use L1 loss in RetinaNet

Summary: update model zoo to be consistent with R-CNN

Reviewed By: rbgirshick

Differential Revision: D21614321

fbshipit-source-id: 4b1147be725f1f9335d51b5404d12d4d3093adb2

MODEL_ZOO.md

@@ -202,7 +202,6 @@ All models available for download through this document are licensed under the
 ./gen_html_table.py --config 'COCO-Detection/retina*50*' 'COCO-Detection/retina*101*' --name R50 R50 R101 --fields lr_sched train_speed inference_speed mem box_AP
 -->
 
-
 <table><tbody>
 <!-- START TABLE -->
 <!-- TABLE HEADER -->
@@ -218,35 +217,36 @@ All models available for download through this document are licensed under the
 <!-- ROW: retinanet_R_50_FPN_1x -->
  <tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml">R50</a></td>
 <td align="center">1x</td>
-<td align="center">0.200</td>
-<td align="center">0.055</td>
-<td align="center">3.9</td>
-<td align="center">36.5</td>
-<td align="center">137593951</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/137593951/model_final_b796dc.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/137593951/metrics.json">metrics</a></td>
+<td align="center">0.205</td>
+<td align="center">0.056</td>
+<td align="center">4.1</td>
+<td align="center">37.4</td>
+<td align="center">190397773</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/190397773/model_final_bfca0b.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_1x/190397773/metrics.json">metrics</a></td>
 </tr>
 <!-- ROW: retinanet_R_50_FPN_3x -->
  <tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml">R50</a></td>
 <td align="center">3x</td>
-<td align="center">0.201</td>
-<td align="center">0.055</td>
-<td align="center">3.9</td>
-<td align="center">37.9</td>
-<td align="center">137849486</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/137849486/model_final_4cafe0.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/137849486/metrics.json">metrics</a></td>
+<td align="center">0.205</td>
+<td align="center">0.056</td>
+<td align="center">4.1</td>
+<td align="center">38.7</td>
+<td align="center">190397829</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/190397829/model_final_5bd44e.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_50_FPN_3x/190397829/metrics.json">metrics</a></td>
 </tr>
 <!-- ROW: retinanet_R_101_FPN_3x -->
  <tr><td align="left"><a href="configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml">R101</a></td>
 <td align="center">3x</td>
-<td align="center">0.280</td>
-<td align="center">0.068</td>
-<td align="center">5.1</td>
-<td align="center">39.9</td>
-<td align="center">138363263</td>
-<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/138363263/model_final_59f53c.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/138363263/metrics.json">metrics</a></td>
+<td align="center">0.291</td>
+<td align="center">0.069</td>
+<td align="center">5.2</td>
+<td align="center">40.4</td>
+<td align="center">190397697</td>
+<td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/model_final_971ab9.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/retinanet_R_101_FPN_3x/190397697/metrics.json">metrics</a></td>
 </tr>
 </tbody></table>
 
+
 #### RPN & Fast R-CNN:
 <!--
 ./gen_html_table.py --config 'COCO-Detection/rpn*' 'COCO-Detection/fast_rcnn*' --name "RPN R50-C4" "RPN R50-FPN" "Fast R-CNN R50-FPN" --fields lr_sched train_speed inference_speed mem box_AP prop_AR

configs/Base-RetinaNet.yaml

@@ -11,6 +11,7 @@ MODEL:
   RETINANET:
     IOU_THRESHOLDS: [0.4, 0.5]
     IOU_LABELS: [0, -1, 1]
+    SMOOTH_L1_LOSS_BETA: 0.0
 DATASETS:
   TRAIN: ("coco_2017_train",)
   TEST: ("coco_2017_val",)

configs/quick_schedules/retinanet_R_50_FPN_inference_acc_test.yaml

@@ -1,7 +1,7 @@
 _BASE_: "../COCO-Detection/retinanet_R_50_FPN_3x.yaml"
 MODEL:
-  WEIGHTS: "detectron2://COCO-Detection/retinanet_R_50_FPN_3x/137849486/model_final_4cafe0.pkl"
+  WEIGHTS: "detectron2://COCO-Detection/retinanet_R_50_FPN_3x/190397829/model_final_5bd44e.pkl"
 DATASETS:
   TEST: ("coco_2017_val_100",)
 TEST:
-  EXPECTED_RESULTS: [["bbox", "AP", 44.36, 0.02]]
+  EXPECTED_RESULTS: [["bbox", "AP", 44.45, 0.02]]

detectron2/model_zoo/model_zoo.py

@@ -29,9 +29,9 @@ class _ModelZooUrls(object):
         "COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml": "137851257/model_final_f6e8b1.pkl",
         "COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml": "139173657/model_final_68b088.pkl",
         # COCO Detection with RetinaNet
-        "COCO-Detection/retinanet_R_50_FPN_1x.yaml": "137593951/model_final_b796dc.pkl",
-        "COCO-Detection/retinanet_R_50_FPN_3x.yaml": "137849486/model_final_4cafe0.pkl",
-        "COCO-Detection/retinanet_R_101_FPN_3x.yaml": "138363263/model_final_59f53c.pkl",
+        "COCO-Detection/retinanet_R_50_FPN_1x.yaml": "190397773/model_final_bfca0b.pkl",
+        "COCO-Detection/retinanet_R_50_FPN_3x.yaml": "190397829/model_final_5bd44e.pkl",
+        "COCO-Detection/retinanet_R_101_FPN_3x.yaml": "190397697/model_final_971ab9.pkl",
         # COCO Detection with RPN and Fast R-CNN
         "COCO-Detection/rpn_R_50_C4_1x.yaml": "137258005/model_final_450694.pkl",
         "COCO-Detection/rpn_R_50_FPN_1x.yaml": "137258492/model_final_02ce48.pkl",