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mask_rcnn_flash_intern_image_t_fpn_1x_coco.py
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mask_rcnn_flash_intern_image_t_fpn_1x_coco.py
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# --------------------------------------------------------
# DCNv4
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
pretrained = 'https://huggingface.co/OpenGVLab/DCNv4/resolve/main/flash_intern_image_t_1k_224.pth'
model = dict(
backbone=dict(
_delete_=True,
type='FlashInternImage',
core_op='DCNv4',
channels=64,
depths=[4, 4, 18, 4],
groups=[4, 8, 16, 32],
mlp_ratio=4.,
drop_path_rate=0.2,
norm_layer='LN',
layer_scale=1.0,
offset_scale=1.0,
post_norm=False,
with_cp=True,
out_indices=(0, 1, 2, 3),
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
# We leverage the FPN implemented in ViTDet for stable training,
# and we don't benefit from this FPN in terms of performance.
neck=dict(
type='FPN_vitdet',
in_channels=[64, 128, 256, 512],
out_channels=256,
norm_cfg=dict(type='LN', requires_grad=True),
use_residual=True,
num_outs=5)
)
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
optimizer = dict(
_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.05,
constructor='CustomLayerDecayOptimizerConstructor',
paramwise_cfg=dict(num_layers=30, layer_decay_rate=1.0,
depths=[4, 4, 18, 4]))
optimizer_config = dict(grad_clip=None)
# fp16 = dict(loss_scale=dict(init_scale=512))
evaluation = dict(save_best='auto')
checkpoint_config = dict(
interval=1,
max_keep_ckpts=1,
save_last=True,
)
# BBox
# Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.480
# Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.695
# Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.528
# Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.303
# Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.515
# Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.629
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.599
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.599
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.599
# Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.408
# Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.637
# Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.750
# Segm
# Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.431
# Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.667 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.463 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.225
# Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.461
# Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.622
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.543
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.543
# Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.543
# Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.352
# Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.581
# Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.705