upload weight and config file

yolox_tiny_8x8_300e_coco.py

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

yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b40479060a02f93ea12f872ec984f583f1c2a5e899f3e5c876b78b78d0391a0a
+size 20404656