提交 55416643 编写于 作者: A A. Unique TensorFlower

Internal change

PiperOrigin-RevId: 336942308
上级 a708019f
......@@ -49,12 +49,15 @@ TF Vision model garden provides a large collection of baselines and checkpoints
| R50-FPN | 640x640 | 12 | 97.0 | 34.0 | 34.3 | config|
| R50-FPN | 640x640 | 36 | 97.0 | 34.0 | 37.3 | config|
#### RetinaNet (Trained from scratch)
#### RetinaNet (Trained from scratch) with training features including:
* Stochastic depth with drop rate 0.2.
* Swish activation.
| backbone | resolution | epochs | FLOPs (B) | params (M) | box AP | download |
| ------------ |:-------------:| ---------:|-----------:|--------:|---------:|-----------:|
| SpineNet-49 | 640x640 | 350 | 85.4| 28.5 | 42.4| config|
| SpineNet-96 | 1024x1024 | 350 | 265.4 | 43.0 | 46.0 | config |
| SpineNet-143 | 1280x1280 | 350 | 524.0 | 67.0 | 46.8 |config|
| SpineNet-49 | 640x640 | 500 | 85.4| 28.5 | 44.2 | [config](https://github.com/tensorflow/models/blob/master/official/vision/beta/configs/experiments/retinanet/coco_spinenet49_tpu.yaml)|
| SpineNet-96 | 1024x1024 | 500 | 265.4 | 43.0 | 48.5 | [config](https://github.com/tensorflow/models/blob/master/official/vision/beta/configs/experiments/retinanet/coco_spinenet96_tpu.yaml) |
| SpineNet-143 | 1280x1280 | 500 | 524.0 | 67.0 | 50.0 | [config](https://github.com/tensorflow/models/blob/master/official/vision/beta/configs/experiments/retinanet/coco_spinenet143_tpu.yaml)|
### Instance Segmentation Baselines
......
# SpineNet-143 COCO detection with protocal C config. Expecting 50.0% AP.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
task:
annotation_file: '/readahead/200M/placer/prod/home/snaggletooth/test/data/coco/instances_val2017.json'
gradient_clip_norm: 0.0
gradient_clip_norm: 10.0
losses:
l2_weight_decay: 4.0e-05
model:
......
# SpineNet-49 COCO detection with protocal C config. Expecting 44.2% AP.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
......
# SpineNet-96 COCO detection with protocol C config. Expecting 48.5% AP.
runtime:
distribution_strategy: 'tpu'
mixed_precision_dtype: 'bfloat16'
......
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