data lable wrong when train yoloworld about ultralytics

[github-actions]

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Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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[ccl-private]

the label in the train batch：

the label in text:

[glenn-jocher]

@ccl-private hello,

Thank you for bringing this to our attention. It looks like there might be an issue with the labeling consistency in your training data.

To help us investigate further, could you please ensure the following:

1. Reproducible Example: You've provided a good start, but please confirm that this issue persists with the latest versions of torch and ultralytics. If not, please upgrade your packages and try again.
2. Data Consistency: Ensure that your dataset annotations are consistent across all sources. Mixed datasets can sometimes lead to label mismatches.

Here's a quick checklist to verify your setup:

• Ensure that all datasets (Objects365, Flickr30k, GQA, and LVIS) have consistent label formats.
• Double-check the json_file paths and contents to ensure they align correctly with the image paths.

If the issue persists, please provide a minimal reproducible example that includes a small subset of your data, so we can replicate the problem on our end. You can find more details on creating a minimal reproducible example here.

Looking forward to your response!

[ccl-private]

task: detect
mode: train
model: yolov8s-worldv2.pt
data:
train:
yolo_data:
- VisDrone.yaml
val:
yolo_data:
- lvis.yaml
epochs: 100
time: null
patience: 100
batch: 128
imgsz: 640
save: true
save_period: -1
cache: false
device:

• 0
• 1
  workers: 8
  project: null
  name: train11
  exist_ok: false
  pretrained: true
  optimizer: Adam
  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: 19
  multi_scale: false
  overlap_mask: true
  mask_ratio: 4
  dropout: 0.0
  val: true
  split: val
  save_json: false
  save_hybrid: false
  conf: null
  iou: 0.7
  max_det: 300
  half: false
  dnn: false
  plots: true
  source: null
  vid_stride: 1
  stream_buffer: false
  visualize: false
  augment: false
  agnostic_nms: false
  classes: null
  retina_masks: false
  embed: null
  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: null
  format: torchscript
  keras: false
  optimize: false
  int8: false
  dynamic: false
  simplify: false
  opset: null
  workspace: 4
  nms: false
  lr0: 0.0001
  lrf: 0.1
  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
  auto_augment: randaugment
  erasing: 0.4
  crop_fraction: 1.0
  cfg: null
  tracker: botsort.yaml
  save_dir: runs/detect/train11

[ccl-private]

I only use lvis.yaml and VisDrone.yaml to test the train batch label.


car with different label occurs.

[ccl-private]

the dataset label of VisDrone.yaml should be right, because I have trained a yolov8n on it , and get a map of 0.4.

[ccl-private]

Environment
pip install ultralytics

Minimal Reproducible Example
from ultralytics import YOLOWorld
from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1"

data = dict(
train=dict(
yolo_data=["VisDrone.yaml"],
),
val=dict(yolo_data=["lvis.yaml"]),
)

model = YOLOWorld("yolov8s-worldv2.pt")
model.train(data=data, batch=128, epochs=100, trainer=WorldTrainerFromScratch, device=[0, 1],
optimizer="Adam", lr0=0.0001, lrf=0.1, freeze=19) # cache='disk'