diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..9f21b54
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+/venv/
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..94a25f7
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/Pot-plante.tfrecord b/Pot-plante.tfrecord
new file mode 100644
index 0000000..e758d36
Binary files /dev/null and b/Pot-plante.tfrecord differ
diff --git a/Pot-plante_label_map.pbtxt b/Pot-plante_label_map.pbtxt
new file mode 100644
index 0000000..7c8cc54
--- /dev/null
+++ b/Pot-plante_label_map.pbtxt
@@ -0,0 +1,30 @@
+item {
+    name: "plante_dans_pot_couchee",
+    id: 1,
+    display_name: "plante_dans_pot_couchee"
+}
+item {
+    name: "plante_dans_pot_debout",
+    id: 2,
+    display_name: "plante_dans_pot_debout"
+}
+item {
+    name: "plante_fragile_couchee",
+    id: 3,
+    display_name: "plante_fragile_couchee"
+}
+item {
+    name: "plante_fragile_debout",
+    id: 4,
+    display_name: "plante_fragile_debout"
+}
+item {
+    name: "pot_vide_couche",
+    id: 5,
+    display_name: "pot_vide_couche"
+}
+item {
+    name: "pot_vide_debout",
+    id: 6,
+    display_name: "pot_vide_debout"
+}
diff --git a/modele.py b/modele.py
new file mode 100644
index 0000000..926c522
--- /dev/null
+++ b/modele.py
@@ -0,0 +1,42 @@
+import cv2 as cv
+import numpy as np
+import tensorflow as tf
+
+model = tf.saved_model.load('Pot-plante.tfrecord')
+
+cap = cv2.VideoCapture(0)
+if not cap.isOpened():
+    print("Cannot open camera")
+    exit()
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        print("Can't receive frame (stream end?). Exiting ...")
+        break
+
+    # Prétraiter l'image pour l'entrée du modèle
+    input_image = cv2.resize(frame, (300, 300))
+    input_image = input_image / 127.5 - 1.0  # Normalisation
+
+    # Effectuer la détection d'objets avec le modèle TensorFlow
+    detections = model(tf.convert_to_tensor(np.expand_dims(input_image, axis=0), dtype=tf.float32))
+
+    # Dessiner les boîtes englobantes sur l'image
+    for detection in detections['detection_boxes'][0].numpy():
+        ymin, xmin, ymax, xmax = detection
+        xmin = int(xmin * frame.shape[1])
+        xmax = int(xmax * frame.shape[1])
+        ymin = int(ymin * frame.shape[0])
+        ymax = int(ymax * frame.shape[0])
+
+        cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
+
+    # Afficher le cadre résultant
+    cv2.imshow("Object Detection", frame)
+
+    if cv2.waitKey(1) == ord("q"):
+        break
+
+cap.release()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/train.py b/train.py
new file mode 100644
index 0000000..e5c0b99
--- /dev/null
+++ b/train.py
@@ -0,0 +1,25 @@
+import tensorflow as tf
+
+# Définir la fonction pour parser les exemples TFRecord (similaire à ce que vous avez utilisé)
+def _parse_function(proto):
+    keys_to_features = {
+        'image/encoded': tf.io.FixedLenFeature([], tf.string),
+        'image/format': tf.io.FixedLenFeature([], tf.string),
+        'image/object/class/label': tf.io.FixedLenFeature([], tf.int64),  # Assurez-vous que le type correspond
+        'image/object/bbox/xmin': tf.io.FixedLenFeature([], tf.float32),
+        'image/object/bbox/ymin': tf.io.FixedLenFeature([], tf.float32),
+        'image/object/bbox/xmax': tf.io.FixedLenFeature([], tf.float32),
+        'image/object/bbox/ymax': tf.io.FixedLenFeature([], tf.float32),
+    }
+    parsed_features = tf.io.parse_single_example(proto, keys_to_features)
+    return parsed_features
+
+
+# Charger les TFRecords
+tfrecords_path = "Pot-plante.tfrecord"
+dataset = tf.data.TFRecordDataset(tfrecords_path)
+
+# Afficher les informations pour chaque exemple TFRecord
+for raw_record in dataset.take(5):  # Prenez les 5 premiers exemples pour illustration
+    parsed_record = _parse_function(raw_record.numpy())
+    print(parsed_record)