xdobro23/YOLOv8-16bit
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Add best.pt val and COCO pycocotools val (#98)

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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>
This commit is contained in:
Glenn Jocher
2022-12-27 04:56:24 +01:00
committed by GitHub
parent a1808eeda4
commit 6f0ba81427
12 changed files with 159 additions and 115 deletions
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8
ultralytics/yolo/v8/detect/train.py
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@ -13,7 +13,7 @@ from ultralytics.yolo.utils.metrics import smooth_BCE
from ultralytics.yolo.utils.ops import xywh2xyxy
from ultralytics.yolo.utils.plotting import plot_images, plot_results
from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
from ultralytics.yolo.utils.torch_utils import de_parallel
from ultralytics.yolo.utils.torch_utils import de_parallel, strip_optimizer
# BaseTrainer python usage
@ -54,10 +54,10 @@ class DetectionTrainer(BaseTrainer):
# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
self.model.names = self.data["names"]
def load_model(self, model_cfg=None, weights=None):
model = DetectionModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"])
def load_model(self, model_cfg=None, weights=None, verbose=True):
model = DetectionModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"], verbose=verbose)
if weights:
model.load(weights)
model.load(weights, verbose)
return model
def get_validator(self):