From 1199c33b13e9f562f362e68fbcf01c05fedb7747 Mon Sep 17 00:00:00 2001 From: jerrywgz Date: Mon, 1 Jul 2019 13:02:15 +0800 Subject: [PATCH] fix model zoo doc (#2629) * fix model zoo doc --- configs/faster_rcnn_se154_vd_1x.yml | 122 ------------------- configs/faster_rcnn_se154_vd_fpn_1x.yml | 140 ---------------------- configs/faster_rcnn_se154_vd_fpn_s1x.yml | 2 +- configs/faster_rcnn_x101_64x4d_fpn_1x.yml | 139 --------------------- configs/faster_rcnn_x101_64x4d_fpn_2x.yml | 139 --------------------- docs/MODEL_ZOO.md | 59 ++++----- 6 files changed, 31 insertions(+), 570 deletions(-) delete mode 100644 configs/faster_rcnn_se154_vd_1x.yml delete mode 100644 configs/faster_rcnn_se154_vd_fpn_1x.yml delete mode 100644 configs/faster_rcnn_x101_64x4d_fpn_1x.yml delete mode 100644 configs/faster_rcnn_x101_64x4d_fpn_2x.yml diff --git a/configs/faster_rcnn_se154_vd_1x.yml b/configs/faster_rcnn_se154_vd_1x.yml deleted file mode 100644 index 94cbec022..000000000 --- a/configs/faster_rcnn_se154_vd_1x.yml +++ /dev/null @@ -1,122 +0,0 @@ -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/SE154_vd_pretrained.tar -weights: output/faster_rcnn_se154_1x/model_final -metric: COCO - -FasterRCNN: - backbone: SENet - rpn_head: RPNHead - roi_extractor: RoIAlign - bbox_head: BBoxHead - bbox_assigner: BBoxAssigner - -SENet: - depth: 152 - feature_maps: 4 - freeze_at: 2 - group_width: 4 - groups: 64 - norm_type: affine_channel - variant: d - -SENetC5: - depth: 152 - freeze_at: 2 - group_width: 4 - groups: 64 - norm_type: affine_channel - variant: d - -RPNHead: - 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] - 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: 12000 - test_proposal: - min_size: 0.0 - nms_thresh: 0.7 - post_nms_top_n: 1000 - pre_nms_top_n: 6000 - -RoIAlign: - resolution: 7 - sampling_ratio: 0 - spatial_scale: 0.0625 - -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: SENetC5 - nms: - keep_top_k: 100 - nms_threshold: 0.5 - score_threshold: 0.05 - num_classes: 81 - -LearningRate: - base_lr: 0.01 - schedulers: - - !PiecewiseDecay - gamma: 0.1 - milestones: [120000, 160000] - - !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 - annotation: annotations/instances_val2017.json - image_dir: val2017 - num_workers: 2 - -FasterRCNNEvalFeed: - batch_size: 1 - dataset: - dataset_dir: dataset/coco - annotation: annotations/instances_val2017.json - image_dir: val2017 - num_workers: 2 - -FasterRCNNTestFeed: - batch_size: 1 - dataset: - annotation: annotations/instances_val2017.json - num_workers: 2 diff --git a/configs/faster_rcnn_se154_vd_fpn_1x.yml b/configs/faster_rcnn_se154_vd_fpn_1x.yml deleted file mode 100644 index a5f3fd306..000000000 --- a/configs/faster_rcnn_se154_vd_fpn_1x.yml +++ /dev/null @@ -1,140 +0,0 @@ -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/SE154_vd_pretrained.tar -weights: output/faster_rcnn_se154_fpn_1x/model_final -metric: COCO - -FasterRCNN: - backbone: SENet - fpn: FPN - rpn_head: FPNRPNHead - roi_extractor: FPNRoIAlign - bbox_head: BBoxHead - bbox_assigner: BBoxAssigner - -SENet: - depth: 152 - 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] - - !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 - -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 diff --git a/configs/faster_rcnn_se154_vd_fpn_s1x.yml b/configs/faster_rcnn_se154_vd_fpn_s1x.yml index 4284df8d9..26dc789bd 100644 --- a/configs/faster_rcnn_se154_vd_fpn_s1x.yml +++ b/configs/faster_rcnn_se154_vd_fpn_s1x.yml @@ -8,7 +8,7 @@ use_gpu: true log_smooth_window: 20 save_dir: output pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar -weights: output/faster_rcnn_se154_fpn_s1x/model_final +weights: output/faster_rcnn_se154_vd_fpn_s1x/model_final metric: COCO FasterRCNN: diff --git a/configs/faster_rcnn_x101_64x4d_fpn_1x.yml b/configs/faster_rcnn_x101_64x4d_fpn_1x.yml deleted file mode 100644 index 887632eda..000000000 --- a/configs/faster_rcnn_x101_64x4d_fpn_1x.yml +++ /dev/null @@ -1,139 +0,0 @@ -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_64x4d_pretrained.tar -weights: output/faster_rcnn_x101_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 - -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] - - !LinearWarmup - start_factor: 0.3333333333333333 - steps: 500 - -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 - -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 diff --git a/configs/faster_rcnn_x101_64x4d_fpn_2x.yml b/configs/faster_rcnn_x101_64x4d_fpn_2x.yml deleted file mode 100644 index 5cecc09dd..000000000 --- a/configs/faster_rcnn_x101_64x4d_fpn_2x.yml +++ /dev/null @@ -1,139 +0,0 @@ -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_64x4d_pretrained.tar -weights: output/faster_rcnn_x101_64x4d_fpn_2x/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 - -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: [240000, 320000] - - !LinearWarmup - start_factor: 0.3333333333333333 - steps: 500 - -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 - -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 diff --git a/docs/MODEL_ZOO.md b/docs/MODEL_ZOO.md index b51ee3c9d..4b0c7556c 100644 --- a/docs/MODEL_ZOO.md +++ b/docs/MODEL_ZOO.md @@ -30,57 +30,58 @@ The backbone models pretrained on ImageNet are available. All backbone models ar ### Faster & Mask R-CNN -| Backbone | Type | Img/gpu | Lr schd | Box AP | Mask AP | Download | +| Backbone | Type | Image/gpu | Lr schd | Box AP | Mask AP | Download | | :------------------- | :------------- | :-----: | :-----: | :----: | :-----: | :----------------------------------------------------------: | | 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 | | | [model]() | -| ResNet50-D | Faster | 1 | 1x | 36.4 | - | [model](ttps://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_1x.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) | | ResNet50-FPN | Mask | 2 | 1x | 37.9 | 34.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_fpn_1x.tar) | | ResNet50-FPN | Cascade Faster | 2 | 1x | 40.9 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_r50_fpn_1x.tar) | -| ResNet50-D-FPN | Faster | 2 | 2x | 38.9 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar) | -| ResNet50-D-FPN | Mask | 2 | 2x | 39.8 | 35.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_2x.tar) | +| ResNet50-vd-FPN | Faster | 2 | 2x | 38.9 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar) | +| ResNet50-vd-FPN | Mask | 2 | 2x | 39.8 | 35.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_2x.tar) | | ResNet101 | Faster | 1 | 1x | 38.3 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_1x.tar) | | 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-D-FPN | Faster | 1 | 1x | 40.0 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_1x.tar) | -| ResNet101-D-FPN | Faster | 1 | 2x | 40.6 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_2x.tar) | -| SENet154-D-FPN | Faster | 1 | 1.44x | 43.5 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_se154_fpn_s1x.tar) | -| SENet154-D-FPN | Mask | 1 | 1.44x | 44.0 | 38.7 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_se154_vd_fpn_s1x.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 | 2x | 40.6 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_fpn_2x.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) | ### Yolo v3 -| Backbone | Size | Img/gpu | Lr schd | Box AP | Download | +| Backbone | Size | Image/gpu | Lr schd | Box AP | Download | | :----------- | :--: | :-----: | :-----: | :----: | :-------: | -| DarkNet53 | 608 | 8 | 120e | 38.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | -| DarkNet53 | 416 | 8 | 120e | 37.5 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | -| DarkNet53 | 320 | 8 | 120e | 34.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | -| MobileNet-V1 | 608 | 8 | 120e | 29.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | -| MobileNet-V1 | 416 | 8 | 120e | 29.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | -| MobileNet-V1 | 320 | 8 | 120e | 27.1 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | -| ResNet34 | 608 | 8 | 120e | 36.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | -| ResNet34 | 416 | 8 | 120e | 34.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | -| ResNet34 | 320 | 8 | 120e | 31.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | - -**NOTE**: Yolo v3 trained in 8 GPU with total batch size as 64. Yolo v3 training data augmentations: mixup image, -random distort image, random crop image, random expand image, random interpolate, random flip image. +| DarkNet53 | 608 | 8 | 270e | 38.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | +| DarkNet53 | 416 | 8 | 270e | 37.5 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | +| DarkNet53 | 320 | 8 | 270e | 34.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) | +| MobileNet-V1 | 608 | 8 | 270e | 29.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | +| MobileNet-V1 | 416 | 8 | 270e | 29.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | +| MobileNet-V1 | 320 | 8 | 270e | 27.1 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar) | +| ResNet34 | 608 | 8 | 270e | 36.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | +| ResNet34 | 416 | 8 | 270e | 34.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | +| ResNet34 | 320 | 8 | 270e | 31.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r34.tar) | + +**NOTE**: Yolo v3 trained in 8 GPU with total batch size as 64 and trained 270 epoches. Yolo v3 training data augmentations: mixup, +randomly color distortion, randomly cropping, randomly expansion, randomly interpolation method, randomly flippling. ### RetinaNet -| Backbone | Size | Lr schd | Box AP | Download | -| :----------- | :--: | :-----: | :----: | :-------: | -| ResNet50-FPN | 300 | 120e | 36.0 | [model](https://paddlemodels.bj.bcebos.com/object_detection/retinanet_r50_fpn_1x.tar) | -| ResNet101-FPN | 300 | 120e | 37.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/retinanet_r101_fpn_1x.tar) | +| Backbone | Image/gpu | Lr schd | Box AP | Download | +| :----------- | :-----: | :-----: | :----: | :-------: | +| ResNet50-FPN | 2 | 1x | 36.0 | [model](https://paddlemodels.bj.bcebos.com/object_detection/retinanet_r50_fpn_1x.tar) | +| ResNet101-FPN | 2 | 1x | 37.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/retinanet_r101_fpn_1x.tar) | + +**Notes:** In RetinaNet, the base LR is changed to 0.01 for minibatch size 16. ### SSD on PascalVOC -| Backbone | Size | Img/gpu | Lr schd | Box AP | Download | +| Backbone | Size | Image/gpu | Lr schd | Box AP | Download | | :----------- | :--: | :-----: | :-----: | :----: | :-------: | | MobileNet v1 | 300 | 32 | 120e | 73.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssd_mobilenet_v1_voc.tar) | -**NOTE**: SSD trained in 2 GPU with totoal batch size as 64. SSD training data augmentations: random distort image, -random crop image, random expand image, random flip image. +**NOTE**: SSD trained in 2 GPU with totoal batch size as 64 and trained 120 epoches. SSD training data augmentations: randomly color distortion, +randomly cropping, randomly expansion, randomly flipping. -- GitLab