diff --git a/src/cnn/cnn.c b/src/cnn/cnn.c
index f8420c4..639f232 100644
--- a/src/cnn/cnn.c
+++ b/src/cnn/cnn.c
@@ -130,11 +130,29 @@ void write_image_in_network_32(int** image, int height, int width, float** input
     }
 }
 
-void write_256_image_in_network(unsigned char* image, int img_width, int img_depth, int input_width, float*** input) {
-    assert(img_width <= input_width);
-    assert((input_width - img_width)%2 == 0);
+void write_256_image_in_network(unsigned char* image, int img_width, int img_height, int img_depth, int input_width, float*** input) {
+    int padding = 0;
+    int decalage_x = 0; // Si l'input est plus petit que img_height, décalage de l'input par rapport à l'image selon 1e coord
+    int decalage_y = 0; // Pareil avec width et 2e coord
 
-    int padding = (input_width - img_width)/2;
+    if (img_width < input_width) { // Avec padding, l'image est carrée
+        assert(img_height == img_width);
+        assert((input_width - img_width)%2 == 0);
+
+        padding = (input_width - img_width)/2;
+    } else { // Sans padding, l'image est au minimum de la taille de l'input
+        assert(img_height >= input_width);
+
+        int decalage_possible_x = input_width - img_height;
+        if (decalage_possible_x > 0) {
+            decalage_x = rand() %decalage_possible_x;
+        }
+
+        int decalage_possible_y = input_width - img_width;
+        if (decalage_possible_y > 0) {
+            decalage_y = rand() %decalage_possible_y;
+        }
+    }
 
     for (int i=0; i < padding; i++) {
         for (int j=0; j < input_width; j++) {
@@ -147,10 +165,14 @@ void write_256_image_in_network(unsigned char* image, int img_width, int img_dep
         }
     }
 
-    for (int i=0; i < img_width; i++) {
-        for (int j=0; j < img_width; j++) {
+    int min_width = min(img_width, input_width);
+    int min_height = min(img_height, input_width);
+    for (int i=0; i < min_height; i++) {
+        for (int j=0; j < min_width; j++) {
             for (int composante=0; composante < img_depth; composante++) {
-                input[composante][i+padding][j+padding] = (float)image[(i*img_width+j)*img_depth + composante] / 255.0f;
+                int x = i + decalage_x;
+                int y = j + decalage_y;
+                input[composante][i+padding][j+padding] = (float)image[(x*img_width+y)*img_depth + composante] / 255.0f;
             }
         }
     }
diff --git a/src/cnn/export.c b/src/cnn/export.c
index 4bfa151..d304ab1 100644
--- a/src/cnn/export.c
+++ b/src/cnn/export.c
@@ -152,7 +152,7 @@ void visual_propagation(char* modele_file, char* mnist_images_file, char* out_ba
     } else {
         imgRawImage* image = loadJpegImageFile(jpeg_file);
 
-        write_256_image_in_network(image->lpData, image->width, image->numComponents, network->width[0], network->input[0]);
+        write_256_image_in_network(image->lpData, image->width, image->height, image->numComponents, network->width[0], network->input[0]);
 
         // Free allocated memory from image reading
         free(image->lpData);
diff --git a/src/cnn/include/cnn.h b/src/cnn/include/cnn.h
index 5ed7e7c..f50d226 100644
--- a/src/cnn/include/cnn.h
+++ b/src/cnn/include/cnn.h
@@ -6,8 +6,8 @@
 #define DEF_MAIN_H
 
 #define EVERYTHING 0
-#define NN_ONLY 1
-#define NN_AND_LINEARISATION 2
+#define NN_AND_LINEARISATION 1
+#define NN_ONLY 2
 
 /*
 * Renvoie l'indice de l'élément de valeur maximale dans un tableau de flottants
@@ -26,12 +26,15 @@ int will_be_drop(int dropout_prob);
 void write_image_in_network_32(int** image, int height, int width, float** input, bool random_offset);
 
 /*
-* Écrit une image linéarisée de img_width*img_width*img_depth pixels dans un tableau de taille size_input*size_input*3
+* Écrit une image linéarisée de img_width*img_height*img_depth pixels dans un tableau de taille size_input*size_input*3
 * Les conditions suivantes doivent être respectées:
-* - l'image est au plus de la même taille que input
-* - la différence de taille entre input et l'image doit être un multiple de 2 (pour centrer l'image)
+ 
+* Soit l'image est plus petite que l'input, et est carrée, alors
+* la différence de taille entre input et l'image doit être un multiple de 2 (pour centrer l'image)
+ 
+* Soit l'image est de taille au moins la taille de l'input, et elle sera décalée de manière aléatoire
 */
-void write_256_image_in_network(unsigned char* image, int img_width, int img_depth, int input_width, float*** input);
+void write_256_image_in_network(unsigned char* image, int img_width, int img_height, int img_depth, int input_width, float*** input);
 
 /*
 * Propage en avant le cnn. Le dropout est actif que si le réseau est en phase d'apprentissage.
diff --git a/src/cnn/test_network.c b/src/cnn/test_network.c
index 9bbd0bd..3212aeb 100644
--- a/src/cnn/test_network.c
+++ b/src/cnn/test_network.c
@@ -80,7 +80,7 @@ float* test_network_jpg(Network* network, char* data_dir, bool preview_fails, bo
             printf("Avancement: %.1f%%\r",  1000*i/(float)dataset->numImages);
             fflush(stdout);
         }
-        write_256_image_in_network(dataset->images[i], dataset->height, dataset->numComponents, network->width[0], network->input[0]);
+        write_256_image_in_network(dataset->images[i], dataset->width, dataset->height, dataset->numComponents, network->width[0], network->input[0]);
         forward_propagation(network);
         maxi = indice_max(network->input[network->size-1][0][0], 50);
 
@@ -184,11 +184,11 @@ void recognize_mnist(Network* network, char* input_file, char* out) {
 }
 
 void recognize_jpg(Network* network, char* input_file, char* out) {
-    int width; // Dimensions de l'image, qui doit être carrée
     int maxi;
 
     imgRawImage* image = loadJpegImageFile(input_file);
-    width = image->width;
+    int height = image->height;
+    int width = image->width;
 
     assert(image->width == image->height);
 
@@ -198,7 +198,7 @@ void recognize_jpg(Network* network, char* input_file, char* out) {
     }
 
     // Load image in the first layer of the Network
-    write_256_image_in_network(image->lpData, width, image->numComponents, network->width[0], network->input[0]);
+    write_256_image_in_network(image->lpData, width, height, image->numComponents, network->width[0], network->input[0]);
     forward_propagation(network);
 
 
diff --git a/src/cnn/train.c b/src/cnn/train.c
index 0238a38..5840585 100644
--- a/src/cnn/train.c
+++ b/src/cnn/train.c
@@ -128,7 +128,7 @@ void* train_thread(void* parameters) {
                 load_image_param->index = index[i+1];
                 pthread_create(&tid, NULL, load_image, (void*) load_image_param);
             }
-            write_256_image_in_network(param->dataset->images[index[i]], width, param->dataset->numComponents, network->width[0], network->input[0]);
+            write_256_image_in_network(param->dataset->images[index[i]], width, height, param->dataset->numComponents, network->width[0], network->input[0]);
 
             #ifdef DETAILED_TRAIN_TIMINGS
                 start_time = omp_get_wtime();