diff --git a/package.json b/package.json index 8c2a53f..2ae609e 100644 --- a/package.json +++ b/package.json @@ -2,7 +2,7 @@ "name": "ultralytics-snippets", "displayName": "Ultralytics Snippets", "description": "Snippets to use with the Ultralytics Python library.", - "version": "0.1.0", + "version": "0.1.1", "publisher": "Ultralytics", "repository": { "type": "git", diff --git a/snippets/examples.json b/snippets/examples.json index d9b602c..30f2fd8 100644 --- a/snippets/examples.json +++ b/snippets/examples.json @@ -205,74 +205,74 @@ "'''", "model = YOLO(\"yolov${1|8,5,9,10|}${2|n,s,m,l,x,c,e|}${3|.,-cls.,-seg.,-obb.,-pose.,-world.,-worldv2.|}pt\")", "results: list = model.train(", - "data=${4:\"coco8.yaml\"}, # (str, optional) path to data file, i.e. coco8.yaml", - "epochs=${5:100}, # (int) number of epochs to train for", - "time=${6:None}, # (float, optional) number of hours to train for, overrides epochs if supplied", - "patience=${7:100}, # (int) epochs to wait for no observable improvement for early stopping of training", - "batch=${8:16}, # (int) number of images per batch (-1 for AutoBatch)", - "imgsz=${9:640}, # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes", - "save=${10:True}, # (bool) save train checkpoints and predict results", - "save_period=${11:-1}, # (int) Save checkpoint every x epochs (disabled if < 1)", - "cache=${12:False}, # (bool) True/ram, disk or False. Use cache for data loading", - "device=${13:None}, # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu", - "workers=${14:8}, # (int) number of worker threads for data loading (per RANK if DDP)", - "project=${15:None}, # (str, optional) project name", - "name=${16:None}, # (str, optional) experiment name, results saved to 'project/name' directory", - "exist_ok=${17:False}, # (bool) whether to overwrite existing experiment", - "val=${18:True}, # (bool) validate/test during training", - "pretrained=${19:True}, # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)", - "optimizer=\"${20|SGD,Adam,Adamax,AdamW,NAdam,RAdam,RMSProp,auto|}\", # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]", - "verbose=${21:True}, # (bool) whether to print verbose output", - "seed=${22:0}, # (int) random seed for reproducibility", - "deterministic=${23:True}, # (bool) whether to enable deterministic mode", - "single_cls=${24:False}, # (bool) train multi-class data as single-class", - "rect=${25:False}, # (bool) rectangular training if mode='train' or rectangular validation if mode='val'", - "cos_lr=${26:False}, # (bool) use cosine learning rate scheduler", - "close_mosaic=${27:10}, # (int) disable mosaic augmentation for final epochs (0 to disable)", - "resume=${28:False}, # (bool) resume training from last checkpoint", - "amp=${29:True}, # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check", - "fraction=${30:1.0}, # (float) dataset fraction to train on (default is 1.0, all images in train set)", - "profile=${31:False}, # (bool) profile ONNX and TensorRT speeds during training for loggers", - "freeze=${32:None}, # (int | list, optional) freeze first n layers, or freeze list of layer indices during training", - "multi_scale=${33:False}, # (bool) Whether to use multiscale during training", - "plots=${34:True} # (bool) save plots and images during train/val", - "# Segmentation", - "overlap_mask=${35:True}, # (bool) masks should overlap during training (segment train only)", - "mask_ratio=${36:4}, # (int) mask downsample ratio (segment train only)", - "# Classification", - "dropout=${37:0.0}, # (float) use dropout regularization (classify train only)", - "# Hyperparameters", - "lr0=${38:0.01}, # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)", - "lrf=${39:0.01}, # (float) final learning rate (lr0 * lrf)", - "momentum=${40:0.937}, # (float) SGD momentum/Adam beta1", - "weight_decay=${41:0.0005}, # (float) optimizer weight decay 5e-4", - "warmup_epochs=${42:3.0}, # (float) warmup epochs (fractions ok)", - "warmup_momentum=${43:0.8}, # (float) warmup initial momentum", - "warmup_bias_lr=${44:0.1}, # (float) warmup initial bias lr", - "box=${45:7.5}, # (float) box loss gain", - "cls=${46:0.5}, # (float) cls loss gain (scale with pixels)", - "dfl=${47:1.5}, # (float) dfl loss gain", - "pose=${48:12.0}, # (float) pose loss gain", - "kobj=${49:1.0}, # (float) keypoint obj loss gain", - "label_smoothing=${50:0.0}, # (float) label smoothing (fraction)", - "nbs=${51:64}, # (int) nominal batch size", - "hsv_h=${52:0.015}, # (float) image HSV-Hue augmentation (fraction)", - "hsv_s=${53:0.7}, # (float) image HSV-Saturation augmentation (fraction)", - "hsv_v=${54:0.4}, # (float) image HSV-Value augmentation (fraction)", - "degrees=${55:0.0}, # (float) image rotation (+/- deg)", - "translate=${56:0.1}, # (float) image translation (+/- fraction)", - "scale=${57:0.5}, # (float) image scale (+/- gain)", - "shear=${58:0.0}, # (float) image shear (+/- deg)", - "perspective=${59:0.0}, # (float) image perspective (+/- fraction), range 0-0.001", - "flipud=${60:0.0}, # (float) image flip up-down (probability)", - "fliplr=${61:0.5}, # (float) image flip left-right (probability)", - "bgr=${62:0.0}, # (float) image channel BGR (probability)", - "mosaic=${63:1.0}, # (float) image mosaic (probability)", - "mixup=${64:0.0}, # (float) image mixup (probability)", - "copy_paste=${65:0.0}, # (float) segment copy-paste (probability)", - "auto_augment=\"${66|randaugment,autoaugment,augmix|}\", # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)", - "erasing=${67:0.4}, # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.", - "crop_fraction=${68:1.0}, # (float) image crop fraction for classification (0.1-1), 1.0 means no crop, must be greater than 0.", + " data=${4:\"coco8.yaml\"}, # (str, optional) path to data file, i.e. coco8.yaml", + " epochs=${5:100}, # (int) number of epochs to train for", + " time=${6:None}, # (float, optional) number of hours to train for, overrides epochs if supplied", + " patience=${7:100}, # (int) epochs to wait for no observable improvement for early stopping of training", + " batch=${8:16}, # (int) number of images per batch (-1 for AutoBatch)", + " imgsz=${9:640}, # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes", + " save=${10:True}, # (bool) save train checkpoints and predict results", + " save_period=${11:-1}, # (int) Save checkpoint every x epochs (disabled if < 1)", + " cache=${12:False}, # (bool) True/ram, disk or False. Use cache for data loading", + " device=${13:None}, # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu", + " workers=${14:8}, # (int) number of worker threads for data loading (per RANK if DDP)", + " project=${15:None}, # (str, optional) project name", + " name=${16:None}, # (str, optional) experiment name, results saved to 'project/name' directory", + " exist_ok=${17:False}, # (bool) whether to overwrite existing experiment", + " val=${18:True}, # (bool) validate/test during training", + " pretrained=${19:True}, # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)", + " optimizer=\"${20|SGD,Adam,Adamax,AdamW,NAdam,RAdam,RMSProp,auto|}\", # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]", + " verbose=${21:True}, # (bool) whether to print verbose output", + " seed=${22:0}, # (int) random seed for reproducibility", + " deterministic=${23:True}, # (bool) whether to enable deterministic mode", + " single_cls=${24:False}, # (bool) train multi-class data as single-class", + " rect=${25:False}, # (bool) rectangular training if mode='train' or rectangular validation if mode='val'", + " cos_lr=${26:False}, # (bool) use cosine learning rate scheduler", + " close_mosaic=${27:10}, # (int) disable mosaic augmentation for final epochs (0 to disable)", + " resume=${28:False}, # (bool) resume training from last checkpoint", + " amp=${29:True}, # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check", + " fraction=${30:1.0}, # (float) dataset fraction to train on (default is 1.0, all images in train set)", + " profile=${31:False}, # (bool) profile ONNX and TensorRT speeds during training for loggers", + " freeze=${32:None}, # (int | list, optional) freeze first n layers, or freeze list of layer indices during training", + " multi_scale=${33:False}, # (bool) Whether to use multiscale during training", + " plots=${34:True}, # (bool) save plots and images during train/val", + " # Segmentation", + " overlap_mask=${35:True}, # (bool) masks should overlap during training (segment train only)", + " mask_ratio=${36:4}, # (int) mask downsample ratio (segment train only)", + " # Classification", + " dropout=${37:0.0}, # (float) use dropout regularization (classify train only)", + " # Hyperparameters", + " lr0=${38:0.01}, # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)", + " lrf=${39:0.01}, # (float) final learning rate (lr0 * lrf)", + " momentum=${40:0.937}, # (float) SGD momentum/Adam beta1", + " weight_decay=${41:0.0005}, # (float) optimizer weight decay 5e-4", + " warmup_epochs=${42:3.0}, # (float) warmup epochs (fractions ok)", + " warmup_momentum=${43:0.8}, # (float) warmup initial momentum", + " warmup_bias_lr=${44:0.1}, # (float) warmup initial bias lr", + " box=${45:7.5}, # (float) box loss gain", + " cls=${46:0.5}, # (float) cls loss gain (scale with pixels)", + " dfl=${47:1.5}, # (float) dfl loss gain", + " pose=${48:12.0}, # (float) pose loss gain", + " kobj=${49:1.0}, # (float) keypoint obj loss gain", + " label_smoothing=${50:0.0}, # (float) label smoothing (fraction)", + " nbs=${51:64}, # (int) nominal batch size", + " hsv_h=${52:0.015}, # (float) image HSV-Hue augmentation (fraction)", + " hsv_s=${53:0.7}, # (float) image HSV-Saturation augmentation (fraction)", + " hsv_v=${54:0.4}, # (float) image HSV-Value augmentation (fraction)", + " degrees=${55:0.0}, # (float) image rotation (+/- deg)", + " translate=${56:0.1}, # (float) image translation (+/- fraction)", + " scale=${57:0.5}, # (float) image scale (+/- gain)", + " shear=${58:0.0}, # (float) image shear (+/- deg)", + " perspective=${59:0.0}, # (float) image perspective (+/- fraction), range 0-0.001", + " flipud=${60:0.0}, # (float) image flip up-down (probability)", + " fliplr=${61:0.5}, # (float) image flip left-right (probability)", + " bgr=${62:0.0}, # (float) image channel BGR (probability)", + " mosaic=${63:1.0}, # (float) image mosaic (probability)", + " mixup=${64:0.0}, # (float) image mixup (probability)", + " copy_paste=${65:0.0}, # (float) segment copy-paste (probability)", + " auto_augment=\"${66|randaugment,autoaugment,augmix|}\", # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)", + " erasing=${67:0.4}, # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.", + " crop_fraction=${68:1.0}, # (float) image crop fraction for classification (0.1-1), 1.0 means no crop, must be greater than 0.", ")", "# reference https://docs.ultralytics.com/modes/predict/" ],