xdobro23/YOLOv8-16bit
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Simplify argument names (#141)

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This commit is contained in:
Glenn Jocher
2023-01-03 19:47:56 +01:00
committed by GitHub
parent d74de2582c
commit 3c18aa5278
9 changed files with 22 additions and 24 deletions
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8
ultralytics/yolo/configs/default.yaml
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@ -9,7 +9,7 @@ model: null # i.e. yolov8n.pt, yolov8n.yaml. Path to model file
data: null # i.e. coco128.yaml. Path to data file
epochs: 100 # number of epochs to train for
patience: 50 # TODO: epochs to wait for no observable improvement for early stopping of training
batch_size: 16 # number of images per batch
batch: 16 # number of images per batch
imgsz: 640 # size of input images
save: True # save checkpoints
cache: False # True/ram, disk or False. Use cache for data loading
@ -23,7 +23,6 @@ optimizer: 'SGD' # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
verbose: False # whether to print verbose output
seed: 0 # random seed for reproducibility
deterministic: True # whether to enable deterministic mode
local_rank: -1 # local rank for distributed training
single_cls: False # train multi-class data as single-class
image_weights: False # use weighted image selection for training
rect: False # support rectangular training
@ -40,8 +39,8 @@ dropout: False # use dropout regularization
val: True # validate/test during training
save_json: False # save results to JSON file
save_hybrid: False # save hybrid version of labels (labels + additional predictions)
conf_thres: 0.001 # object confidence threshold for detection
iou_thres: 0.7 # intersection over union threshold for NMS
conf: 0.001 # object confidence threshold for detection
iou: 0.7 # intersection over union (IoU) threshold for NMS
max_det: 300 # maximum number of detections per image
half: False # use half precision (FP16)
dnn: False # use OpenCV DNN for ONNX inference
@ -57,7 +56,6 @@ hide_labels: False # hide labels
hide_conf: False # hide confidence scores
vid_stride: 1 # video frame-rate stride
line_thickness: 3 # bounding box thickness (pixels)
update: False # Update all models
visualize: False # visualize results
augment: False # apply data augmentation to images
agnostic_nms: False # class-agnostic NMS