Fixed ignore_thresh

src/yolo_layer.c

@@ -129,23 +129,18 @@ box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw
 }
 
 
-int get_yolo_class(float *output, int classes, int class_index, int stride, float objectness)
+int compare_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id)
 {
-    int class_id = 0;
-    float max_prob = FLT_MIN;
+    const float conf_thresh = 0.25;
 
     int j;
     for (j = 0; j < classes; ++j) {
         float prob = objectness * output[class_index + stride*j];
-        if (prob > max_prob) {
-            max_prob = prob;
-            class_id = j;
+        if (prob > conf_thresh) {
+            return 1;
         }
-        //int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j);
-        //float prob = objectness*predictions[class_index];
-        //dets[count].prob[j] = (prob > thresh) ? prob : 0;
     }
-    return class_id;
+    return 0;
 }
 
 ious delta_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta, float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss)
@@ -280,6 +275,8 @@ void forward_yolo_layer(const layer l, network_state state)
                 for (n = 0; n < l.n; ++n) {
                     int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
                     box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h);
+                    float best_match_iou = 0;
+                    int best_match_t = 0;
                     float best_iou = 0;
                     int best_t = 0;
                     for (t = 0; t < l.max_boxes; ++t) {
@@ -296,14 +293,14 @@ void forward_yolo_layer(const layer l, network_state state)
                         int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
                         int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
                         float objectness = l.output[obj_index];
-                        int pred_class_id = get_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness);
-                        int class_id_match = 0;
-                        if (class_id == pred_class_id) class_id_match = 1;
-                        else class_id_match = 0;
+                        int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id);
 
                         float iou = box_iou(pred, truth);
-                        //if (iou > best_iou) {
-                        if (iou > best_iou && class_id_match == 1) {
+                        if (iou > best_match_iou && class_id_match == 1) {
+                            best_match_iou = iou;
+                            best_match_t = t;
+                        }
+                        if (iou > best_iou) {
                             best_iou = iou;
                             best_t = t;
                         }
@@ -311,7 +308,7 @@ void forward_yolo_layer(const layer l, network_state state)
                     int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
                     avg_anyobj += l.output[obj_index];
                     l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]);
-                    if (best_iou > l.ignore_thresh) {
+                    if (best_match_iou > l.ignore_thresh) {
                         l.delta[obj_index] = 0;
                     }
                     if (best_iou > l.truth_thresh) {
@@ -376,9 +373,6 @@ void forward_yolo_layer(const layer l, network_state state)
 
                 ++count;
                 ++class_count;
-                //if(iou > .5) recall += 1;
-                //if(iou > .75) recall75 += 1;
-                //avg_iou += iou;
                 if (all_ious.iou > .5) recall += 1;
                 if (all_ious.iou > .75) recall75 += 1;
             }