diff --git a/src/cnn/include/models.h b/src/cnn/include/models.h
index 1c92596..748f20e 100644
--- a/src/cnn/include/models.h
+++ b/src/cnn/include/models.h
@@ -22,6 +22,13 @@ Network* create_network_alexnet(float learning_rate, int dropout, int activation
 */
 Network* create_network_VGG16(float learning_rate, int dropout, int activation, int initialisation, int size_output);
 
+
+/*
+* Renvoie un réseau suivant l'architecture VGG16 originel pour prendre en entrée 3x227x227
+* et une sortie de taille 1 000
+*/
+Network* create_network_VGG16_227(float learning_rate, int dropout, int activation, int initialisation);
+
 /*
 * Renvoie un réseau sans convolution, similaire à celui utilisé dans src/dense
 */
diff --git a/src/cnn/models.c b/src/cnn/models.c
index 8061a0e..7f3130a 100644
--- a/src/cnn/models.c
+++ b/src/cnn/models.c
@@ -66,6 +66,37 @@ Network* create_network_VGG16(float learning_rate, int dropout, int activation,
     return network;
 }
 
+Network* create_network_VGG16_227(float learning_rate, int dropout, int activation, int initialisation) {
+    Network* network = create_network(22, learning_rate, dropout, initialisation, 227, 3);
+    add_convolution(network, 3, 64, 1, 1, activation); // Conv3-64
+    add_convolution(network, 3, 64, 1, 1, activation); // Conv3-64
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 128, 1, 1, activation); // Conv3-128
+    add_convolution(network, 1, 128, 1, 0, activation); // Conv1-128
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 256, 1, 1, activation); // Conv3-256
+    add_convolution(network, 3, 256, 1, 1, activation); // Conv3-256
+    add_convolution(network, 1, 256, 1, 0, activation); // Conv1-256
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 512, 1, 1, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 1, activation); // Conv3-512
+    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_convolution(network, 3, 512, 1, 1, activation); // Conv3-512
+    add_convolution(network, 3, 512, 1, 1, activation); // Conv3-512
+    add_convolution(network, 1, 512, 1, 0, activation); // Conv1-512
+    add_average_pooling(network, 2, 2, 0); // Max Pool
+
+    add_dense_linearisation(network, 4096, activation);
+    add_dense(network, 4096, activation);
+    add_dense(network, 1000, SOFTMAX);
+    return network;
+}
+
 Network* create_simple_one(float learning_rate, int dropout, int activation, int initialisation, int input_width, int input_depth) {
     Network* network = create_network(3, learning_rate, dropout, initialisation, input_width, input_depth);
     add_dense_linearisation(network, 80, activation);