diff --git a/configs/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py b/configs/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py index 3fca98f0..1247774e 100644 --- a/configs/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py +++ b/configs/yolov7/yolov7_l_syncbn_fast_8x16b-300e_coco.py @@ -26,7 +26,7 @@ anchors = [ [(142, 110), (192, 243), (459, 401)] # P5/32 ] # -----train val related----- -# Base learning rate for optim_wrapper. Corresponding to 8xb16=64 bs +# Base learning rate for optim_wrapper. Corresponding to 8xb16=128 bs base_lr = 0.01 max_epochs = 300 # Maximum training epochs diff --git a/configs/yolox/README.md b/configs/yolox/README.md index 78550047..11f9e307 100644 --- a/configs/yolox/README.md +++ b/configs/yolox/README.md @@ -17,12 +17,28 @@ In this report, we present some experienced improvements to YOLO series, forming YOLOX-l model structure -## Results and Models +## 🥳 🚀 Results and Models -| Backbone | size | Mem (GB) | box AP | Config | Download | -| :--------: | :--: | :------: | :----: | :------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| YOLOX-tiny | 416 | 2.8 | 32.7 | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908-0e40a6fc.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908.log.json) | -| YOLOX-s | 640 | 5.6 | 40.8 | [config](https://github.com/open-mmlab/mmyolo/tree/main/configs/yolox/yolox_s_fast_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738-d7e60cb2.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738.log.json) | +| Backbone | Size | Batch Size | AMP | RTMDet-Hyp | Mem (GB) | Box AP | Config | Download | +| :--------: | :--: | :--------: | :-: | :--------: | :------: | :---------: | :-----------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| YOLOX-tiny | 416 | 8xb8 | No | No | 2.8 | 32.7 | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908-0e40a6fc.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_8xb8-300e_coco/yolox_tiny_8xb8-300e_coco_20220919_090908.log.json) | +| YOLOX-tiny | 416 | 8xb32 | Yes | Yes | 4.9 | 34.3 (+1.6) | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637-4c338102.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637.log.json) | +| YOLOX-s | 640 | 8xb8 | Yes | No | 2.9 | 40.7 | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_s_fast_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600-2b224d8b.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600.log.json) | +| YOLOX-s | 640 | 8xb32 | Yes | Yes | 9.8 | 41.9 (+1.2) | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645-3a8dfbd7.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645.log.json) | +| YOLOX-m | 640 | 8xb8 | Yes | No | 4.9 | 46.9 | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_m_fast_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218-a71a6b25.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218.log.json) | +| YOLOX-m | 640 | 8xb32 | Yes | Yes | 17.6 | 47.5 (+0.6) | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328-e657e182.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328.log.json) | +| YOLOX-l | 640 | 8xb8 | Yes | No | 8.0 | 50.1 | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_l_fast_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast__8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715-c731eb1c.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast_8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715.log.json) | +| YOLOX-x | 640 | 8xb8 | Yes | No | 9.8 | 51.4 | [config](https://github.com/open-mmlab/mmyolo/tree/dev/configs/yolox/yolox_x_fast_8xb8-300e_coco.py) | [model](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950-1d509fab.pth) \| [log](https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950.log.json) | + +YOLOX uses a default training configuration of `8xbs8` which results in a long training time, we expect it to use `8xbs32` to speed up the training and not cause a decrease in mAP. We modified `train_batch_size_per_gpu` from 8 to 32, `batch_augments_interval` from 10 to 1 and `base_lr` from 0.01 to 0.04 under YOLOX-s default configuration based on the linear scaling rule, which resulted in mAP degradation. Finally, I found that using RTMDet's training hyperparameter can improve performance in YOLOX Tiny/S/M, which also validates the superiority of RTMDet's training hyperparameter. + +The modified training parameters are as follows： + +1. train_batch_size_per_gpu: 8 -> 32 +2. batch_augments_interval: 10 -> 1 +3. num_last_epochs: 15 -> 20 +4. optim cfg: SGD -> AdamW, base_lr 0.01 -> 0.004, weight_decay 0.0005 -> 0.05 +5. ema momentum: 0.0001 -> 0.0002 **Note**: diff --git a/configs/yolox/metafile.yml b/configs/yolox/metafile.yml index baf3a8f5..0926519e 100644 --- a/configs/yolox/metafile.yml +++ b/configs/yolox/metafile.yml @@ -36,11 +36,83 @@ Models: In Collection: YOLOX Config: configs/yolox/yolox_s_fast_8xb8-300e_coco.py Metadata: - Training Memory (GB): 5.6 + Training Memory (GB): 2.9 Epochs: 300 Results: - Task: Object Detection Dataset: COCO Metrics: - box AP: 40.8 - Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_8xb8-300e_coco/yolox_s_8xb8-300e_coco_20220917_030738-d7e60cb2.pth + box AP: 40.7 + Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb8-300e_coco/yolox_s_fast_8xb8-300e_coco_20230213_142600-2b224d8b.pth + - Name: yolox_m_fast_8xb8-300e_coco + In Collection: YOLOX + Config: configs/yolox/yolox_m_fast_8xb8-300e_coco.py + Metadata: + Training Memory (GB): 4.9 + Epochs: 300 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.9 + Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb8-300e_coco/yolox_m_fast_8xb8-300e_coco_20230213_160218-a71a6b25.pth + - Name: yolox_l_fast_8xb8-300e_coco + In Collection: YOLOX + Config: configs/yolox/yolox_l_fast_8xb8-300e_coco.py + Metadata: + Training Memory (GB): 8.0 + Epochs: 300 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.1 + Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_l_fast_8xb8-300e_coco/yolox_l_fast_8xb8-300e_coco_20230213_160715-c731eb1c.pth + - Name: yolox_x_fast_8xb8-300e_coco + In Collection: YOLOX + Config: configs/yolox/yolox_x_fast_8xb8-300e_coco.py + Metadata: + Training Memory (GB): 9.8 + Epochs: 300 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 51.4 + Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_x_fast_8xb8-300e_coco/yolox_x_fast_8xb8-300e_coco_20230215_133950-1d509fab.pth + - Name: yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco + In Collection: YOLOX + Config: configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py + Metadata: + Training Memory (GB): 4.9 + Epochs: 300 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 34.3 + Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637-4c338102.pth + - Name: yolox_s_fast_8xb32-300e-rtmdet-hyp_coco + In Collection: YOLOX + Config: configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py + Metadata: + Training Memory (GB): 9.8 + Epochs: 300 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645-3a8dfbd7.pth + - Name: yolox_m_fast_8xb32-300e-rtmdet-hyp_coco + In Collection: YOLOX + Config: configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py + Metadata: + Training Memory (GB): 17.6 + Epochs: 300 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.5 + Weights: https://download.openmmlab.com/mmyolo/v0/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco_20230210_144328-e657e182.pth diff --git a/configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py b/configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py new file mode 100644 index 00000000..4a4743c2 --- /dev/null +++ b/configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py @@ -0,0 +1,12 @@ +_base_ = './yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py' + +# ========================modified parameters====================== +deepen_factor = 0.67 +widen_factor = 0.75 + +# =======================Unmodified in most cases================== +# model settings +model = dict( + backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor), + neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor), + bbox_head=dict(head_module=dict(widen_factor=widen_factor))) diff --git a/configs/yolox/yolox_nano_fast_8xb32-300e-rtmdet-hyp_coco.py b/configs/yolox/yolox_nano_fast_8xb32-300e-rtmdet-hyp_coco.py new file mode 100644 index 00000000..851664fb --- /dev/null +++ b/configs/yolox/yolox_nano_fast_8xb32-300e-rtmdet-hyp_coco.py @@ -0,0 +1,21 @@ +_base_ = './yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py' + +# ========================modified parameters====================== +deepen_factor = 0.33 +widen_factor = 0.25 +use_depthwise = True + +# =======================Unmodified in most cases================== +# model settings +model = dict( + backbone=dict( + deepen_factor=deepen_factor, + widen_factor=widen_factor, + use_depthwise=use_depthwise), + neck=dict( + deepen_factor=deepen_factor, + widen_factor=widen_factor, + use_depthwise=use_depthwise), + bbox_head=dict( + head_module=dict( + widen_factor=widen_factor, use_depthwise=use_depthwise))) diff --git a/configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py b/configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py new file mode 100644 index 00000000..167023da --- /dev/null +++ b/configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py @@ -0,0 +1,87 @@ +_base_ = './yolox_s_fast_8xb8-300e_coco.py' + +# ========================modified parameters====================== +# Batch size of a single GPU during training +# 8 -> 32 +train_batch_size_per_gpu = 32 + +# Multi-scale training intervals +# 10 -> 1 +batch_augments_interval = 1 + +# Last epoch number to switch training pipeline +# 15 -> 20 +num_last_epochs = 20 + +# Base learning rate for optim_wrapper. Corresponding to 8xb32=256 bs +base_lr = 0.004 + +# SGD -> AdamW +optim_wrapper = dict( + _delete_=True, + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# 0.0001 -> 0.0002 +ema_momentum = 0.0002 + +# ============================== Unmodified in most cases =================== +model = dict( + data_preprocessor=dict(batch_augments=[ + dict( + type='YOLOXBatchSyncRandomResize', + random_size_range=(480, 800), + size_divisor=32, + interval=batch_augments_interval) + ])) + +param_scheduler = [ + dict( + # use quadratic formula to warm up 5 epochs + # and lr is updated by iteration + # TODO: fix default scope in get function + type='mmdet.QuadraticWarmupLR', + by_epoch=True, + begin=0, + end=5, + convert_to_iter_based=True), + dict( + # use cosine lr from 5 to 285 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=5, + T_max=_base_.max_epochs - num_last_epochs, + end=_base_.max_epochs - num_last_epochs, + by_epoch=True, + convert_to_iter_based=True), + dict( + # use fixed lr during last num_last_epochs epochs + type='ConstantLR', + by_epoch=True, + factor=1, + begin=_base_.max_epochs - num_last_epochs, + end=_base_.max_epochs, + ) +] + +custom_hooks = [ + dict( + type='YOLOXModeSwitchHook', + num_last_epochs=num_last_epochs, + new_train_pipeline=_base_.train_pipeline_stage2, + priority=48), + dict(type='mmdet.SyncNormHook', priority=48), + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=ema_momentum, + update_buffers=True, + strict_load=False, + priority=49) +] + +train_dataloader = dict(batch_size=train_batch_size_per_gpu) +train_cfg = dict(dynamic_intervals=[(_base_.max_epochs - num_last_epochs, 1)]) +auto_scale_lr = dict(base_batch_size=8 * train_batch_size_per_gpu) diff --git a/configs/yolox/yolox_s_fast_8xb8-300e_coco.py b/configs/yolox/yolox_s_fast_8xb8-300e_coco.py index 46ec96dc..b51a1087 100644 --- a/configs/yolox/yolox_s_fast_8xb8-300e_coco.py +++ b/configs/yolox/yolox_s_fast_8xb8-300e_coco.py @@ -47,9 +47,16 @@ deepen_factor = 0.33 # The scaling factor that controls the width of the network structure widen_factor = 0.5 norm_cfg = dict(type='BN', momentum=0.03, eps=0.001) +# generate new random resize shape interval +batch_augments_interval = 10 # -----train val related----- weight_decay = 0.0005 +loss_cls_weight = 1.0 +loss_bbox_weight = 5.0 +loss_obj_weight = 1.0 +loss_bbox_aux_weight = 1.0 +center_radius = 2.5 # SimOTAAssigner num_last_epochs = 15 random_affine_scaling_ratio_range = (0.1, 2) mixup_ratio_range = (0.8, 1.6) @@ -58,6 +65,8 @@ save_epoch_intervals = 10 # The maximum checkpoints to keep. max_keep_ckpts = 3 +ema_momentum = 0.0001 + # ===============================Unmodified in most cases==================== # model settings model = dict( @@ -79,7 +88,7 @@ model = dict( type='YOLOXBatchSyncRandomResize', random_size_range=(480, 800), size_divisor=32, - interval=10) + interval=batch_augments_interval) ]), backbone=dict( type='YOLOXCSPDarknet', @@ -116,24 +125,26 @@ model = dict( type='mmdet.CrossEntropyLoss', use_sigmoid=True, reduction='sum', - loss_weight=1.0), + loss_weight=loss_cls_weight), loss_bbox=dict( type='mmdet.IoULoss', mode='square', eps=1e-16, reduction='sum', - loss_weight=5.0), + loss_weight=loss_bbox_weight), loss_obj=dict( type='mmdet.CrossEntropyLoss', use_sigmoid=True, reduction='sum', - loss_weight=1.0), + loss_weight=loss_obj_weight), loss_bbox_aux=dict( - type='mmdet.L1Loss', reduction='sum', loss_weight=1.0)), + type='mmdet.L1Loss', + reduction='sum', + loss_weight=loss_bbox_aux_weight)), train_cfg=dict( assigner=dict( type='mmdet.SimOTAAssigner', - center_radius=2.5, + center_radius=center_radius, iou_calculator=dict(type='mmdet.BboxOverlaps2D'))), test_cfg=model_test_cfg) @@ -303,7 +314,7 @@ custom_hooks = [ dict( type='EMAHook', ema_type='ExpMomentumEMA', - momentum=0.0001, + momentum=ema_momentum, update_buffers=True, strict_load=False, priority=49) @@ -315,6 +326,6 @@ train_cfg = dict( val_interval=save_epoch_intervals, dynamic_intervals=[(max_epochs - num_last_epochs, 1)]) -auto_scale_lr = dict(base_batch_size=64) +auto_scale_lr = dict(base_batch_size=8 * train_batch_size_per_gpu) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') diff --git a/configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py b/configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py new file mode 100644 index 00000000..d133c95f --- /dev/null +++ b/configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py @@ -0,0 +1,70 @@ +_base_ = './yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py' + +# ========================modified parameters====================== +deepen_factor = 0.33 +widen_factor = 0.375 + +# Multi-scale training intervals +# 10 -> 1 +batch_augments_interval = 1 + +scaling_ratio_range = (0.5, 1.5) + +# =======================Unmodified in most cases================== +img_scale = _base_.img_scale +pre_transform = _base_.pre_transform + +# model settings +model = dict( + data_preprocessor=dict(batch_augments=[ + dict( + type='YOLOXBatchSyncRandomResize', + random_size_range=(320, 640), + size_divisor=32, + interval=batch_augments_interval) + ]), + backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor), + neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor), + bbox_head=dict(head_module=dict(widen_factor=widen_factor))) + +train_pipeline_stage1 = [ + *pre_transform, + dict( + type='Mosaic', + img_scale=img_scale, + pad_val=114.0, + pre_transform=pre_transform), + dict( + type='mmdet.RandomAffine', + scaling_ratio_range=scaling_ratio_range, # note + # img_scale is (width, height) + border=(-img_scale[0] // 2, -img_scale[1] // 2)), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict(type='mmdet.RandomFlip', prob=0.5), + dict( + type='mmdet.FilterAnnotations', + min_gt_bbox_wh=(1, 1), + keep_empty=False), + dict( + type='mmdet.PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', + 'flip_direction')) +] + +test_pipeline = [ + dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args), + dict(type='mmdet.Resize', scale=(416, 416), keep_ratio=True), # note + dict( + type='mmdet.Pad', + pad_to_square=True, + pad_val=dict(img=(114.0, 114.0, 114.0))), + dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), + dict( + type='mmdet.PackDetInputs', + meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', + 'scale_factor')) +] + +train_dataloader = dict(dataset=dict(pipeline=train_pipeline_stage1)) +val_dataloader = dict(dataset=dict(pipeline=test_pipeline)) +test_dataloader = val_dataloader diff --git a/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py b/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py index 90e7e411..e8c822e0 100644 --- a/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py +++ b/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py @@ -3,12 +3,12 @@ _base_ = './yolox_s_fast_8xb8-300e_coco.py' # ========================modified parameters====================== deepen_factor = 0.33 widen_factor = 0.375 - -img_scale = _base_.img_scale -pre_transform = _base_.pre_transform scaling_ratio_range = (0.5, 1.5) # =======================Unmodified in most cases================== +img_scale = _base_.img_scale +pre_transform = _base_.pre_transform + # model settings model = dict( data_preprocessor=dict(batch_augments=[