pythonobject-detectionyolotraining-datayolov8

YOLOv8 no prediction results

Get interested in yolov8 and after few youtube tutorials i tried to train custom dataset. After all manipulations i got no prediction results :( 2nd image - val_batch0_labels, 3rd image - val_batch0_pred

that's a results val_batch0_labels val_batch0_pred

I tried to do this in pycharm and google colab (same results) and here's the code:

# main.py
from ultralytics import YOLO


model = YOLO("yolov8n.yaml")

results = model.train(data="config.yaml", epochs=1)

# config.yaml
path: Y:\coding\python\yolo_test\data_airplane\data # dataset root dir
train: images  # train images (relative to 'path')
val: images  # val images (relative to 'path')

# Classes
names:
  0: airplane

And for more information here's all what i get after run this:

              from  n    params  module                                       arguments

0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 897664 ultralytics.nn.modules.head.Detect [80, [64, 128, 256]]
YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients

Ultralytics YOLOv8.0.141 Python-3.10.8 torch-2.0.1+cpu CPU (Intel Core(TM) i3-10105F 3.70GHz) engine\trainer: task=detect, mode=train, model=yolov8n.yaml, data=config.yaml, epochs=1, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, 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, 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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, 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, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs\detect\train22 Overriding model.yaml nc=80 with nc=1

               from  n    params  module                                       arguments

0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 751507 ultralytics.nn.modules.head.Detect [1, [64, 128, 256]]
YOLOv8n summary: 225 layers, 3011043 parameters, 3011027 gradients

train: Scanning Y:\coding\python\yolo_test\data_airplane\data\labels.cache... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00<?, ?it/s] val: Scanning Y:\coding\python\yolo_test\data_airplane\data\labels.cache... 3 images, 0 backgrounds, 0 corrupt: 100%|██████████| 3/3 [00:00<?, ?it/s] Plotting labels to runs\detect\train22\labels.jpg... optimizer: AdamW(lr=0.002, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0) Image sizes 640 train, 640 val Using 0 dataloader workers Logging results to runs\detect\train22 Starting training for 1 epochs..

  Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
    1/1         0G      3.156      3.899      4.605         33        640: 100%|██████████| 1/1 [00:02<00:00,  2.21s/it]
             Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:01<00:00,  1.14s/it]
               all          3         16    0.00111     0.0625   0.000607   6.07e-05

1 epochs completed in 0.001 hours. Optimizer stripped from runs\detect\train22\weights\last.pt, 6.2MB Optimizer stripped from runs\detect\train22\weights\best.pt, 6.2MB

Validating runs\detect\train22\weights\best.pt... Ultralytics YOLOv8.0.141 Python-3.10.8 torch-2.0.1+cpu CPU (Intel Core(TM) i3-10105F 3.70GHz) YOLOv8n summary (fused): 168 layers, 3005843 parameters, 0 gradients Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 1/1 [00:01<00:00, 1.08s/it] all 3 16 0.00114 0.0625 0.00086 8.6e-05 Speed: 2.0ms preprocess, 123.7ms inference, 0.0ms loss, 1.7ms postprocess per image Results saved to runs\detect\train22

Process finished with exit code 0

Solution

  • It is ok to get no predictions after 1 epoch of training from scratch on 3 images. The model has not learned relevant features from the data to correctly detect airplanes, so it doesn't recognize them. If you want fast results, start your training from a pre-trained model model = YOLO("yolov8n.pt"), it has already learned a lot of useful features from a large dataset and a long-term training process.

    Also, you may need to increase the number of epochs (in this case you will see more meaningful 'results' charts), and the number of training data (in this case your model may learn something useful and not only remembers your dataset).

    Check these tips for best training results: https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/?h=tips