提交 2ebb6e42 编写于 作者: G Guanghua Yu 提交者: qingqing01

dudate config and modelzoo (#2723)

* Add faster_rcnn_x101_vd_64x4d_fpn_1x.yml
* Release faster_rcnn_r101_vd_fpn_1x.tar, faster_rcnn_x101_vd_64x4d_fpn_1x.tar, mask_rcnn_r50_2x.tar
上级 3d103821
......@@ -36,16 +36,16 @@ multi-GPU training.
Supported Architectures:
| | ResNet | ResNet-vd <sup>[1](#vd)</sup> | SENet | MobileNet | DarkNet |
|--------------------|:------:|------------------------------:|:-----:|:---------:|:-------:|
| Faster R-CNN | ✓ | ✓ | ✓ | ✗ | ✗ |
| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✗ | ✗ |
| Mask R-CNN | ✓ | ✓ | ✓ | ✗ | ✗ |
| Mask R-CNN + FPN | ✓ | ✓ | ✓ | ✗ | ✗ |
| Cascade R-CNN | ✓ | ✗ | ✗ | ✗ | ✗ |
| RetinaNet | ✓ | ✗ | ✗ | ✗ | ✗ |
| Yolov3 | ✓ | ✗ | ✗ | ✓ | ✓ |
| SSD | ✗ | ✗ | ✗ | ✓ | ✗ |
| | ResNet | ResNet-vd <sup>[1](#vd)</sup> | ResNeXt-vd | SENet | MobileNet | DarkNet |
|--------------------|:------:|------------------------------:|:----------:|:-----:|:---------:|:-------:|
| Faster R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ |
| Faster R-CNN + FPN | ✓ | ✓ | ✓ | ✓ | ✗ | ✗ |
| Mask R-CNN | ✓ | ✓ | x | ✓ | ✗ | ✗ |
| Mask R-CNN + FPN | ✓ | ✓ | x | ✓ | ✗ | ✗ |
| Cascade R-CNN | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| RetinaNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
| Yolov3 | ✓ | ✗ | ✗ | ✗ | ✓ | ✓ |
| SSD | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
<a name="vd">[1]</a> [ResNet-vd](https://arxiv.org/pdf/1812.01187) models offer much improved accuracy with negligible performance cost.
......
architecture: FasterRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 180000
snapshot_iter: 10000
use_gpu: True
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar
weights: output/faster_rcnn_x101_vd_64x4d_fpn_1x/model_final
metric: COCO
FasterRCNN:
backbone: ResNeXt
fpn: FPN
rpn_head: FPNRPNHead
roi_extractor: FPNRoIAlign
bbox_head: BBoxHead
bbox_assigner: BBoxAssigner
ResNeXt:
depth: 101
feature_maps: [2, 3, 4, 5]
freeze_at: 2
group_width: 4
groups: 64
norm_type: affine_channel
variant: d
FPN:
max_level: 6
min_level: 2
num_chan: 256
spatial_scale: [0.03125, 0.0625, 0.125, 0.25]
FPNRPNHead:
anchor_generator:
anchor_sizes: [32, 64, 128, 256, 512]
aspect_ratios: [0.5, 1.0, 2.0]
stride: [16.0, 16.0]
variance: [1.0, 1.0, 1.0, 1.0]
anchor_start_size: 32
max_level: 6
min_level: 2
num_chan: 256
rpn_target_assign:
rpn_batch_size_per_im: 256
rpn_fg_fraction: 0.5
rpn_negative_overlap: 0.3
rpn_positive_overlap: 0.7
rpn_straddle_thresh: 0.0
train_proposal:
min_size: 0.0
nms_thresh: 0.7
post_nms_top_n: 2000
pre_nms_top_n: 2000
test_proposal:
min_size: 0.0
nms_thresh: 0.7
post_nms_top_n: 1000
pre_nms_top_n: 1000
FPNRoIAlign:
canconical_level: 4
canonical_size: 224
max_level: 5
min_level: 2
box_resolution: 7
sampling_ratio: 2
BBoxAssigner:
batch_size_per_im: 512
bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
bg_thresh_hi: 0.5
bg_thresh_lo: 0.0
fg_fraction: 0.25
fg_thresh: 0.5
num_classes: 81
BBoxHead:
head: TwoFCHead
nms:
keep_top_k: 100
nms_threshold: 0.5
score_threshold: 0.05
num_classes: 81
TwoFCHead:
num_chan: 1024
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [120000, 160000]
values: null
- !LinearWarmup
start_factor: 0.1
steps: 1000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0001
type: L2
FasterRCNNTrainFeed:
# batch size per device
batch_size: 1
dataset:
dataset_dir: dataset/coco
image_dir: train2017
annotation: annotations/instances_train2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
shuffle: True
FasterRCNNEvalFeed:
batch_size: 1
dataset:
dataset_dir: dataset/coco
annotation: annotations/instances_val2017.json
image_dir: val2017
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
FasterRCNNTestFeed:
batch_size: 1
dataset:
annotation: annotations/instances_val2017.json
batch_transforms:
- !PadBatch
pad_to_stride: 32
num_workers: 2
shuffle: False
......@@ -35,6 +35,7 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
| ResNet50 | Faster | 1 | 1x | 35.2 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_1x.tar) |
| ResNet50 | Faster | 1 | 2x | 37.1 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_2x.tar) |
| ResNet50 | Mask | 1 | 1x | 36.5 | 32.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_1x.tar) |
| ResNet50 | Mask | 1 | 2x | 38.2 | 33.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_2x.tar) |
| ResNet50-vd | Faster | 1 | 1x | 36.4 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.tar) |
| ResNet50-FPN | Faster | 2 | 1x | 37.2 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_1x.tar) |
| ResNet50-FPN | Faster | 2 | 2x | 37.7 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_fpn_2x.tar) |
......@@ -46,8 +47,9 @@ The backbone models pretrained on ImageNet are available. All backbone models ar
| ResNet101-FPN | Faster | 1 | 1x | 38.7 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar) |
| ResNet101-FPN | Faster | 1 | 2x | 39.1 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_2x.tar) |
| ResNet101-FPN | Mask | 1 | 1x | 39.5 | 35.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r101_fpn_1x.tar) |
| ResNet101-vd-FPN | Faster | 1 | 1x | 40.0 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar) |
| ResNet101-vd-FPN | Faster | 1 | 1x | 40.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_1x.tar) |
| ResNet101-vd-FPN | Faster | 1 | 2x | 40.6 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_2x.tar) |
| ResNeXt101-vd-FPN | Faster | 1 | 1x | 42.2 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_x101_vd_64x4d_fpn_1x.tar) |
| SENet154-vd-FPN | Faster | 1 | 1.44x | 42.9 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_se154_vd_fpn_s1x.tar) |
| SENet154-vd-FPN | Mask | 1 | 1.44x | 44.0 | 38.7 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_se154_vd_fpn_s1x.tar) |
......
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