Thanks for the input, will update my posting. However, it’s a custom dataset, not COCO. The images are annotated using CVAT . 2 Objects per view. Each object annotated with 4 keypoints., hence: kpt_shape: [4,3] . Yolo replaces the [17,3] with [4,3] and nc=2 when it initializes, see below.

The labels have 12 columns but Yolo expects 14, so it must be looking for an additional 2 columns. Any suggestions??

Here is one sample label txt file: 0 0.671668 0.586995 0.126533 0.0187935 0.608402 0.577598 2 0.694217 0.584489 2 0.734935 0.596391 2 0.711239 0.593663 2 5 0.535234 0.610418 0.0907717 0.0387717 0.489848 0.629804 2 0.523033 0.622391 2 0.554239 0.606946 2 0.58062 0.591033 2

This looks ok to me, but adds up to 12, not 14: see below: Class: 5 Bbox: 0.535234 0.610418 0. 0907717 0.0387717 4 Keypoints: 0.489848 0.629804 2 0.523033 0.622391 2 0.554239 0.606946 2 0.58062 0.591033 2

Here is the initialization report: using YAML: /content/yaml_files/modelR.yaml Ultralytics 8.3.15 🚀 Python-3.10.12 torch-2.4.1+cu121 CPU (Intel Xeon 2.20GHz) engine/trainer: task=pose, mode=train, model=yolov8n-pose.yaml, data=/content/yaml_files/modelR.yaml, epochs=2, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train4, exist_ok=False, pretrained=yolov8n-pose.pt, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/pose/train4 Overriding model.yaml kpt_shape=[17, 3] with kpt_shape=[4, 3] Overriding model.yaml nc=1 with nc=2