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Burhan-QBurhan-Q
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Merge pull request #8 from Burhan-Q/dev
`0.1.1` Patch - correct `yolo-train-kwords`
2 parents 74fa9f4 + 6d219df commit 896ebe4

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‎package.json‎

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@@ -2,7 +2,7 @@
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"name": "ultralytics-snippets",
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"displayName": "Ultralytics Snippets",
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"description": "Snippets to use with the Ultralytics Python library.",
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"version": "0.1.0",
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"version": "0.1.1",
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"publisher": "Ultralytics",
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"repository": {
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"type": "git",

‎snippets/examples.json‎

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@@ -205,74 +205,74 @@
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"'''",
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"model = YOLO(\"yolov${1|8,5,9,10|}${2|n,s,m,l,x,c,e|}${3|.,-cls.,-seg.,-obb.,-pose.,-world.,-worldv2.|}pt\")",
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"results: list = model.train(",
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"data=${4:\"coco8.yaml\"}, # (str, optional) path to data file, i.e. coco8.yaml",
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"epochs=${5:100}, # (int) number of epochs to train for",
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"time=${6:None}, # (float, optional) number of hours to train for, overrides epochs if supplied",
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"patience=${7:100}, # (int) epochs to wait for no observable improvement for early stopping of training",
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"batch=${8:16}, # (int) number of images per batch (-1 for AutoBatch)",
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"imgsz=${9:640}, # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes",
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"save=${10:True}, # (bool) save train checkpoints and predict results",
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"save_period=${11:-1}, # (int) Save checkpoint every x epochs (disabled if < 1)",
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"cache=${12:False}, # (bool) True/ram, disk or False. Use cache for data loading",
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"device=${13:None}, # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu",
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"workers=${14:8}, # (int) number of worker threads for data loading (per RANK if DDP)",
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"project=${15:None}, # (str, optional) project name",
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"name=${16:None}, # (str, optional) experiment name, results saved to 'project/name' directory",
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"exist_ok=${17:False}, # (bool) whether to overwrite existing experiment",
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"val=${18:True}, # (bool) validate/test during training",
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"pretrained=${19:True}, # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)",
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"optimizer=\"${20|SGD,Adam,Adamax,AdamW,NAdam,RAdam,RMSProp,auto|}\", # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]",
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"verbose=${21:True}, # (bool) whether to print verbose output",
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"seed=${22:0}, # (int) random seed for reproducibility",
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"deterministic=${23:True}, # (bool) whether to enable deterministic mode",
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"single_cls=${24:False}, # (bool) train multi-class data as single-class",
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"rect=${25:False}, # (bool) rectangular training if mode='train' or rectangular validation if mode='val'",
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"cos_lr=${26:False}, # (bool) use cosine learning rate scheduler",
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"close_mosaic=${27:10}, # (int) disable mosaic augmentation for final epochs (0 to disable)",
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"resume=${28:False}, # (bool) resume training from last checkpoint",
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"amp=${29:True}, # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check",
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"fraction=${30:1.0}, # (float) dataset fraction to train on (default is 1.0, all images in train set)",
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"profile=${31:False}, # (bool) profile ONNX and TensorRT speeds during training for loggers",
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"freeze=${32:None}, # (int | list, optional) freeze first n layers, or freeze list of layer indices during training",
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"multi_scale=${33:False}, # (bool) Whether to use multiscale during training",
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"plots=${34:True} # (bool) save plots and images during train/val",
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"# Segmentation",
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"overlap_mask=${35:True}, # (bool) masks should overlap during training (segment train only)",
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"mask_ratio=${36:4}, # (int) mask downsample ratio (segment train only)",
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"# Classification",
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"dropout=${37:0.0}, # (float) use dropout regularization (classify train only)",
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"# Hyperparameters",
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"lr0=${38:0.01}, # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)",
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"lrf=${39:0.01}, # (float) final learning rate (lr0 * lrf)",
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"momentum=${40:0.937}, # (float) SGD momentum/Adam beta1",
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"weight_decay=${41:0.0005}, # (float) optimizer weight decay 5e-4",
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"warmup_epochs=${42:3.0}, # (float) warmup epochs (fractions ok)",
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"warmup_momentum=${43:0.8}, # (float) warmup initial momentum",
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"warmup_bias_lr=${44:0.1}, # (float) warmup initial bias lr",
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"box=${45:7.5}, # (float) box loss gain",
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"cls=${46:0.5}, # (float) cls loss gain (scale with pixels)",
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"dfl=${47:1.5}, # (float) dfl loss gain",
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"pose=${48:12.0}, # (float) pose loss gain",
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"kobj=${49:1.0}, # (float) keypoint obj loss gain",
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"label_smoothing=${50:0.0}, # (float) label smoothing (fraction)",
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"nbs=${51:64}, # (int) nominal batch size",
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"hsv_h=${52:0.015}, # (float) image HSV-Hue augmentation (fraction)",
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"hsv_s=${53:0.7}, # (float) image HSV-Saturation augmentation (fraction)",
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"hsv_v=${54:0.4}, # (float) image HSV-Value augmentation (fraction)",
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"degrees=${55:0.0}, # (float) image rotation (+/- deg)",
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"translate=${56:0.1}, # (float) image translation (+/- fraction)",
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"scale=${57:0.5}, # (float) image scale (+/- gain)",
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"shear=${58:0.0}, # (float) image shear (+/- deg)",
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"perspective=${59:0.0}, # (float) image perspective (+/- fraction), range 0-0.001",
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"flipud=${60:0.0}, # (float) image flip up-down (probability)",
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"fliplr=${61:0.5}, # (float) image flip left-right (probability)",
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"bgr=${62:0.0}, # (float) image channel BGR (probability)",
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"mosaic=${63:1.0}, # (float) image mosaic (probability)",
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"mixup=${64:0.0}, # (float) image mixup (probability)",
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"copy_paste=${65:0.0}, # (float) segment copy-paste (probability)",
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"auto_augment=\"${66|randaugment,autoaugment,augmix|}\", # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)",
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"erasing=${67:0.4}, # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.",
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"crop_fraction=${68:1.0}, # (float) image crop fraction for classification (0.1-1), 1.0 means no crop, must be greater than 0.",
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" data=${4:\"coco8.yaml\"}, # (str, optional) path to data file, i.e. coco8.yaml",
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" epochs=${5:100}, # (int) number of epochs to train for",
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" time=${6:None}, # (float, optional) number of hours to train for, overrides epochs if supplied",
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" patience=${7:100}, # (int) epochs to wait for no observable improvement for early stopping of training",
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" batch=${8:16}, # (int) number of images per batch (-1 for AutoBatch)",
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" imgsz=${9:640}, # (int | list) input images size as int for train and val modes, or list[w,h] for predict and export modes",
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" save=${10:True}, # (bool) save train checkpoints and predict results",
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" save_period=${11:-1}, # (int) Save checkpoint every x epochs (disabled if < 1)",
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" cache=${12:False}, # (bool) True/ram, disk or False. Use cache for data loading",
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" device=${13:None}, # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu",
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" workers=${14:8}, # (int) number of worker threads for data loading (per RANK if DDP)",
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" project=${15:None}, # (str, optional) project name",
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" name=${16:None}, # (str, optional) experiment name, results saved to 'project/name' directory",
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" exist_ok=${17:False}, # (bool) whether to overwrite existing experiment",
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" val=${18:True}, # (bool) validate/test during training",
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" pretrained=${19:True}, # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)",
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" optimizer=\"${20|SGD,Adam,Adamax,AdamW,NAdam,RAdam,RMSProp,auto|}\", # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]",
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" verbose=${21:True}, # (bool) whether to print verbose output",
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" seed=${22:0}, # (int) random seed for reproducibility",
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" deterministic=${23:True}, # (bool) whether to enable deterministic mode",
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" single_cls=${24:False}, # (bool) train multi-class data as single-class",
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" rect=${25:False}, # (bool) rectangular training if mode='train' or rectangular validation if mode='val'",
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" cos_lr=${26:False}, # (bool) use cosine learning rate scheduler",
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" close_mosaic=${27:10}, # (int) disable mosaic augmentation for final epochs (0 to disable)",
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" resume=${28:False}, # (bool) resume training from last checkpoint",
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" amp=${29:True}, # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check",
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" fraction=${30:1.0}, # (float) dataset fraction to train on (default is 1.0, all images in train set)",
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" profile=${31:False}, # (bool) profile ONNX and TensorRT speeds during training for loggers",
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" freeze=${32:None}, # (int | list, optional) freeze first n layers, or freeze list of layer indices during training",
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" multi_scale=${33:False}, # (bool) Whether to use multiscale during training",
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" plots=${34:True}, # (bool) save plots and images during train/val",
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" # Segmentation",
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" overlap_mask=${35:True}, # (bool) masks should overlap during training (segment train only)",
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" mask_ratio=${36:4}, # (int) mask downsample ratio (segment train only)",
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" # Classification",
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" dropout=${37:0.0}, # (float) use dropout regularization (classify train only)",
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" # Hyperparameters",
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" lr0=${38:0.01}, # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)",
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" lrf=${39:0.01}, # (float) final learning rate (lr0 * lrf)",
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" momentum=${40:0.937}, # (float) SGD momentum/Adam beta1",
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" weight_decay=${41:0.0005}, # (float) optimizer weight decay 5e-4",
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" warmup_epochs=${42:3.0}, # (float) warmup epochs (fractions ok)",
250+
" warmup_momentum=${43:0.8}, # (float) warmup initial momentum",
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" warmup_bias_lr=${44:0.1}, # (float) warmup initial bias lr",
252+
" box=${45:7.5}, # (float) box loss gain",
253+
" cls=${46:0.5}, # (float) cls loss gain (scale with pixels)",
254+
" dfl=${47:1.5}, # (float) dfl loss gain",
255+
" pose=${48:12.0}, # (float) pose loss gain",
256+
" kobj=${49:1.0}, # (float) keypoint obj loss gain",
257+
" label_smoothing=${50:0.0}, # (float) label smoothing (fraction)",
258+
" nbs=${51:64}, # (int) nominal batch size",
259+
" hsv_h=${52:0.015}, # (float) image HSV-Hue augmentation (fraction)",
260+
" hsv_s=${53:0.7}, # (float) image HSV-Saturation augmentation (fraction)",
261+
" hsv_v=${54:0.4}, # (float) image HSV-Value augmentation (fraction)",
262+
" degrees=${55:0.0}, # (float) image rotation (+/- deg)",
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" translate=${56:0.1}, # (float) image translation (+/- fraction)",
264+
" scale=${57:0.5}, # (float) image scale (+/- gain)",
265+
" shear=${58:0.0}, # (float) image shear (+/- deg)",
266+
" perspective=${59:0.0}, # (float) image perspective (+/- fraction), range 0-0.001",
267+
" flipud=${60:0.0}, # (float) image flip up-down (probability)",
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" fliplr=${61:0.5}, # (float) image flip left-right (probability)",
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" bgr=${62:0.0}, # (float) image channel BGR (probability)",
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" mosaic=${63:1.0}, # (float) image mosaic (probability)",
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" mixup=${64:0.0}, # (float) image mixup (probability)",
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" copy_paste=${65:0.0}, # (float) segment copy-paste (probability)",
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" auto_augment=\"${66|randaugment,autoaugment,augmix|}\", # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)",
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" erasing=${67:0.4}, # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.",
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" crop_fraction=${68:1.0}, # (float) image crop fraction for classification (0.1-1), 1.0 means no crop, must be greater than 0.",
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")",
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"# reference https://docs.ultralytics.com/modes/predict/"
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],

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