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YOLOv8 architecture updates from R&D branch (#88)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2022-12-24 18:10:44 +01:00
committed by GitHub
parent 5fbea25f0b
commit ebd3cfb2fd
23 changed files with 720 additions and 570 deletions
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251
ultralytics/yolo/v8/detect/train.py
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@ -5,9 +5,12 @@ import torch.nn as nn
from ultralytics.yolo import v8
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
from ultralytics.yolo.utils.metrics import FocalLoss, bbox_iou, smooth_BCE
from ultralytics.yolo.utils.loss import BboxLoss
from ultralytics.yolo.utils.metrics import smooth_BCE
from ultralytics.yolo.utils.modeling.tasks import DetectionModel
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
@ -35,10 +38,7 @@ class DetectionTrainer(BaseTrainer):
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"],
anchors=self.args.get("anchors"))
model = DetectionModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"])
if weights:
model.load(weights)
for _, v in model.named_parameters():
@ -46,150 +46,14 @@ class DetectionTrainer(BaseTrainer):
return model
def get_validator(self):
self.loss_names = 'box_loss', 'obj_loss', 'cls_loss'
self.loss_names = 'box_loss', 'cls_loss', 'dfl_loss'
return v8.detect.DetectionValidator(self.test_loader,
save_dir=self.save_dir,
logger=self.console,
args=self.args)
def criterion(self, preds, batch):
head = de_parallel(self.model).model[-1]
sort_obj_iou = False
autobalance = False
# init losses
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.args.cls_pw], device=self.device))
BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([self.args.obj_pw], device=self.device))
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
cp, cn = smooth_BCE(eps=self.args.label_smoothing) # positive, negative BCE targets
# Focal loss
g = self.args.fl_gamma
if self.args.fl_gamma > 0:
BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
balance = {3: [4.0, 1.0, 0.4]}.get(head.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
ssi = list(head.stride).index(16) if autobalance else 0 # stride 16 index
BCEcls, BCEobj, gr, autobalance = BCEcls, BCEobj, 1.0, autobalance
def build_targets(p, targets):
# Build targets for compute_loss(), input targets(image,class,x,y,w,h)
nonlocal head
na, nt = head.na, targets.shape[0] # number of anchors, targets
tcls, tbox, indices, anch = [], [], [], []
gain = torch.ones(7, device=self.device) # normalized to gridspace gain
ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt)
targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices
g = 0.5 # bias
off = torch.tensor(
[
[0, 0],
[1, 0],
[0, 1],
[-1, 0],
[0, -1], # j,k,l,m
# [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
],
device=self.device).float() * g # offsets
for i in range(head.nl):
anchors, shape = head.anchors[i], p[i].shape
gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
# Match targets to anchors
t = targets * gain # shape(3,n,7)
if nt:
# Matches
r = t[..., 4:6] / anchors[:, None] # wh ratio
j = torch.max(r, 1 / r).max(2)[0] < self.args.anchor_t # compare
# j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
t = t[j] # filter
# Offsets
gxy = t[:, 2:4] # grid xy
gxi = gain[[2, 3]] - gxy # inverse
j, k = ((gxy % 1 < g) & (gxy > 1)).T
l, m = ((gxi % 1 < g) & (gxi > 1)).T
j = torch.stack((torch.ones_like(j), j, k, l, m))
t = t.repeat((5, 1, 1))[j]
offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
else:
t = targets[0]
offsets = 0
# Define
bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
gij = (gxy - offsets).long()
gi, gj = gij.T # grid indices
# Append
indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
anch.append(anchors[a]) # anchors
tcls.append(c) # class
return tcls, tbox, indices, anch
if len(preds) == 2: # eval
_, p = preds
else: # len(3) train
p = preds
targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
targets = targets.to(self.device)
lcls = torch.zeros(1, device=self.device)
lbox = torch.zeros(1, device=self.device)
lobj = torch.zeros(1, device=self.device)
tcls, tbox, indices, anchors = build_targets(p, targets)
# Losses
for i, pi in enumerate(p): # layer index, layer predictions
b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
bs = tobj.shape[0]
n = b.shape[0] # number of targets
if n:
pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, head.nc), 1) # subset of predictions
# Box regression
pxy = pxy.sigmoid() * 2 - 0.5
pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
pbox = torch.cat((pxy, pwh), 1) # predicted box
iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
lbox += (1.0 - iou).mean() # iou loss
# Objectness
iou = iou.detach().clamp(0).type(tobj.dtype)
if sort_obj_iou:
j = iou.argsort()
b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
if gr < 1:
iou = (1.0 - gr) + gr * iou
tobj[b, a, gj, gi] = iou # iou ratio
# Classification
if head.nc > 1: # cls loss (only if multiple classes)
t = torch.full_like(pcls, cn, device=self.device) # targets
t[range(n), tcls[i]] = cp
lcls += BCEcls(pcls, t) # BCE
obji = BCEobj(pi[..., 4], tobj)
lobj += obji * balance[i] # obj loss
if autobalance:
balance[i] = balance[i] * 0.9999 + 0.0001 / obji.detach().item()
if autobalance:
balance = [x / balance[ssi] for x in balance]
lbox *= self.args.box
lobj *= self.args.obj
lcls *= self.args.cls
loss = lbox + lobj + lcls
return loss * bs, torch.cat((lbox, lobj, lcls)).detach()
return Loss(self.model)(preds, batch)
def label_loss_items(self, loss_items=None, prefix="train"):
# We should just use named tensors here in future
@ -212,10 +76,105 @@ class DetectionTrainer(BaseTrainer):
plot_results(file=self.csv) # save results.png
# Criterion class for computing training losses
class Loss:
def __init__(self, model):
device = next(model.parameters()).device # get model device
h = model.args # hyperparameters
# Define criteria
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device), reduction='none')
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets
m = de_parallel(model).model[-1] # Detect() module
self.BCEcls = BCEcls
self.hyp = h
self.stride = m.stride # model strides
self.nc = m.nc # number of classes
self.nl = m.nl # number of layers
self.device = device
self.use_dfl = m.reg_max > 1
self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
def preprocess(self, targets, batch_size, scale_tensor):
if targets.shape[0] == 0:
out = torch.zeros(batch_size, 0, 5, device=self.device)
else:
i = targets[:, 0] # image index
_, counts = i.unique(return_counts=True)
out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
for j in range(batch_size):
matches = i == j
n = matches.sum()
if n:
out[j, :n] = targets[matches, 1:]
out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
return out
def bbox_decode(self, anchor_points, pred_dist):
if self.use_dfl:
b, a, c = pred_dist.shape # batch, anchors, channels
pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
# pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2)
return dist2bbox(pred_dist, anchor_points, xywh=False)
def __call__(self, preds, batch):
loss = torch.zeros(3, device=self.device) # box, cls, dfl
feats, pred_distri, pred_scores = preds if len(preds) == 3 else preds[1]
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
dtype = pred_scores.dtype
batch_size, grid_size = pred_scores.shape[:2]
imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
# targets
targets = torch.cat((batch["batch_idx"].view(-1, 1), batch["cls"].view(-1, 1), batch["bboxes"]), 1)
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
# pboxes
pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
target_labels, target_bboxes, target_scores, fg_mask = self.assigner(
pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
target_bboxes /= stride_tensor
target_scores_sum = target_scores.sum()
# cls loss
# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
loss[1] = self.BCEcls(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
# bbox loss
if fg_mask.sum():
loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
target_scores_sum, fg_mask)
loss[0] *= 7.5 # box gain
loss[1] *= 0.5 # cls gain
loss[2] *= 1.5 # dfl gain
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
def train(cfg):
cfg.model = cfg.model or "models/yolov5n.yaml"
cfg.model = cfg.model or "models/yolov8n.yaml"
cfg.data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist")
cfg.imgsz = 160
cfg.epochs = 5
trainer = DetectionTrainer(cfg)
trainer.train()
@ -223,9 +182,9 @@ def train(cfg):
if __name__ == "__main__":
"""
CLI usage:
python ultralytics/yolo/v8/detect/train.py model=yolov5n.yaml data=coco128 epochs=100 imgsz=640
python ultralytics/yolo/v8/detect/train.py model=yolov8n.yaml data=coco128 epochs=100 imgsz=640
TODO:
yolo task=detect mode=train model=yolov5n.yaml data=coco128.yaml epochs=100
yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
"""
train()

1
ultralytics/yolo/v8/detect/val.py
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@ -3,7 +3,6 @@ import os
import hydra
import numpy as np
import torch
import torch.nn.functional as F
from ultralytics.yolo.data import build_dataloader
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG

48
ultralytics/yolo/v8/models/yolov5n-seg.yaml
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@ -1,48 +0,0 @@
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.25 # layer channel multiple
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5)
]

48
ultralytics/yolo/v8/models/yolov5n.yaml
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@ -1,48 +0,0 @@
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.25 # layer channel multiple
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]

43
ultralytics/yolo/v8/models/yolov8n-seg.yaml Normal file
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@ -0,0 +1,43 @@
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.25 # layer channel multiple
anchors: [[16,19], [55,65], [178,192]]
# YOLOv8n v0.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C2f, [128, True]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C2f, [256, True]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 6, C2f, [512, True]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C2f, [1024, True]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOv8n v0.0 head
head:
[[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C2f, [512]], # 13
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C2f, [256]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 12], 1, Concat, [1]], # cat head P4
[-1, 3, C2f, [512]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 9], 1, Concat, [1]], # cat head P5
[-1, 3, C2f, [1024]], # 23 (P5/32-large)
[[15, 18, 21], 1, Segment, [nc, 32, 256]], # Detect(P3, P4, P5)
]

42
ultralytics/yolo/v8/models/yolov8n.yaml Normal file
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@ -0,0 +1,42 @@
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.25 # layer channel multiple
# YOLOv8.0n backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C2f, [128, True]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C2f, [256, True]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 6, C2f, [512, True]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C2f, [1024, True]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOv8.0n head
head:
[[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C2f, [512]], # 13
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C2f, [256]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 12], 1, Concat, [1]], # cat head P4
[-1, 3, C2f, [512]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 9], 1, Concat, [1]], # cat head P5
[-1, 3, C2f, [1024]], # 23 (P5/32-large)
[[15, 18, 21], 1, Detect, [nc]], # Detect(P3, P4, P5)
]

11
ultralytics/yolo/v8/segment/train.py
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@ -18,10 +18,7 @@ from ..detect import DetectionTrainer
class SegmentationTrainer(DetectionTrainer):
def load_model(self, model_cfg=None, weights=None):
model = SegmentationModel(model_cfg or weights["model"].yaml,
ch=3,
nc=self.data["nc"],
anchors=self.args.get("anchors"))
model = SegmentationModel(model_cfg or weights["model"].yaml, ch=3, nc=self.data["nc"])
if weights:
model.load(weights)
for _, v in model.named_parameters():
@ -29,7 +26,7 @@ class SegmentationTrainer(DetectionTrainer):
return model
def get_validator(self):
self.loss_names = 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss'
self.loss_names = 'box_loss', 'seg_loss', 'cls_loss', 'dfl_loss'
return v8.segment.SegmentationValidator(self.test_loader,
save_dir=self.save_dir,
logger=self.console,
@ -235,7 +232,7 @@ class SegmentationTrainer(DetectionTrainer):
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
def train(cfg):
cfg.model = cfg.model or "models/yolov5n-seg.yaml"
cfg.model = cfg.model or "models/yolov8n-seg.yaml"
cfg.data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")
trainer = SegmentationTrainer(cfg)
trainer.train()
@ -244,7 +241,7 @@ def train(cfg):
if __name__ == "__main__":
"""
CLI usage:
python ultralytics/yolo/v8/segment/train.py model=yolov5n-seg.yaml data=coco128-segments epochs=100 imgsz=640
python ultralytics/yolo/v8/segment/train.py model=yolov8n-seg.yaml data=coco128-segments epochs=100 imgsz=640
TODO:
Direct cli support, i.e, yolov8 classify_train args.epochs 10