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ultralytics 8.0.59 new MLFlow and feature updates (#1720)

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Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: St. HeMeow <sheng.heyang@gmail.com>
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Co-authored-by: Roshanlal <roshanlaladchitre103@gmail.com>
Co-authored-by: Lorenzo Mammana <lorenzo.mammana@orobix.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
This commit is contained in:
Glenn Jocher
2023-03-31 20:33:02 +02:00
committed by GitHub
parent ccb6419835
commit e7876e1ba9
29 changed files with 326 additions and 160 deletions
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24
ultralytics/yolo/cfg/__init__.py
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@ -11,14 +11,28 @@ from typing import Dict, List, Union
from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_PATH, LOGGER, ROOT, USER_CONFIG_DIR,
IterableSimpleNamespace, __version__, checks, colorstr, yaml_load, yaml_print)
# Define valid tasks and modes
MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
TASKS = 'detect', 'segment', 'classify', 'pose'
TASK2DATA = {
'detect': 'coco128.yaml',
'segment': 'coco128-seg.yaml',
'pose': 'coco128-pose.yaml',
'classify': 'imagenet100'}
TASK2MODEL = {
'detect': 'yolov8n.pt',
'segment': 'yolov8n-seg.pt',
'pose': 'yolov8n-pose.yaml',
'classify': 'yolov8n-cls.pt'} # temp
CLI_HELP_MSG = \
f"""
Arguments received: {str(['yolo'] + sys.argv[1:])}. Ultralytics 'yolo' commands use the following syntax:
yolo TASK MODE ARGS
Where TASK (optional) is one of [detect, segment, classify]
MODE (required) is one of [train, val, predict, export, track]
Where TASK (optional) is one of {TASKS}
MODE (required) is one of {MODES}
ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
@ -59,12 +73,6 @@ CFG_BOOL_KEYS = ('save', 'exist_ok', 'verbose', 'deterministic', 'single_cls', '
'save_conf', 'save_crop', 'hide_labels', 'hide_conf', 'visualize', 'augment', 'agnostic_nms',
'retina_masks', 'boxes', 'keras', 'optimize', 'int8', 'dynamic', 'simplify', 'nms', 'v5loader')
# Define valid tasks and modes
MODES = 'train', 'val', 'predict', 'export', 'track', 'benchmark'
TASKS = 'detect', 'segment', 'classify'
TASK2DATA = {'detect': 'coco128.yaml', 'segment': 'coco128-seg.yaml', 'classify': 'imagenet100'}
TASK2MODEL = {'detect': 'yolov8n.pt', 'segment': 'yolov8n-seg.pt', 'classify': 'yolov8n-cls.pt'}
def cfg2dict(cfg):
"""

2
ultralytics/yolo/cfg/default.yaml
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@ -26,7 +26,7 @@ seed: 0 # random seed for reproducibility
deterministic: True # whether to enable deterministic mode
single_cls: False # train multi-class data as single-class
image_weights: False # use weighted image selection for training
rect: False # support rectangular training if mode='train', support rectangular evaluation if mode='val'
rect: False # rectangular training if mode='train' or rectangular validation if mode='val'
cos_lr: False # use cosine learning rate scheduler
close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
resume: False # resume training from last checkpoint