diff --git a/src/cnn/train.c b/src/cnn/train.c
index a5bcf02..e161e2f 100644
--- a/src/cnn/train.c
+++ b/src/cnn/train.c
@@ -91,7 +91,9 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
     float accuracy;
     float current_accuracy;
 
-    int nb_images_total;
+    int nb_images_total; // Images au total
+    int nb_images_total_remaining; // Images restantes dans un batch
+    int batches_epoques; // Batches par époque
 
     int*** images;
     unsigned int* labels;
@@ -180,9 +182,17 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
         // du multi-threading car chaque copie du réseau initiale sera légèrement différente
         // et donnera donc des résultats différents sur les mêmes images.
         accuracy = 0.;
-        for (int j=0; j < nb_images_total / BATCHES; j++) {
+        batches_epoques = nb_images_total / BATCHES;
+        nb_images_total_remaining = nb_images_total;
+        for (int j=0; j < batches_epoques; j++) {
             #ifdef USE_MULTITHREADING
-            nb_remaining_images = BATCHES;
+            if (j == batches_epoques-1) {
+                nb_remaining_images = nb_images_total_remaining;
+                nb_images_total_remaining = 0;
+            } else {
+                nb_images_total_remaining -= BATCHES;
+                nb_remaining_images = BATCHES;
+            }
 
             for (int k=0; k < nb_threads; k++) {
                 if (k == nb_threads-1) {
@@ -191,8 +201,9 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
                 } else {
                     nb_remaining_images -= BATCHES / nb_threads;
                 }
+                train_parameters[k]->start = BATCHES*j + (BATCHES/nb_threads)*k;
                 train_parameters[k]->network = copy_network(network);
-                train_parameters[k]->start = BATCHES*j + (nb_images_total/BATCHES)*k;
+
                 pthread_create( &tid[k], NULL, train_thread, (void*) train_parameters[k]);
             }
             for (int k=0; k < nb_threads; k++) {
@@ -206,6 +217,7 @@ void train(int dataset_type, char* images_file, char* labels_file, char* data_di
             }
             current_accuracy = accuracy * nb_images_total/((j+1)*BATCHES);
             printf("\rThreads [%d]\tÉpoque [%d/%d]\tImage [%d/%d]\tAccuracy: "YELLOW"%0.1f%%"RESET" ", nb_threads, i, epochs, BATCHES*(j+1), nb_images_total, current_accuracy*100);
+            fflush(stdout);
             #else
             train_params->start = j*BATCHES;