Replace nosave and noval with save and val (#127)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/configs/default.yaml

@@ -1,71 +1,69 @@
 # YOLO 🚀 by Ultralytics, GPL-3.0 license
 # Default training settings and hyperparameters for medium-augmentation COCO training
 
-# Task and Mode
-task: "classify"  # choices=['detect', 'segment', 'classify', 'init'] # init is a special case
-mode: "train"  # choice=['train', 'val', 'predict']
+task: "classify" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run.
+mode: "train" # choices=['train', 'val', 'predict'] # mode to run task in.
 
 # Train settings -------------------------------------------------------------------------------------------------------
-model: null  # i.e. yolov5s.pt, yolo.yaml
-data: null  # i.e. coco128.yaml
-epochs: 100
-batch_size: 16
-imgsz: 640
-nosave: False
-cache: False  # True/ram, disk or False
-device: ''  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-workers: 8
-project: null
-name: null
-exist_ok: False
-pretrained: False
-optimizer: 'SGD'  # choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
-verbose: False
-seed: 0
-deterministic: True
-local_rank: -1
-single_cls: False  # train multi-class data as single-class
-image_weights: False  # use weighted image selection for training
-rect: False  # support rectangular training
-cos_lr: False  # use cosine LR scheduler
-close_mosaic: 10  # disable mosaic for final 10 epochs
-resume: False
+model: null # i.e. yolov5s.pt, yolo.yaml. Path to model file
+data: null # i.e. coco128.yaml. Path to data file
+epochs: 100 # number of epochs to train for
+batch_size: 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
+device: '' # cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on
+workers: 8 # number of worker threads for data loading
+project: null # project name
+name: null # experiment name
+exist_ok: False # whether to overwrite existing experiment
+pretrained: False # whether to use a pretrained model
+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
+cos_lr: False # use cosine learning rate scheduler
+close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
+resume: False # resume training from last checkpoint
 # Segmentation
-overlap_mask: True  # masks overlap
-mask_ratio: 4  # mask downsample ratio
+overlap_mask: True # masks should overlap during training
+mask_ratio: 4 # mask downsample ratio
 # Classification
-dropout: False  # use dropout
-
+dropout: False # use dropout regularization
 
 # Val/Test settings ----------------------------------------------------------------------------------------------------
-noval: False
-save_json: False
-save_hybrid: False
-conf_thres: 0.001
-iou_thres: 0.7
-max_det: 300
-half: False
-dnn: False  # use OpenCV DNN for ONNX inference
-plots: True
+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
+max_det: 300 # maximum number of detections per image
+half: False # use half precision (FP16)
+dnn: False # use OpenCV DNN for ONNX inference
+plots: True # show plots during training
 
 # Prediction settings --------------------------------------------------------------------------------------------------
-source: "ultralytics/assets/"
-view_img: False
-save_txt: False
-save_conf: False
-save_crop: False
-hide_labels: False  # hide labels
-hide_conf: False
-vid_stride: 1  # video frame-rate stride
-line_thickness: 3  # bounding box thickness (pixels)
-update: False  # Update all models
-visualize: False
-augment: False
-agnostic_nms: False  # class-agnostic NMS
-retina_masks: False
+source: "ultralytics/assets" # source directory for images or videos
+show: False # show results if possible
+save_txt: False # save results as .txt file
+save_conf: False # save results with confidence scores
+save_crop: False # save cropped images with results
+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
+retina_masks: False # use retina masks for object detection
 
 # Export settings ------------------------------------------------------------------------------------------------------
-format: torchscript
+format: torchscript # format to export to
 keras: False  # use Keras
 optimize: False  # TorchScript: optimize for mobile
 int8: False  # CoreML/TF INT8 quantization
@@ -103,11 +101,11 @@ mosaic: 1.0  # image mosaic (probability)
 mixup: 0.0  # image mixup (probability)
 copy_paste: 0.0  # segment copy-paste (probability)
 
-# For debugging. Don't change
-v5loader: False
-
 # Hydra configs --------------------------------------------------------------------------------------------------------
 hydra:
   output_subdir: null  # disable hydra directory creation
   run:
     dir: .
+
+# Debug, do not modify -------------------------------------------------------------------------------------------------
+v5loader: False  # use legacy YOLOv5 dataloader