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ultralytics 8.0.105 classification hyp fix and new onplot callbacks (#2684)

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Co-authored-by: ayush chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ivan Shcheklein <shcheklein@gmail.com>
This commit is contained in:
Glenn Jocher
2023-05-17 19:10:20 +02:00
committed by GitHub
parent b1119d512e
commit 23fc50641c
92 changed files with 378 additions and 206 deletions
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7
ultralytics/yolo/v8/detect/train.py
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@ -121,17 +121,18 @@ class DetectionTrainer(BaseTrainer):
cls=batch['cls'].squeeze(-1),
bboxes=batch['bboxes'],
paths=batch['im_file'],
fname=self.save_dir / f'train_batch{ni}.jpg')
fname=self.save_dir / f'train_batch{ni}.jpg',
on_plot=self.on_plot)
def plot_metrics(self):
"""Plots metrics from a CSV file."""
plot_results(file=self.csv) # save results.png
plot_results(file=self.csv, on_plot=self.on_plot) # save results.png
def plot_training_labels(self):
"""Create a labeled training plot of the YOLO model."""
boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0)
cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0)
plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir)
plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir, on_plot=self.on_plot)
# Criterion class for computing training losses

13
ultralytics/yolo/v8/detect/val.py
View File

@ -24,7 +24,7 @@ class DetectionValidator(BaseValidator):
self.args.task = 'detect'
self.is_coco = False
self.class_map = None
self.metrics = DetMetrics(save_dir=self.save_dir)
self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
self.iouv = torch.linspace(0.5, 0.95, 10) # iou vector for mAP@0.5:0.95
self.niou = self.iouv.numel()
@ -145,7 +145,10 @@ class DetectionValidator(BaseValidator):
if self.args.plots:
for normalize in True, False:
self.confusion_matrix.plot(save_dir=self.save_dir, names=self.names.values(), normalize=normalize)
self.confusion_matrix.plot(save_dir=self.save_dir,
names=self.names.values(),
normalize=normalize,
on_plot=self.on_plot)
def _process_batch(self, detections, labels):
"""
@ -215,7 +218,8 @@ class DetectionValidator(BaseValidator):
batch['bboxes'],
paths=batch['im_file'],
fname=self.save_dir / f'val_batch{ni}_labels.jpg',
names=self.names)
names=self.names,
on_plot=self.on_plot)
def plot_predictions(self, batch, preds, ni):
"""Plots predicted bounding boxes on input images and saves the result."""
@ -223,7 +227,8 @@ class DetectionValidator(BaseValidator):
*output_to_target(preds, max_det=15),
paths=batch['im_file'],
fname=self.save_dir / f'val_batch{ni}_pred.jpg',
names=self.names) # pred
names=self.names,
on_plot=self.on_plot) # pred
def save_one_txt(self, predn, save_conf, shape, file):
"""Save YOLO detections to a txt file in normalized coordinates in a specific format."""