Commit 1f07de76 authored by Yuxin Wu's avatar Yuxin Wu

fasterrcnn notes

parent d96f2675
# Faster-RCNN / Mask-RCNN on COCO
This example aims to provide a minimal (1.3k lines) multi-GPU implementation of
Faster-RCNN / Mask-RCNN (without FPN) on COCO.
Faster-RCNN & Mask-RCNN (with ResNet backbones) on COCO.
## Dependencies
+ Python 3; TensorFlow >= 1.4.0
......@@ -53,14 +53,14 @@ MaskRCNN results contain both bbox and segm mAP.
|Backbone | `FASTRCNN_BATCH` | resolution | mAP (bbox/segm) | Time |
| - | - | - | - | - |
| Res50 | 64 | (600, 1024) | 33.0 | 22h on 8 P100 |
| Res50 | 256 | (600, 1024) | 34.4 | 49h on 8 M40 |
| Res50 | 512 | (800, 1333) | 35.6 | 55h on 8 P100|
| Res50 | 256 | (800, 1333) | 36.9/32.3 | 39h on 8 P100|
| Res101 | 512 | (800, 1333) | 40.1/34.4 | 70h on 8 P100|
| R50 | 64 | (600, 1024) | 33.0 | 22h on 8 P100 |
| R50 | 256 | (600, 1024) | 34.4 | 49h on 8 M40 |
| R50 | 512 | (800, 1333) | 35.6 | 55h on 8 P100|
| R50 | 256 | (800, 1333) | 36.9/32.3 | 39h on 8 P100|
| R101 | 512 | (800, 1333) | 40.1/34.4 | 70h on 8 P100|
Note that these models are trained with a larger ROI batch size than the paper,
and get about 1mAP better performance.
Note that these models are trained with different ROI batch size and LR schedule.
The performance is slightly better than the paper.
## Notes
......
......@@ -160,7 +160,6 @@ class MultiProcessPrefetchData(ProxyDataFlow):
self._size = -1
self.nr_proc = nr_proc
self.nr_prefetch = nr_prefetch
self._guard = DataFlowReentrantGuard()
if nr_proc > 1:
logger.info("[MultiProcessPrefetchData] Will fork a dataflow more than one times. "
......@@ -173,7 +172,6 @@ class MultiProcessPrefetchData(ProxyDataFlow):
start_proc_mask_signal(self.procs)
def get_data(self):
with self._guard:
for k in itertools.count():
if self._size > 0 and k >= self._size:
break
......
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