天池熊猫数据集大场景目标检测大赛.zip

## Prepare the PANDA-IMAGE dataset First step is reorganize the dataset into COCO format. Please refer to `Task1_utils.py`. ## Prepare config files of MMDetection The second step is to prepare config files thus the dataset could be successfully loaded. Assume that we want to use Faster R-CNN, the config to train the detector on PANDA IMAGE dataset is as below. The **first config** need to be overwritten is the file `configs/_base_/datasets/coco_detection.py`, the config is as below. We train only one category of box during one training process. - Change the **CATE_ID** in the above config to select target category for training - Change the **data_root** and the **anno_root** to your training data path ```python CATE_ID = '1' classes_dict = {'1': 'visible body', '2': 'full body', '3': 'head', '4': 'vehicle'} json_pre_dict = {'1': 'person_visible', '2': 'person_full', '3': 'person_head', '4':'vehicle'} data_root = 'YOUR/PATH/split_' + json_pre_dict[CATE_ID].split('_')[0] +'_train/' anno_root = 'YOUR/PATH/coco_format_json/' classes = (classes_dict[CATE_ID],) json_pre = json_pre_dict[CATE_ID] dataset_type = 'CocoDataset' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', with_bbox=True), dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), dict(type='RandomFlip', flip_ratio=0.5), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(1333, 800), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( samples_per_gpu=2, workers_per_gpu=16, train=dict( type=dataset_type, classes=classes, ann_file=anno_root + json_pre + '_train.json', img_prefix=data_root + 'image_train', pipeline=train_pipeline), val=dict( type=dataset_type, classes=classes, ann_file=anno_root + json_pre + '_val.json', img_prefix=data_root + 'image_train', pipeline=test_pipeline), test=dict( type=dataset_type, classes=classes, ann_file=anno_root + json_pre + '_val.json', img_prefix=data_root + 'image_train', pipeline=test_pipeline)) evaluation = dict(interval=1, metric='bbox') ``` The **second config** need to be overwritten is the file `configs/_base_/models/faster_rcnn_r50_fpn.py`, the config is as below. ```python model = dict( type='FasterRCNN', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, style='pytorch'), neck=dict( type='FPN', in_channels=[256, 512, 1024, 2048], out_channels=256, num_outs=5), rpn_head=dict( type='RPNHead', in_channels=256, feat_channels=256, anchor_generator=dict( type='AnchorGenerator', scales=[8], ratios=[0.5, 1.0, 2.0], strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0]), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0)), roi_head=dict( type='StandardRoIHead', bbox_roi_extractor=dict( type='SingleRoIExtractor', roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), out_channels=256, featmap_strides=[4, 8, 16, 32]), bbox_head=dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=7, num_classes=1, bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[0., 0., 0., 0.], target_stds=[0.1, 0.1, 0.2, 0.2]), reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='L1Loss', loss_weight=1.0))), # model training and testing settings train_cfg=dict( rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, neg_iou_thr=0.3, min_pos_iou=0.3, match_low_quality=True, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=256, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=False), allowed_border=-1, pos_weight=-1, debug=False), rpn_proposal=dict( nms_across_levels=False, nms_pre=2000, nms_post=1000, max_num=1000, nms_thr=0.7, min_bbox_size=0), rcnn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0.5, match_low_quality=False, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', num=512, pos_fraction=0.25, neg_pos_ub=-1, add_gt_as_proposals=True), pos_weight=-1, debug=False)), test_cfg=dict( rpn=dict( nms_across_levels=False, nms_pre=1000, nms_post=1000, max_num=1000, nms_thr=0.7, min_bbox_size=0), rcnn=dict( score_thr=0.05, nms=dict(type='nms', iou_threshold=0.5), max_per_img=100) # soft-nms is also supported for rcnn testing # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) )) ```