repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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mmyolo | mmyolo-main/tests/test_models/test_task_modules/test_assigners/test_batch_atss_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models.task_modules.assigners import BatchATSSAssigner
class TestBatchATSSAssigner(TestCase):
def test_batch_atss_assigner(self):
num_classes = 2
batch_size = 2
batch_atss_assigner = B... | 7,366 | 40.857955 | 79 | py |
mmyolo | mmyolo-main/tests/test_models/test_task_modules/test_assigners/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
| 48 | 23.5 | 47 | py |
mmyolo | mmyolo-main/tests/test_models/test_task_modules/test_assigners/test_batch_task_aligned_assigner.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models.task_modules.assigners import BatchTaskAlignedAssigner
class TestBatchTaskAlignedAssigner(TestCase):
def test_batch_task_aligned_assigner(self):
batch_size = 2
num_classes = 4
a... | 2,212 | 37.824561 | 79 | py |
mmyolo | mmyolo-main/tests/test_models/test_plugins/test_cbam.py | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmyolo.models.plugins import CBAM
from mmyolo.utils import register_all_modules
register_all_modules()
class TestCBAM(TestCase):
def test_forward(self):
tensor_shape = (2, 16, 20, 20)
images = tor... | 783 | 23.5 | 65 | py |
mmyolo | mmyolo-main/tests/test_models/test_plugins/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
| 48 | 23.5 | 47 | py |
mmyolo | mmyolo-main/tests/test_datasets/test_yolov5_voc.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmengine.dataset import ConcatDataset
from mmyolo.datasets import YOLOv5VOCDataset
from mmyolo.utils import register_all_modules
register_all_modules()
class TestYOLOv5VocDataset(unittest.TestCase):
def test_batch_shapes_cfg(self):
b... | 3,002 | 33.517241 | 75 | py |
mmyolo | mmyolo-main/tests/test_datasets/test_yolov5_coco.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmyolo.datasets import YOLOv5CocoDataset
class TestYOLOv5CocoDataset(unittest.TestCase):
def test_batch_shapes_cfg(self):
batch_shapes_cfg = dict(
type='BatchShapePolicy',
batch_size=2,
img_size=640,... | 2,429 | 32.75 | 75 | py |
mmyolo | mmyolo-main/tests/test_datasets/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
| 48 | 23.5 | 47 | py |
mmyolo | mmyolo-main/tests/test_datasets/test_utils.py | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import numpy as np
import torch
from mmdet.structures import DetDataSample
from mmdet.structures.bbox import HorizontalBoxes
from mmengine.structures import InstanceData
from mmyolo.datasets import BatchShapePolicy, yolov5_collate
def _rand_bboxes(rng,... | 4,918 | 34.388489 | 79 | py |
mmyolo | mmyolo-main/tests/test_datasets/test_transforms/test_mix_img_transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
import torch
from mmdet.structures.bbox import HorizontalBoxes
from mmdet.structures.mask import BitmapMasks, PolygonMasks
from mmyolo.datasets import YOLOv5CocoDataset
from mmyolo.datasets.transforms ... | 17,683 | 40.221445 | 79 | py |
mmyolo | mmyolo-main/tests/test_datasets/test_transforms/__init__.py | # Copyright (c) OpenMMLab. All rights reserved.
| 48 | 23.5 | 47 | py |
mmyolo | mmyolo-main/tests/test_datasets/test_transforms/test_transforms.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import mmcv
import numpy as np
import torch
from mmdet.structures.bbox import HorizontalBoxes
from mmdet.structures.mask import BitmapMasks, PolygonMasks
from mmyolo.datasets.transforms import (LetterResize, LoadAnnotati... | 21,175 | 42.12831 | 79 | py |
mmyolo | mmyolo-main/tests/test_utils/test_setup_env.py | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import sys
from unittest import TestCase
from mmengine import DefaultScope
from mmyolo.utils import register_all_modules
class TestSetupEnv(TestCase):
def test_register_all_modules(self):
from mmyolo.registry import DATASETS
# not... | 1,544 | 37.625 | 77 | py |
mmyolo | mmyolo-main/tests/test_utils/test_collect_env.py | # Copyright (c) OpenMMLab. All rights reserved.
import sys
from unittest import TestCase
import mmcv
import mmdet
import mmengine
from mmyolo.utils import collect_env
class TestCollectEnv(TestCase):
def test_collect_env(self):
env_info = collect_env()
print(env_info)
expected_keys = [
... | 956 | 27.147059 | 68 | py |
mmyolo | mmyolo-main/tests/test_downstream/test_mmrazor.py | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import pytest
from mmcls.models.backbones.base_backbone import BaseBackbone
from mmyolo.testing import get_detector_cfg
@pytest.mark.parametrize('cfg_file', [
'razor/subnets/'
'yolov5_s_spos_shufflenetv2_syncbn_8xb16-300e_coco.py', 'razor/subnets/'... | 707 | 31.181818 | 76 | py |
mmyolo | mmyolo-main/demo/large_image_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Perform MMYOLO inference on large images (as satellite imagery) as:
```shell
wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth # noqa: E501,... | 10,634 | 35.050847 | 197 | py |
mmyolo | mmyolo-main/demo/featmap_vis_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from typing import Sequence
import mmcv
from mmdet.apis import inference_detector, init_detector
from mmengine import Config, DictAction
from mmengine.registry import init_default_scope
from mmengine.utils import ProgressBar
from mmyolo.registr... | 6,508 | 31.545 | 95 | py |
mmyolo | mmyolo-main/demo/video_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Perform MMYOLO inference on a video as:
```shell
wget -P checkpoint https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth # noqa: E501, E261.
python demo/video_de... | 3,274 | 32.762887 | 197 | py |
mmyolo | mmyolo-main/demo/deploy_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
"""Deploy demo for mmdeploy.
This script help user to run mmdeploy demo after convert the
checkpoint to backends.
Usage:
python deploy_demo.py img \
config \
checkpoint \
[--deploy-cfg DEP... | 3,823 | 30.603306 | 95 | py |
mmyolo | mmyolo-main/demo/image_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
import os
from argparse import ArgumentParser
from pathlib import Path
import mmcv
from mmdet.apis import inference_detector, init_detector
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
from mmengine.utils import ProgressBar, path
... | 5,733 | 32.928994 | 78 | py |
mmyolo | mmyolo-main/demo/boxam_vis_demo.py | # Copyright (c) OpenMMLab. All rights reserved.
"""This script is in the experimental verification stage and cannot be
guaranteed to be completely correct. Currently Grad-based CAM and Grad-free CAM
are supported.
The target detection task is different from the classification task. It not
only includes the AM map of t... | 9,251 | 32.400722 | 79 | py |
mmyolo | mmyolo-main/configs/rtmdet/rtmdet_x_syncbn_fast_8xb32-300e_coco.py | _base_ = './rtmdet_l_syncbn_fast_8xb32-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 1.33
widen_factor = 1.25
# =======================Unmodified in most cases==================
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),... | 457 | 37.166667 | 74 | py |
mmyolo | mmyolo-main/configs/rtmdet/rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py | _base_ = './rtmdet_s_syncbn_fast_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
# ========================modified parameters======================
deepen_factor = 0.167
widen_factor = 0.375
img_scale = _base_.img_sc... | 1,988 | 32.711864 | 129 | py |
mmyolo | mmyolo-main/configs/rtmdet/rtmdet_tiny_fast_1xb12-40e_cat.py | _base_ = 'rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py'
data_root = './data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
num_epochs_stage2 = 5
max_epochs = 40
train_batch_size_per_gpu = 12
train_num_workers = 4
val_batch_size_per_gpu = 1
val_nu... | 2,147 | 29.253521 | 178 | py |
mmyolo | mmyolo-main/configs/rtmdet/rtmdet_m_syncbn_fast_8xb32-300e_coco.py | _base_ = './rtmdet_l_syncbn_fast_8xb32-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
# =======================Unmodified in most cases==================
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),... | 457 | 37.166667 | 74 | py |
mmyolo | mmyolo-main/configs/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco.py | _base_ = './rtmdet_l_syncbn_fast_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
# ========================modified parameters======================
deepen_factor = 0.33
widen_factor = 0.5
img_scale = _base_.img_scale
#... | 3,129 | 32.655914 | 126 | py |
mmyolo | mmyolo-main/configs/rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco.py | _base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
# ========================Frequently modified parameters======================
# -----data related-----
data_root = 'data/coco/'
# Path of train annotation file
train_ann_file = 'annotations/instances_train2017.json'
train_data_prefix = 'train2017/' ... | 9,806 | 31.154098 | 78 | py |
mmyolo | mmyolo-main/configs/rtmdet/rtmdet-ins_s_syncbn_fast_8xb32-300e_coco.py | _base_ = './rtmdet_s_syncbn_fast_8xb32-300e_coco.py'
widen_factor = 0.5
model = dict(
bbox_head=dict(
type='RTMDetInsSepBNHead',
head_module=dict(
type='RTMDetInsSepBNHeadModule',
use_sigmoid_cls=True,
widen_factor=widen_factor),
loss_mask=dict(
... | 916 | 27.65625 | 76 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_m_syncbn_fast_2xb4-36e_dota.py | _base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth' # noqa
# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
# Submiss... | 1,274 | 36.5 | 145 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_tiny_fast_1xb8-36e_dota-ms.py | _base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota-ms.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
# ========================modified parameters======================
deepen_factor = 0.167
widen_factor = 0.375
# Batch size of a si... | 1,400 | 34.923077 | 129 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_tiny_fast_1xb8-36e_dota.py | _base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
# ========================modified parameters======================
deepen_factor = 0.167
widen_factor = 0.375
# Batch size of a singl... | 1,397 | 34.846154 | 129 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_l_syncbn_fast_2xb4-36e_dota-ms.py | _base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota.py'
# ========================modified parameters======================
data_root = 'data/split_ms_dota/'
# Path of test images folder
test_data_prefix = 'test/images/'
# Submission dir for result submit
submission_dir = './work_dirs/{{fileBasenameNoExtension}}/submissi... | 1,058 | 33.16129 | 69 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_s_fast_1xb8-36e_dota.py | _base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
# ========================modified parameters======================
deepen_factor = 0.33
widen_factor = 0.5
# Batch size of a single GPU ... | 1,391 | 34.692308 | 126 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_l_syncbn_fast_coco-pretrain_2xb4-36e_dota-ms.py | _base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota-ms.py'
load_from = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco/rtmdet_l_syncbn_fast_8xb32-300e_coco_20230102_135928-ee3abdc4.pth' # noqa
# Submission dir for result submit
submission_dir = './work_dirs/{{fileBasenameNoExtensio... | 837 | 38.904762 | 172 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_s_fast_1xb8-36e_dota-ms.py | _base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota-ms.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
# ========================modified parameters======================
deepen_factor = 0.33
widen_factor = 0.5
# Batch size of a single G... | 1,394 | 34.769231 | 126 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_l_syncbn_fast_2xb4-aug-100e_dota.py | _base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota.py'
# This config use longer schedule with Mixup, Mosaic and Random Rotate.
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth' # noqa
# ========================modified parameters=... | 5,386 | 30.87574 | 145 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_m_syncbn_fast_2xb4-36e_dota-ms.py | _base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota-ms.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-m_8xb256-rsb-a1-600e_in1k-ecb3bbd9.pth' # noqa
# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
# Subm... | 1,277 | 36.588235 | 145 | py |
mmyolo | mmyolo-main/configs/rtmdet/rotated/rtmdet-r_l_syncbn_fast_2xb4-36e_dota.py | _base_ = '../../_base_/default_runtime.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth' # noqa
# ========================Frequently modified parameters======================
# -----data related-----
data_root = 'data/split_s... | 11,268 | 32.942771 | 145 | py |
mmyolo | mmyolo-main/configs/rtmdet/cspnext_imagenet_pretrain/cspnext-tiny_8xb256-rsb-a1-600e_in1k.py | _base_ = './cspnext-s_8xb256-rsb-a1-600e_in1k.py'
model = dict(
backbone=dict(deepen_factor=0.167, widen_factor=0.375),
head=dict(in_channels=384))
| 157 | 25.333333 | 59 | py |
mmyolo | mmyolo-main/configs/rtmdet/cspnext_imagenet_pretrain/cspnext-s_8xb256-rsb-a1-600e_in1k.py | _base_ = [
'mmcls::_base_/datasets/imagenet_bs256_rsb_a12.py',
'mmcls::_base_/schedules/imagenet_bs2048_rsb.py',
'mmcls::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['mmdet.models', 'mmyolo.models'], allow_failed_imports=False)
model = dict(
type='ImageClassifier',
backbone=dic... | 1,766 | 24.985294 | 76 | py |
mmyolo | mmyolo-main/configs/rtmdet/distillation/kd_s_rtmdet_m_neck_300e_coco.py | _base_ = '../rtmdet_s_syncbn_fast_8xb32-300e_coco.py'
teacher_ckpt = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_m_syncbn_fast_8xb32-300e_coco/rtmdet_m_syncbn_fast_8xb32-300e_coco_20230102_135952-40af4fe8.pth' # noqa: E501
norm_cfg = dict(type='BN', affine=False, track_running_stats=False)
model = dict(... | 4,108 | 40.09 | 181 | py |
mmyolo | mmyolo-main/configs/rtmdet/distillation/kd_l_rtmdet_x_neck_300e_coco.py | _base_ = '../rtmdet_l_syncbn_fast_8xb32-300e_coco.py'
teacher_ckpt = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_x_syncbn_fast_8xb32-300e_coco/rtmdet_x_syncbn_fast_8xb32-300e_coco_20221231_100345-b85cd476.pth' # noqa: E501
norm_cfg = dict(type='BN', affine=False, track_running_stats=False)
model = dict(... | 4,108 | 40.09 | 181 | py |
mmyolo | mmyolo-main/configs/rtmdet/distillation/kd_m_rtmdet_l_neck_300e_coco.py | _base_ = '../rtmdet_m_syncbn_fast_8xb32-300e_coco.py'
teacher_ckpt = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_l_syncbn_fast_8xb32-300e_coco/rtmdet_l_syncbn_fast_8xb32-300e_coco_20230102_135928-ee3abdc4.pth' # noqa: E501
norm_cfg = dict(type='BN', affine=False, track_running_stats=False)
model = dict(... | 4,108 | 40.09 | 181 | py |
mmyolo | mmyolo-main/configs/rtmdet/distillation/kd_tiny_rtmdet_s_neck_300e_coco.py | _base_ = '../rtmdet_tiny_syncbn_fast_8xb32-300e_coco.py'
teacher_ckpt = 'https://download.openmmlab.com/mmyolo/v0/rtmdet/rtmdet_s_syncbn_fast_8xb32-300e_coco/rtmdet_s_syncbn_fast_8xb32-300e_coco_20221230_182329-0a8c901a.pth' # noqa: E501
norm_cfg = dict(type='BN', affine=False, track_running_stats=False)
model = di... | 4,111 | 40.12 | 181 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_plus_l_fast_8xb8-80e_coco.py | _base_ = './ppyoloe_plus_s_fast_8xb8-80e_coco.py'
# The pretrained model is geted and converted from official PPYOLOE.
# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
load_from = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/ppyoloe_plus_l_obj365_pretra... | 636 | 36.470588 | 133 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_x_fast_8xb16-300e_coco.py | _base_ = './ppyoloe_s_fast_8xb32-300e_coco.py'
# The pretrained model is geted and converted from official PPYOLOE.
# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
checkpoint = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/cspresnet_x_imagenet1k_pretrai... | 793 | 32.083333 | 135 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_s_fast_8xb32-300e_coco.py | _base_ = './ppyoloe_plus_s_fast_8xb8-80e_coco.py'
# The pretrained model is geted and converted from official PPYOLOE.
# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
checkpoint = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/cspresnet_s_imagenet1k_pret... | 1,207 | 31.648649 | 135 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_plus_s_fast_8xb8-80e_coco.py | _base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
# dataset settings
data_root = 'data/coco/'
dataset_type = 'YOLOv5CocoDataset'
# parameters that often need to be modified
img_scale = (640, 640) # width, height
deepen_factor = 0.33
widen_factor = 0.5
max_epochs = 80
num_classes = 80
save_epoch_in... | 7,582 | 30.595833 | 133 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_l_fast_8xb20-300e_coco.py | _base_ = './ppyoloe_s_fast_8xb32-300e_coco.py'
# The pretrained model is geted and converted from official PPYOLOE.
# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
checkpoint = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/cspresnet_l_imagenet1k_pretrai... | 791 | 32 | 135 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_plus_x_fast_8xb8-80e_coco.py | _base_ = './ppyoloe_plus_s_fast_8xb8-80e_coco.py'
# The pretrained model is geted and converted from official PPYOLOE.
# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
load_from = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/ppyoloe_plus_x_obj365_pretra... | 638 | 36.588235 | 133 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_m_fast_8xb28-300e_coco.py | _base_ = './ppyoloe_s_fast_8xb32-300e_coco.py'
# The pretrained model is geted and converted from official PPYOLOE.
# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
checkpoint = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/cspresnet_m_imagenet1k_pretrai... | 793 | 32.083333 | 135 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_plus_m_fast_8xb8-80e_coco.py | _base_ = './ppyoloe_plus_s_fast_8xb8-80e_coco.py'
# The pretrained model is geted and converted from official PPYOLOE.
# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
load_from = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/ppyoloe_plus_m_ojb365_pretra... | 638 | 36.588235 | 133 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_s_fast_8xb32-400e_coco.py | _base_ = './ppyoloe_s_fast_8xb32-300e_coco.py'
max_epochs = 400
model = dict(train_cfg=dict(initial_epoch=133))
default_hooks = dict(param_scheduler=dict(total_epochs=int(max_epochs * 1.2)))
train_cfg = dict(max_epochs=max_epochs)
| 235 | 22.6 | 78 | py |
mmyolo | mmyolo-main/configs/ppyoloe/ppyoloe_plus_s_fast_1xb12-40e_cat.py | # Compared to other same scale models, this configuration consumes too much
# GPU memory and is not validated for now
_base_ = 'ppyoloe_plus_s_fast_8xb8-80e_coco.py'
data_root = './data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
num_last_epo... | 1,833 | 31.175439 | 167 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py | _base_ = './yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py'
deepen_factor = 1.0
widen_factor = 1.0
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
bbox_head=di... | 369 | 22.125 | 64 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py | _base_ = 'yolov5_s-v61_syncbn_8xb16-300e_coco.py'
# fast means faster training speed,
# but less flexibility for multitasking
model = dict(
data_preprocessor=dict(
type='YOLOv5DetDataPreprocessor',
mean=[0., 0., 0.],
std=[255., 255., 255.],
bgr_to_rgb=True))
train_dataloader = dict... | 361 | 26.846154 | 63 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_x-p6-v62_syncbn_fast_8xb16-300e_coco.py | _base_ = './yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py'
deepen_factor = 1.33
widen_factor = 1.25
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
bbox_hea... | 373 | 23.933333 | 64 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_s-v61_syncbn_fast_1xb4-300e_balloon.py | _base_ = './yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
# ========================modified parameters======================
data_root = 'data/balloon/'
# Path of train annotation file
train_ann_file = 'train.json'
train_data_prefix = 'train/' # Prefix of train image path
# Path of val annotation file
val_ann_file = ... | 1,312 | 29.534884 | 71 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py | _base_ = './yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
lr_factor = 0.1
affine_scale = 0.9
loss_cls_weight = 0.3
loss_obj_weight = 0.7
mixup_prob = 0.1
# =======================Unmodified in most cases====... | 2,411 | 29.15 | 74 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_s-v61_fast_1xb12-40e_608x352_cat.py | _base_ = 'yolov5_s-v61_fast_1xb12-40e_cat.py'
# This configuration is used to provide non-square training examples
# Must be a multiple of 32
img_scale = (608, 352) # w h
anchors = [
[(65, 35), (159, 45), (119, 80)], # P3/8
[(215, 77), (224, 116), (170, 166)], # P4/16
[(376, 108), (339, 176), (483, 190... | 2,305 | 31.478873 | 79 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco.py | _base_ = './yolov5_m-p6-v62_syncbn_fast_8xb16-300e_coco.py'
deepen_factor = 1.0
widen_factor = 1.0
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
bbox_head... | 372 | 22.3125 | 64 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_x-v61_syncbn_fast_8xb16-300e_coco.py | _base_ = './yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py'
deepen_factor = 1.33
widen_factor = 1.25
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
bbox_head=d... | 370 | 23.733333 | 64 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_s-v61_syncbn_8xb16-300e_coco.py | _base_ = ['../_base_/default_runtime.py', '../_base_/det_p5_tta.py']
# ========================Frequently modified parameters======================
# -----data related-----
data_root = 'data/coco/' # Root path of data
# Path of train annotation file
train_ann_file = 'annotations/instances_train2017.json'
train_data_p... | 9,466 | 31.31058 | 78 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py | _base_ = 'yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
# ========================modified parameters======================
img_scale = (1280, 1280) # width, height
num_classes = 80 # Number of classes for classification
# Config of batch shapes. Only on val.
# It means not used if batch_shapes_cfg is None.
batch_sha... | 4,851 | 33.906475 | 79 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_n-p6-v62_syncbn_fast_8xb16-300e_coco.py | _base_ = 'yolov5_s-p6-v62_syncbn_fast_8xb16-300e_coco.py'
deepen_factor = 0.33
widen_factor = 0.25
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
bbox_head... | 372 | 22.3125 | 64 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco.py | _base_ = './yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
lr_factor = 0.1
affine_scale = 0.9
loss_cls_weight = 0.3
loss_obj_weight = 0.7
mixup_prob = 0.1
# =======================Unmodified in most cases=======... | 2,408 | 29.1125 | 74 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_s-v61_fast_1xb12-40e_cat.py | _base_ = 'yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
data_root = './data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
anchors = [
[(68, 69), (154, 91), (143, 162)], # P3/8
[(242, 160), (189, 287), (391, 207)], # P4/16
[(353, 33... | 1,932 | 32.912281 | 180 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py | _base_ = './yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
deepen_factor = 0.33
widen_factor = 0.25
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
bbox_head=... | 371 | 22.25 | 64 | py |
mmyolo | mmyolo-main/configs/yolov5/yolov5_s-v61_syncbn-detect_8xb16-300e_coco.py | _base_ = 'yolov5_s-v61_syncbn_8xb16-300e_coco.py'
test_pipeline = [
dict(type='LoadImageFromFile', file_client_args=_base_.file_client_args),
dict(
type='LetterResize',
scale=_base_.img_scale,
allow_scale_up=True,
use_mini_pad=True),
dict(type='LoadAnnotations', with_bbox=Tr... | 719 | 29 | 77 | py |
mmyolo | mmyolo-main/configs/yolov5/crowdhuman/yolov5_s-v61_8xb16-300e_ignore_crowdhuman.py | _base_ = 'yolov5_s-v61_fast_8xb16-300e_crowdhuman.py'
model = dict(
data_preprocessor=dict(
_delete_=True,
type='mmdet.DetDataPreprocessor',
mean=[0., 0., 0.],
std=[255., 255., 255.],
bgr_to_rgb=True),
bbox_head=dict(ignore_iof_thr=0.5))
img_scale = _base_.img_scale
al... | 1,780 | 26.828125 | 77 | py |
mmyolo | mmyolo-main/configs/yolov5/crowdhuman/yolov5_s-v61_fast_8xb16-300e_crowdhuman.py | _base_ = '../yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
# Use the model trained on the COCO as the pretrained model
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth' # noqa
# dataset settings
... | 1,495 | 30.166667 | 180 | py |
mmyolo | mmyolo-main/configs/yolov5/voc/yolov5_l-v61_fast_1xb32-50e_voc.py | _base_ = './yolov5_s-v61_fast_1xb64-50e_voc.py'
deepen_factor = 1.0
widen_factor = 1.0
train_batch_size_per_gpu = 32
train_num_workers = 8
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_l-v61_syncbn_fast_8xb16-300e_coco/yolov5_l-v61_syncbn_fast_8xb16-300e_coco_20220917_031007-096ef0eb.pth' # noq... | 780 | 29.038462 | 180 | py |
mmyolo | mmyolo-main/configs/yolov5/voc/yolov5_m-v61_fast_1xb64-50e_voc.py | _base_ = './yolov5_s-v61_fast_1xb64-50e_voc.py'
deepen_factor = 0.67
widen_factor = 0.75
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_m-v61_syncbn_fast_8xb16-300e_coco/yolov5_m-v61_syncbn_fast_8xb16-300e_coco_20220917_204944-516a710f.pth' # noqa
model = dict(
backbone=dict(
deepen... | 544 | 29.277778 | 180 | py |
mmyolo | mmyolo-main/configs/yolov5/voc/yolov5_n-v61_fast_1xb64-50e_voc.py | _base_ = './yolov5_s-v61_fast_1xb64-50e_voc.py'
deepen_factor = 0.33
widen_factor = 0.25
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739-b804c1ad.pth' # noqa
model = dict(
backbone=dict(
deepen... | 544 | 29.277778 | 180 | py |
mmyolo | mmyolo-main/configs/yolov5/voc/yolov5_s-v61_fast_1xb64-50e_voc.py | _base_ = '../yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
# dataset settings
data_root = 'data/VOCdevkit/'
dataset_type = 'YOLOv5VOCDataset'
# parameters that often need to be modified
num_classes = 20
img_scale = (512, 512) # width, height
max_epochs = 50
train_batch_size_per_gpu = 64
train_num_workers = 8
val_batc... | 8,555 | 30.571956 | 180 | py |
mmyolo | mmyolo-main/configs/yolov5/voc/yolov5_x-v61_fast_1xb32-50e_voc.py | _base_ = './yolov5_s-v61_fast_1xb64-50e_voc.py'
deepen_factor = 1.33
widen_factor = 1.25
train_batch_size_per_gpu = 32
train_num_workers = 8
# TODO: need to add pretrained_model
load_from = None
model = dict(
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=d... | 655 | 23.296296 | 71 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_p5_tta.py | # TODO: Need to solve the problem of multiple file_client_args parameters
# _file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
_file_client_args = dict(backend='disk')
t... | 2,240 | 39.017857 | 87 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_s_fast_8xb8-300e_coco.py | _base_ = ['../_base_/default_runtime.py', 'yolox_p5_tta.py']
# ========================Frequently modified parameters======================
# -----data related-----
data_root = 'data/coco/' # Root path of data
# path of train annotation file
train_ann_file = 'annotations/instances_train2017.json'
train_data_prefix = ... | 10,329 | 30.114458 | 78 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_s_fast_1xb12-40e-rtmdet-hyp_cat.py | _base_ = './yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py'
data_root = './data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
num_last_epochs = 5
max_epochs = 40
train_batch_size_per_gpu = 12
train_num_workers = 4
load_from = 'https://download.op... | 2,326 | 29.220779 | 177 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_nano_fast_8xb32-300e-rtmdet-hyp_coco.py | _base_ = './yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py'
# ========================modified parameters======================
deepen_factor = 0.33
widen_factor = 0.25
use_depthwise = True
# =======================Unmodified in most cases==================
# model settings
model = dict(
backbone=dict(
dee... | 660 | 29.045455 | 69 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_nano_fast_8xb8-300e_coco.py | _base_ = './yolox_tiny_fast_8xb8-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 0.33
widen_factor = 0.25
use_depthwise = True
# =======================Unmodified in most cases==================
# model settings
model = dict(
backbone=dict(
deepen_factor=d... | 648 | 28.5 | 69 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_tiny_fast_8xb8-300e_coco.py | _base_ = './yolox_s_fast_8xb8-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 0.33
widen_factor = 0.375
scaling_ratio_range = (0.5, 1.5)
# =======================Unmodified in most cases==================
img_scale = _base_.img_scale
pre_transform = _base_.pre_transfo... | 3,369 | 32.366337 | 78 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py | _base_ = './yolox_s_fast_8xb8-300e_coco.py'
# ========================modified parameters======================
# Batch size of a single GPU during training
# 8 -> 32
train_batch_size_per_gpu = 32
# Multi-scale training intervals
# 10 -> 1
batch_augments_interval = 1
# Last epoch number to switch training pipeline
#... | 2,494 | 27.352273 | 78 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_m_fast_8xb8-300e_coco.py | _base_ = './yolox_s_fast_8xb8-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
# =======================Unmodified in most cases==================
# model settings
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_... | 465 | 34.846154 | 74 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco.py | _base_ = './yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py'
# ========================modified parameters======================
deepen_factor = 0.33
widen_factor = 0.375
# Multi-scale training intervals
# 10 -> 1
batch_augments_interval = 1
scaling_ratio_range = (0.5, 1.5)
# =======================Unmodified in most ca... | 2,273 | 31.028169 | 77 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_m_fast_8xb32-300e-rtmdet-hyp_coco.py | _base_ = './yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py'
# ========================modified parameters======================
deepen_factor = 0.67
widen_factor = 0.75
# =======================Unmodified in most cases==================
# model settings
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_f... | 477 | 35.769231 | 74 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_l_fast_8xb8-300e_coco.py | _base_ = './yolox_s_fast_8xb8-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 1.0
widen_factor = 1.0
# =======================Unmodified in most cases==================
# model settings
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_fa... | 463 | 34.692308 | 74 | py |
mmyolo | mmyolo-main/configs/yolox/yolox_x_fast_8xb8-300e_coco.py | _base_ = './yolox_s_fast_8xb8-300e_coco.py'
# ========================modified parameters======================
deepen_factor = 1.33
widen_factor = 1.25
# =======================Unmodified in most cases==================
# model settings
model = dict(
backbone=dict(deepen_factor=deepen_factor, widen_factor=widen_... | 465 | 34.846154 | 74 | py |
mmyolo | mmyolo-main/configs/_base_/default_runtime.py | default_scope = 'mmyolo'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(type='CheckpointHook', interval=1),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(ty... | 1,043 | 28.828571 | 72 | py |
mmyolo | mmyolo-main/configs/_base_/det_p5_tta.py | # TODO: Need to solve the problem of multiple file_client_args parameters
# _file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
_file_client_args = dict(backend='disk')
t... | 2,216 | 37.224138 | 89 | py |
mmyolo | mmyolo-main/configs/deploy/base_dynamic.py | _base_ = ['./base_static.py']
onnx_config = dict(
dynamic_axes={
'input': {
0: 'batch',
2: 'height',
3: 'width'
},
'dets': {
0: 'batch',
1: 'num_dets'
},
'labels': {
0: 'batch',
1: 'num_dets'
... | 337 | 17.777778 | 29 | py |
mmyolo | mmyolo-main/configs/deploy/detection_tensorrt_static-640x640.py | _base_ = ['./base_static.py']
onnx_config = dict(input_shape=(640, 640))
backend_config = dict(
type='tensorrt',
common_config=dict(fp16_mode=False, max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 640, ... | 536 | 34.8 | 104 | py |
mmyolo | mmyolo-main/configs/deploy/detection_tensorrt-fp16_dynamic-64x64-1344x1344.py | _base_ = ['./base_dynamic.py']
backend_config = dict(
type='tensorrt',
common_config=dict(fp16_mode=True, max_workspace_size=1 << 32),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 64, 64],
opt_shape=[1, 3, 64... | 493 | 34.285714 | 104 | py |
mmyolo | mmyolo-main/configs/deploy/detection_tensorrt_dynamic-192x192-960x960.py | _base_ = ['./base_dynamic.py']
backend_config = dict(
type='tensorrt',
common_config=dict(fp16_mode=False, max_workspace_size=1 << 30),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 192, 192],
opt_shape=[1, 3,... | 494 | 34.357143 | 104 | py |
mmyolo | mmyolo-main/configs/deploy/detection_onnxruntime_static.py | _base_ = ['./base_static.py']
codebase_config = dict(
type='mmyolo',
task='ObjectDetection',
model_type='end2end',
post_processing=dict(
score_threshold=0.05,
confidence_threshold=0.005,
iou_threshold=0.5,
max_output_boxes_per_class=200,
pre_top_k=5000,
ke... | 439 | 26.5 | 41 | py |
mmyolo | mmyolo-main/configs/deploy/detection_tensorrt-int8_static-640x640.py | _base_ = ['./base_static.py']
onnx_config = dict(input_shape=(640, 640))
backend_config = dict(
type='tensorrt',
common_config=dict(
fp16_mode=True, max_workspace_size=1 << 30, int8_mode=True),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
... | 627 | 35.941176 | 104 | py |
mmyolo | mmyolo-main/configs/deploy/detection_rknn-fp16_static-320x320.py | _base_ = ['./base_static.py']
onnx_config = dict(
input_shape=[320, 320], output_names=['feat0', 'feat1', 'feat2'])
codebase_config = dict(model_type='rknn')
backend_config = dict(
type='rknn',
common_config=dict(target_platform='rv1126', optimization_level=1),
quantization_config=dict(do_quantization=F... | 378 | 36.9 | 71 | py |
mmyolo | mmyolo-main/configs/deploy/base_static.py | onnx_config = dict(
type='onnx',
export_params=True,
keep_initializers_as_inputs=False,
opset_version=11,
save_file='end2end.onnx',
input_names=['input'],
output_names=['dets', 'labels'],
input_shape=None,
optimize=True)
codebase_config = dict(
type='mmyolo',
task='ObjectDete... | 624 | 25.041667 | 39 | py |
mmyolo | mmyolo-main/configs/deploy/detection_tensorrt-int8_dynamic-192x192-960x960.py | _base_ = ['./base_dynamic.py']
backend_config = dict(
type='tensorrt',
common_config=dict(
fp16_mode=True, max_workspace_size=1 << 30, int8_mode=True),
model_inputs=[
dict(
input_shapes=dict(
input=dict(
min_shape=[1, 3, 192, 192],
... | 585 | 35.625 | 104 | py |
mmyolo | mmyolo-main/configs/deploy/detection_rknn-int8_static-320x320.py | _base_ = ['./base_static.py']
onnx_config = dict(
input_shape=[320, 320], output_names=['feat0', 'feat1', 'feat2'])
codebase_config = dict(model_type='rknn')
backend_config = dict(
type='rknn',
common_config=dict(target_platform='rv1126', optimization_level=1),
quantization_config=dict(do_quantization=T... | 377 | 36.8 | 71 | py |
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