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ultralytics 8.0.196 instance-mean Segment loss (#5285)

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Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>
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Glenn Jocher 2023-10-09 20:08:39 +02:00 • committed by GitHub
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22
docs/guides/kfold-cross-validation.md
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@ -153,7 +153,7 @@ The rows index the label files, each corresponding to an image in your dataset,
```python
folds = [f'split_{n}' for n in range(1, ksplit + 1)]
folds_df = pd.DataFrame(index=indx, columns=folds)
for idx, (train, val) in enumerate(kfolds, start=1):
folds_df[f'split_{idx}'].loc[labels_df.iloc[train].index] = 'train'
folds_df[f'split_{idx}'].loc[labels_df.iloc[val].index] = 'val'
@ -163,11 +163,11 @@ The rows index the label files, each corresponding to an image in your dataset,
```python
fold_lbl_distrb = pd.DataFrame(index=folds, columns=cls_idx)
for n, (train_indices, val_indices) in enumerate(kfolds, start=1):
train_totals = labels_df.iloc[train_indices].sum()
val_totals = labels_df.iloc[val_indices].sum()
# To avoid division by zero, we add a small value (1E-7) to the denominator
ratio = val_totals / (train_totals + 1E-7)
fold_lbl_distrb.loc[f'split_{n}'] = ratio
@ -179,19 +179,19 @@ The ideal scenario is for all class ratios to be reasonably similar for each spl
```python
supported_extensions = ['.jpg', '.jpeg', '.png']
# Initialize an empty list to store image file paths
images = []
# Loop through supported extensions and gather image files
for ext in supported_extensions:
images.extend(sorted((dataset_path / 'images').rglob(f"*{ext}")))
# Create the necessary directories and dataset YAML files (unchanged)
save_path = Path(dataset_path / f'{datetime.date.today().isoformat()}_{ksplit}-Fold_Cross-val')
save_path.mkdir(parents=True, exist_ok=True)
ds_yamls = []
for split in folds_df.columns:
# Create directories
split_dir = save_path / split
@ -200,11 +200,11 @@ The ideal scenario is for all class ratios to be reasonably similar for each spl
(split_dir / 'train' / 'labels').mkdir(parents=True, exist_ok=True)
(split_dir / 'val' / 'images').mkdir(parents=True, exist_ok=True)
(split_dir / 'val' / 'labels').mkdir(parents=True, exist_ok=True)
# Create dataset YAML files
dataset_yaml = split_dir / f'{split}_dataset.yaml'
ds_yamls.append(dataset_yaml)
with open(dataset_yaml, 'w') as ds_y:
yaml.safe_dump({
'path': split_dir.as_posix(),
@ -224,7 +224,7 @@ The ideal scenario is for all class ratios to be reasonably similar for each spl
# Destination directory
img_to_path = save_path / split / k_split / 'images'
lbl_to_path = save_path / split / k_split / 'labels'
# Copy image and label files to new directory (SamefileError if file already exists)
shutil.copy(image, img_to_path / image.name)
shutil.copy(label, lbl_to_path / label.name)
@ -252,7 +252,7 @@ fold_lbl_distrb.to_csv(save_path / "kfold_label_distribution.csv")
```python
results = {}
# Define your additional arguments here
batch = 16
project = 'kfold_demo'