（YOLOv5）物体検出でエラー「 Label class 1 exceeds nc=1 in data.yaml. Possible class labels are 0-0」

yolov5物体検出をしています。
スナックエンドウの画像を使って学習実行の項目くでつまづいてしまいました。

私はスナックエンドウではなく、キャベツで物体検出しています。
エラー結果の意味がいまいち分からず、何かアドバイスいただけると嬉しいです。

使用するtrain.pyのプログラムが長いのでリンク先で確認していただけたらと思います。
プログラムの確認先

実行結果とエラー
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(yolov5) C:\Users\isl\Desktop\yolov5>python train.py --data data.yaml --cfg yolov5m.yaml --weights '' --batch-size 8 --epochs 100 train: weights='', cfg=yolov5m.yaml, data=data.yaml, hyp=data\hyps\hyp.scratch-low.yaml, epochs=100, batch_size=8, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest github: skipping check (not a git repository), for updates see https://github.com/ultralytics/yolov5 YOLOv5 2022-10-19 Python-3.8.13 torch-1.7.1+cu110 CUDA:0 (NVIDIA GeForce GTX 1050 Ti, 4096MiB) hyperparameters: 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=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0 ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 in ClearML Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 runs in Comet TensorBoard: Start with 'tensorboard --logdir runs\train', view at http://localhost:6006/ Overriding model.yaml nc=80 with nc=1 from n params module arguments 0 -1 1 5280 models.common.Conv [3, 48, 6, 2, 2] 1 -1 1 41664 models.common.Conv [48, 96, 3, 2] 2 -1 2 65280 models.common.C3 [96, 96, 2] 3 -1 1 166272 models.common.Conv [96, 192, 3, 2] 4 -1 4 444672 models.common.C3 [192, 192, 4] 5 -1 1 664320 models.common.Conv [192, 384, 3, 2] 6 -1 6 2512896 models.common.C3 [384, 384, 6] 7 -1 1 2655744 models.common.Conv [384, 768, 3, 2] 8 -1 2 4134912 models.common.C3 [768, 768, 2] 9 -1 1 1476864 models.common.SPPF [768, 768, 5] 10 -1 1 295680 models.common.Conv [768, 384, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 2 1182720 models.common.C3 [768, 384, 2, False] 14 -1 1 74112 models.common.Conv [384, 192, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 2 296448 models.common.C3 [384, 192, 2, False] 18 -1 1 332160 models.common.Conv [192, 192, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 2 1035264 models.common.C3 [384, 384, 2, False] 21 -1 1 1327872 models.common.Conv [384, 384, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 2 4134912 models.common.C3 [768, 768, 2, False] 24 [17, 20, 23] 1 24246 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [192, 384, 768]] YOLOv5m summary: 291 layers, 20871318 parameters, 20871318 gradients, 48.2 GFLOPs AMP: checks passed optimizer: SGD(lr=0.01) with parameter groups 79 weight(decay=0.0), 82 weight(decay=0.0005), 82 bias train: Scanning 'C:\Users\isl\Desktop\yolov5\data\Train\labels.cache' images and labels... 40 found, 0 missing, 0 empty Traceback (most recent call last): File "train.py", line 630, in <module> main(opt) File "train.py", line 524, in main train(opt.hyp, opt, device, callbacks) File "train.py", line 202, in train assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' AssertionError: Label class 1 exceeds nc=1 in data.yaml. Possible class labels are 0-0