Commit 2efc98f7 authored by Yuxin Wu's avatar Yuxin Wu

[MaskRCNN] score was improved by 8f4a27f0. rerun evaluation.

parent 99ba595d
...@@ -79,14 +79,14 @@ MaskRCNN results contain both box and mask mAP. ...@@ -79,14 +79,14 @@ MaskRCNN results contain both box and mask mAP.
| R50-C4 | 33.1 | | 18h | <details><summary>super quick</summary>`MODE_MASK=False FRCNN.BATCH_PER_IM=64`<br/>`PREPROC.SHORT_EDGE_SIZE=600 PREPROC.MAX_SIZE=1024`<br/>`TRAIN.LR_SCHEDULE=[150000,230000,280000]` </details> | | R50-C4 | 33.1 | | 18h | <details><summary>super quick</summary>`MODE_MASK=False FRCNN.BATCH_PER_IM=64`<br/>`PREPROC.SHORT_EDGE_SIZE=600 PREPROC.MAX_SIZE=1024`<br/>`TRAIN.LR_SCHEDULE=[150000,230000,280000]` </details> |
| R50-C4 | 36.6 | 36.5 | 44h | <details><summary>standard</summary>`MODE_MASK=False` </details> | | R50-C4 | 36.6 | 36.5 | 44h | <details><summary>standard</summary>`MODE_MASK=False` </details> |
| R50-FPN | 37.4 | 37.9 | 30h | <details><summary>standard</summary>`MODE_MASK=False MODE_FPN=True` </details> | | R50-FPN | 37.4 | 37.9 | 30h | <details><summary>standard</summary>`MODE_MASK=False MODE_FPN=True` </details> |
| R50-C4 | 37.8;33.1 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R50C4-MaskRCNN-Standard.npz) | 37.8;32.8 | 49h | <details><summary>standard</summary>`MODE_MASK=True` </details> | | R50-C4 | 38.2;33.3 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R50C4-MaskRCNN-Standard.npz) | 37.8;32.8 | 49h | <details><summary>standard</summary>`MODE_MASK=True` </details> |
| R50-FPN | 38.2;34.9 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R50FPN-MaskRCNN-Standard.npz) | 38.6;34.5 | 32h | <details><summary>standard</summary>`MODE_MASK=True MODE_FPN=True` </details> | | R50-FPN | 38.5;35.2 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R50FPN-MaskRCNN-Standard.npz) | 38.6;34.5 | 32h | <details><summary>standard</summary>`MODE_MASK=True MODE_FPN=True` </details> |
| R50-FPN | 39.1;35.2 | 38.6;34.5 | 32h | <details><summary>better params</summary>`MODE_MASK=True MODE_FPN=True`<br/>`TEST.RESULT_SCORE_THRESH=1e-4`<br/>`FRCNN.BBOX_REG_WEIGHTS=[20,20,10,10]` </details> | | R50-FPN | 39.1;35.2 | 38.6;34.5 | 32h | <details><summary>better params</summary>`MODE_MASK=True MODE_FPN=True`<br/>`TEST.RESULT_SCORE_THRESH=1e-4`<br/>`FRCNN.BBOX_REG_WEIGHTS=[20,20,10,10]` </details> |
| R50-FPN | 39.5;35.2 | 39.5;34.4<sup>[2](#ft2)</sup> | 34h | <details><summary>standard+ConvGNHead</summary>`MODE_MASK=True MODE_FPN=True`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head` </details> | | R50-FPN | 39.5;35.2 | 39.5;34.4<sup>[2](#ft2)</sup> | 34h | <details><summary>standard+ConvGNHead</summary>`MODE_MASK=True MODE_FPN=True`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head` </details> |
| R50-FPN | 40.0;36.1 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R50FPN-MaskRCNN-StandardGN.npz) | 40.3;35.7 | 44h | <details><summary>standard+GN</summary>`MODE_MASK=True MODE_FPN=True`<br/>`FPN.NORM=GN BACKBONE.NORM=GN`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`<br/>`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head` | | R50-FPN | 40.0;36.2 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R50FPN-MaskRCNN-StandardGN.npz) | 40.3;35.7 | 44h | <details><summary>standard+GN</summary>`MODE_MASK=True MODE_FPN=True`<br/>`FPN.NORM=GN BACKBONE.NORM=GN`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`<br/>`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head` |
| R101-C4 | 40.8;35.1 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R101C4-MaskRCNN-Standard.npz) | | 63h | <details><summary>standard</summary>`MODE_MASK=True `<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]` </details> | | R101-C4 | 41.4;35.2 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R101C4-MaskRCNN-Standard.npz) | | 63h | <details><summary>standard</summary>`MODE_MASK=True `<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]` </details> |
| R101-FPN | 40.4;36.6 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R101FPN-MaskRCNN-Standard.npz) | 40.9;36.4 | 40h | <details><summary>standard</summary>`MODE_MASK=True MODE_FPN=True`<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]` </details> | | R101-FPN | 40.4;36.6 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R101FPN-MaskRCNN-Standard.npz) | 40.9;36.4 | 40h | <details><summary>standard</summary>`MODE_MASK=True MODE_FPN=True`<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]` </details> |
| R101-FPN | 41.0;36.6 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R101FPN-MaskRCNN-BetterParams.npz) | 40.9;36.4 | 40h | <details><summary>better params</summary>`MODE_MASK=True MODE_FPN=True`<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]`<br/>`TEST.RESULT_SCORE_THRESH=1e-4`<br/>`FRCNN.BBOX_REG_WEIGHTS=[20,20,10,10]` </details> | | R101-FPN | 41.1;36.6 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R101FPN-MaskRCNN-BetterParams.npz) | 40.9;36.4 | 40h | <details><summary>better params</summary>`MODE_MASK=True MODE_FPN=True`<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]`<br/>`TEST.RESULT_SCORE_THRESH=1e-4`<br/>`FRCNN.BBOX_REG_WEIGHTS=[20,20,10,10]` </details> |
<a id="ft1">1</a>: Here we comapre models that have identical training & inference cost between the two implementation. However their numbers are different due to many small implementation details. <a id="ft1">1</a>: Here we comapre models that have identical training & inference cost between the two implementation. However their numbers are different due to many small implementation details.
......
...@@ -120,8 +120,8 @@ def eval_coco(df, detect_func, tqdm_bar=None): ...@@ -120,8 +120,8 @@ def eval_coco(df, detect_func, tqdm_bar=None):
res = { res = {
'image_id': img_id, 'image_id': img_id,
'category_id': cat_id, 'category_id': cat_id,
'bbox': list(map(lambda x: round(float(x), 2), box)), 'bbox': list(map(lambda x: round(float(x), 3), box)),
'score': round(float(r.score), 3), 'score': round(float(r.score), 4),
} }
# also append segmentation to results # also append segmentation to results
......
...@@ -34,9 +34,9 @@ It has: ...@@ -34,9 +34,9 @@ It has:
### Note: ### Note:
Keras support is __not official__. Keras does not respect variable scopes or variable Keras does not respect variable scopes or variable
collections, which contradicts with TensorFlow conventions and tensorpack trainers. collections, which contradicts with tensorpack trainers.
Therefore, the support in tensorpack is experimental. Therefore Keras support is __experimental__.
These simple examples can run within tensorpack smoothly, but note that a future version These simple examples can run within tensorpack smoothly, but note that a future version
of Keras may break them (unlikely, though). of Keras may break them (unlikely, though).
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