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"""Sparse RCNN with ResNet50-FPN, 100 proposals, 3x schedule, MS training.""" |
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_base_ = "./sparse_rcnn_r50_fpn_1x_det_bdd100k.py" |
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data_root = "../data/bdd100k/" |
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img_norm_cfg = dict( |
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True |
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) |
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min_values = (600, 624, 648, 672, 696, 720) |
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train_pipeline = [ |
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dict(type="LoadImageFromFile", to_float32=True), |
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dict(type="LoadAnnotations", with_bbox=True), |
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dict( |
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type="Resize", |
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img_scale=[(1280, value) for value in min_values], |
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multiscale_mode="value", |
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keep_ratio=True, |
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), |
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dict(type="RandomFlip", flip_ratio=0.5), |
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dict(type="Normalize", **img_norm_cfg), |
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dict(type="Pad", size_divisor=32), |
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dict(type="DefaultFormatBundle"), |
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dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels"]), |
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] |
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data = dict(train=dict(pipeline=train_pipeline)) |
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lr_config = dict(policy="step", step=[27, 33]) |
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runner = dict(type="EpochBasedRunner", max_epochs=36) |
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load_from = "https://dl.cv.ethz.ch/bdd100k/det/models/sparse_rcnn_r50_fpn_3x_det_bdd100k.pth" |
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