fcakyon commited on
Commit
3db7d9e
1 Parent(s): 1db0da4

upload weight and config file

Browse files
yolox_tiny_8x8_300e_coco.py ADDED
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+ optimizer = dict(
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+ type="SGD",
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+ lr=0.01,
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+ momentum=0.9,
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+ weight_decay=0.0005,
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+ nesterov=True,
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+ paramwise_cfg=dict(norm_decay_mult=0.0, bias_decay_mult=0.0),
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+ )
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+ optimizer_config = dict(grad_clip=None)
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+ lr_config = dict(
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+ policy="YOLOX",
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+ warmup="exp",
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+ by_epoch=False,
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+ warmup_by_epoch=True,
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+ warmup_ratio=1,
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+ warmup_iters=5,
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+ num_last_epochs=15,
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+ min_lr_ratio=0.05,
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+ )
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+ runner = dict(type="EpochBasedRunner", max_epochs=300)
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+ checkpoint_config = dict(interval=10)
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+ log_config = dict(interval=50, hooks=[dict(type="TextLoggerHook")])
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+ custom_hooks = [
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+ dict(type="YOLOXModeSwitchHook", num_last_epochs=15, priority=48),
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+ dict(type="SyncNormHook", num_last_epochs=15, interval=10, priority=48),
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+ dict(type="ExpMomentumEMAHook", resume_from=None, momentum=0.0001, priority=49),
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+ ]
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+ dist_params = dict(backend="nccl")
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+ log_level = "INFO"
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+ load_from = None
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+ resume_from = None
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+ workflow = [("train", 1)]
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+ img_scale = (640, 640)
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+ model = dict(
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+ type="YOLOX",
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+ input_size=(640, 640),
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+ random_size_range=(10, 20),
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+ random_size_interval=10,
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+ backbone=dict(type="CSPDarknet", deepen_factor=0.33, widen_factor=0.375),
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+ neck=dict(type="YOLOXPAFPN", in_channels=[96, 192, 384], out_channels=96, num_csp_blocks=1),
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+ bbox_head=dict(type="YOLOXHead", num_classes=80, in_channels=96, feat_channels=96),
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+ train_cfg=dict(assigner=dict(type="SimOTAAssigner", center_radius=2.5)),
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+ test_cfg=dict(score_thr=0.01, nms=dict(type="nms", iou_threshold=0.65)),
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+ )
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+ data_root = "data/coco/"
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+ dataset_type = "CocoDataset"
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+ train_pipeline = [
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+ dict(type="Mosaic", img_scale=(640, 640), pad_val=114.0),
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+ dict(type="RandomAffine", scaling_ratio_range=(0.5, 1.5), border=(-320, -320)),
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+ dict(type="YOLOXHSVRandomAug"),
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+ dict(type="RandomFlip", flip_ratio=0.5),
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+ dict(type="Resize", img_scale=(640, 640), keep_ratio=True),
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+ dict(type="Pad", pad_to_square=True, pad_val=dict(img=(114.0, 114.0, 114.0))),
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+ dict(type="FilterAnnotations", min_gt_bbox_wh=(1, 1), keep_empty=False),
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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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+ train_dataset = dict(
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+ type="MultiImageMixDataset",
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+ dataset=dict(
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+ type="CocoDataset",
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+ ann_file="data/coco/annotations/instances_train2017.json",
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+ img_prefix="data/coco/train2017/",
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+ pipeline=[dict(type="LoadImageFromFile"), dict(type="LoadAnnotations", with_bbox=True)],
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+ filter_empty_gt=False,
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+ ),
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+ pipeline=[
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+ dict(type="Mosaic", img_scale=(640, 640), pad_val=114.0),
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+ dict(type="RandomAffine", scaling_ratio_range=(0.5, 1.5), border=(-320, -320)),
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+ dict(type="YOLOXHSVRandomAug"),
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+ dict(type="RandomFlip", flip_ratio=0.5),
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+ dict(type="Resize", img_scale=(640, 640), keep_ratio=True),
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+ dict(type="Pad", pad_to_square=True, pad_val=dict(img=(114.0, 114.0, 114.0))),
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+ dict(type="FilterAnnotations", min_gt_bbox_wh=(1, 1), keep_empty=False),
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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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+ )
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+ test_pipeline = [
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+ dict(type="LoadImageFromFile"),
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+ dict(
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+ type="MultiScaleFlipAug",
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+ img_scale=(416, 416),
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+ flip=False,
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+ transforms=[
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+ dict(type="Resize", keep_ratio=True),
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+ dict(type="RandomFlip"),
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+ dict(type="Pad", pad_to_square=True, pad_val=dict(img=(114.0, 114.0, 114.0))),
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+ dict(type="DefaultFormatBundle"),
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+ dict(type="Collect", keys=["img"]),
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+ ],
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+ ),
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+ ]
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+ data = dict(
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+ samples_per_gpu=8,
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+ workers_per_gpu=4,
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+ persistent_workers=True,
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+ train=dict(
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+ type="MultiImageMixDataset",
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+ dataset=dict(
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+ type="CocoDataset",
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+ ann_file="data/coco/annotations/instances_train2017.json",
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+ img_prefix="data/coco/train2017/",
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+ pipeline=[dict(type="LoadImageFromFile"), dict(type="LoadAnnotations", with_bbox=True)],
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+ filter_empty_gt=False,
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+ ),
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+ pipeline=[
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+ dict(type="Mosaic", img_scale=(640, 640), pad_val=114.0),
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+ dict(type="RandomAffine", scaling_ratio_range=(0.5, 1.5), border=(-320, -320)),
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+ dict(type="YOLOXHSVRandomAug"),
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+ dict(type="RandomFlip", flip_ratio=0.5),
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+ dict(type="Resize", img_scale=(640, 640), keep_ratio=True),
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+ dict(type="Pad", pad_to_square=True, pad_val=dict(img=(114.0, 114.0, 114.0))),
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+ dict(type="FilterAnnotations", min_gt_bbox_wh=(1, 1), keep_empty=False),
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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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+ ),
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+ val=dict(
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+ type="CocoDataset",
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+ ann_file="data/coco/annotations/instances_val2017.json",
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+ img_prefix="data/coco/val2017/",
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+ pipeline=[
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+ dict(type="LoadImageFromFile"),
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+ dict(
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+ type="MultiScaleFlipAug",
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+ img_scale=(416, 416),
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+ flip=False,
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+ transforms=[
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+ dict(type="Resize", keep_ratio=True),
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+ dict(type="RandomFlip"),
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+ dict(type="Pad", pad_to_square=True, pad_val=dict(img=(114.0, 114.0, 114.0))),
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+ dict(type="DefaultFormatBundle"),
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+ dict(type="Collect", keys=["img"]),
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+ ],
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+ ),
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+ ],
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+ ),
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+ test=dict(
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+ type="CocoDataset",
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+ ann_file="data/coco/annotations/instances_val2017.json",
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+ img_prefix="data/coco/val2017/",
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+ pipeline=[
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+ dict(type="LoadImageFromFile"),
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+ dict(
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+ type="MultiScaleFlipAug",
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+ img_scale=(416, 416),
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+ flip=False,
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+ transforms=[
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+ dict(type="Resize", keep_ratio=True),
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+ dict(type="RandomFlip"),
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+ dict(type="Pad", pad_to_square=True, pad_val=dict(img=(114.0, 114.0, 114.0))),
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+ dict(type="DefaultFormatBundle"),
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+ dict(type="Collect", keys=["img"]),
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+ ],
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+ ),
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+ ],
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+ ),
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+ )
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+ max_epochs = 300
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+ num_last_epochs = 15
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+ interval = 10
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+ evaluation = dict(save_best="auto", interval=10, dynamic_intervals=[(285, 1)], metric="bbox")
yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:b40479060a02f93ea12f872ec984f583f1c2a5e899f3e5c876b78b78d0391a0a
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+ size 20404656