diff --git a/src/scripts/benchmark_binary.py b/src/scripts/benchmark_binary.py
new file mode 100644
index 0000000..479bbc5
--- /dev/null
+++ b/src/scripts/benchmark_binary.py
@@ -0,0 +1,84 @@
+#!/usr/bin/python3
+#
+# Steps to use:
+# - modify src/scripts/generate_binaries.sh to suit your needs
+# - execute the following commands:
+# ```bash
+# src/scripts/generate_binaries.sh
+# python -i src/scripts/benchmark_binary.py
+# >>> compare_binaries(["binaries/"+i for i in os.listdir("binaries")], tries=4, dataset='train')
+# ```
+# tries is the number of epochs to train on and dataset is the dataset to use ('train' or 't10k')
+import subprocess
+import json
+import os
+
+from matplotlib import pyplot as plt
+
+def train(binary, base_network, tries=3, dataset="train"):
+    temp_out = f".cache/tmp-{abs(hash(binary))}.bin"
+    results = []
+    fail = os.system(f"{binary} train --dataset mnist --images data/mnist/{dataset}-images-idx3-ubyte --labels data/mnist/{dataset}-labels-idx1-ubyte --epochs 1 -o {temp_out} --recover {base_network}")
+    output = subprocess.check_output([
+        binary,
+        'test',
+        '--modele', temp_out,
+        '-d', 'mnist',
+        '-i', 'data/mnist/t10k-images-idx3-ubyte',
+        '-l', "data/mnist/t10k-labels-idx1-ubyte"
+    ]).decode("utf-8")
+    results.append(float(output.split('\r')[-1].split(" ")[-1].split("%")[0]))
+    for i in range(tries-1):
+        if fail == 0:
+            fail = os.system(f"{binary} train --dataset mnist --images data/mnist/{dataset}-images-idx3-ubyte --labels data/mnist/{dataset}-labels-idx1-ubyte --epochs 1 --out {temp_out} --recover {temp_out}")
+            output = subprocess.check_output([
+                binary,
+                'test',
+                '--modele', temp_out,
+                '-d', 'mnist',
+                '-i', 'data/mnist/t10k-images-idx3-ubyte',
+                '-l', "data/mnist/t10k-labels-idx1-ubyte"
+            ]).decode("utf-8")
+            results.append(float(output.split('\r')[-1].split(" ")[-1].split("%")[0]))
+        else:
+            results.append(results[-1])
+
+    return results
+
+def create_base_network(binary, file):
+    os.system(f"{binary} train --dataset mnist --images data/mnist/train-images-idx3-ubyte --labels data/mnist/train-labels-idx1-ubyte --epochs 0 --out {file}")
+
+
+def compare_binaries(binaries, tries=3, dataset="train"):
+    print(f"========== {len(binaries)} Fichiers chargés ==========")
+    base_net = f".cache/basenet-{abs(hash(''.join(binaries)))}.bin"
+    create_base_network(binaries[0], base_net)
+    results = []
+    for i in range(len(binaries)):
+        binary = binaries[i]
+        print(f"========== Benchmmark de {binary} ({1+i}/{len(binaries)}) ==========")
+        try:
+            results.append(train(binary, base_net, tries, dataset=dataset))
+        except:
+            print(f"========== Erreur sur {binary} ==========")
+            results.append(0)
+
+    x = [i for i in range(tries)]
+
+    fig, ax = plt.subplots()
+    
+    res = []
+    for i in range(len(binaries)):
+        if results[i] != 0:
+            res.append(ax.plot(x, results[i])[0])
+            res[i].set_label(binaries[i])
+    
+    ax.set_ylabel("Taux de réussite (%)")
+    ax.set_xlabel("Nombre de batchs")
+
+    ax.legend()
+
+    plt.ylim(0, 100)
+    plt.show()
+    return results
+    
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