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ultralytics 8.2.50 new Streamlit live inference Solution (#14210)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
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Glenn Jocher 2024-07-05 22:02:38 +02:00 • committed by GitHub
parent 5f0fd710a4
commit 26a664f636
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20 changed files with 350 additions and 22 deletions
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6
ultralytics/data/augment.py
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@ -686,7 +686,7 @@ class RandomFlip:
flip_idx (array-like, optional): Index mapping for flipping keypoints, if any.
"""
assert direction in {"horizontal", "vertical"}, f"Support direction `horizontal` or `vertical`, got {direction}"
assert 0 <= p <= 1.0
assert 0 <= p <= 1.0, f"The probability should be in range [0, 1], but got {p}."
self.p = p
self.direction = direction
@ -1210,7 +1210,7 @@ def classify_transforms(
import torchvision.transforms as T # scope for faster 'import ultralytics'
if isinstance(size, (tuple, list)):
assert len(size) == 2
assert len(size) == 2, f"'size' tuples must be length 2, not length {len(size)}"
scale_size = tuple(math.floor(x / crop_fraction) for x in size)
else:
scale_size = math.floor(size / crop_fraction)
@ -1288,7 +1288,7 @@ def classify_augmentations(
secondary_tfl = []
disable_color_jitter = False
if auto_augment:
assert isinstance(auto_augment, str)
assert isinstance(auto_augment, str), f"Provided argument should be string, but got type {type(auto_augment)}"
# color jitter is typically disabled if AA/RA on,
# this allows override without breaking old hparm cfgs
disable_color_jitter = not force_color_jitter