samples/00000_rgb.jpg differ
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new file mode 100644
index 0000000..4c2b4dc
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diff --git a/scripts/evaluation/samples/00050_rgb.jpg b/scripts/evaluation/samples/00050_rgb.jpg
new file mode 100644
index 0000000..b50703a
Binary files /dev/null and b/scripts/evaluation/samples/00050_rgb.jpg differ
diff --git a/scripts/evaluation/samples/00250_dp.jpg b/scripts/evaluation/samples/00250_dp.jpg
new file mode 100644
index 0000000..b960383
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diff --git a/scripts/evaluation/samples/00250_rgb.jpg b/scripts/evaluation/samples/00250_rgb.jpg
new file mode 100644
index 0000000..1f09720
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diff --git a/tests/resources/test_evaluation.json b/tests/resources/test_evaluation.json
new file mode 100644
index 0000000..0251721
--- /dev/null
+++ b/tests/resources/test_evaluation.json
@@ -0,0 +1,40 @@
+{
+    "scenarios": {
+        "Training": "tiny_dataset",
+        "Industrial Hall": "tiny_dataset_validation"
+    },
+    "networks": {
+        "RGB": null,
+        "Naive": null,
+        "Ours-S": null
+    },
+    "RGB": {
+        "use_depth_prior": false,
+        "output_channels": 1,
+        "relative_depth": 0,
+        "depth_min": 0.19983673095703125,
+        "depth_max": 120.49285888671875,
+        "encoder": "vits",
+        "plot_prediction": 0
+    },
+    "Ours-S": {
+        "use_depth_prior": true,
+        "output_channels": 2,
+        "relative_depth": 0,
+        "depth_min": 0.19983673095703125,
+        "depth_max": 120.49285888671875,
+        "encoder": "vits",
+        "plot_prediction": 1
+    },
+    "Naive": {
+        "use_depth_prior": false,
+        "output_channels": 1,
+        "relative_depth": 1,
+        "depth_min": 0.0,
+        "depth_max": 1.0,
+        "encoder": "vits",
+        "plot_prediction": 0
+    },
+    "height": 480,
+    "width": 640
+}
diff --git a/tests/resources/test_train_metric.json b/tests/resources/test_train_metric.json
new file mode 100644
index 0000000..4cbb8f7
--- /dev/null
+++ b/tests/resources/test_train_metric.json
@@ -0,0 +1,21 @@
+{
+    "epochs": 1,
+    "encoder": "vits",
+    "depth_min": 0.19983673095703125,
+    "depth_max": 120.49285888671875,
+    "output_channels": 1,
+    "use_depth_prior": 0,
+    "pretrained_from": null,
+    "height": 518,
+    "width": 518,
+    "task": {
+        "train_all": {
+            "dir": "",
+            "datasets": ["tiny_dataset"]
+        },
+        "val_all": {
+            "dir": "",
+            "datasets": ["tiny_dataset_validation"]
+        }
+    }
+}
diff --git a/tests/resources/test_train_radar.json b/tests/resources/test_train_radar.json
new file mode 100644
index 0000000..94d49d4
--- /dev/null
+++ b/tests/resources/test_train_radar.json
@@ -0,0 +1,21 @@
+{
+    "epochs": 1,
+    "encoder": "vits",
+    "depth_min": 0.19983673095703125,
+    "depth_max": 120.49285888671875,
+    "output_channels": 2,
+    "use_depth_prior": 1,
+    "pretrained_from": null,
+    "height": 518,
+    "width": 518,
+    "task": {
+        "train_all": {
+            "dir": "",
+            "datasets": ["tiny_dataset"]
+        },
+        "val_all": {
+            "dir": "",
+            "datasets": ["tiny_dataset_validation"]
+        }
+    }
+}
diff --git a/tests/resources/test_train_relative.json b/tests/resources/test_train_relative.json
new file mode 100644
index 0000000..856ecdd
--- /dev/null
+++ b/tests/resources/test_train_relative.json
@@ -0,0 +1,21 @@
+{
+    "epochs": 1,
+    "encoder": "vits",
+    "depth_min": 0.0,
+    "depth_max": 1.0,
+    "output_channels": 1,
+    "use_depth_prior": 0,
+    "pretrained_from": null,
+    "height": 518,
+    "width": 518,
+    "task": {
+        "train_all": {
+            "dir": "",
+            "datasets": ["tiny_dataset"]
+        },
+        "val_all": {
+            "dir": "",
+            "datasets": ["tiny_dataset_validation"]
+        }
+    }
+}