Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Invalid string class label CHIP-Segmentation@8c5696fb656145ab08cc748bbc7a4b27c435e078
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
example_data = self.str2int(example_data)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
output = [self._strval2int(value) for value in values]
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
raise ValueError(f"Invalid string class label {value}")
ValueError: Invalid string class label CHIP-Segmentation@8c5696fb656145ab08cc748bbc7a4b27c435e078Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CHIP Dataset - Segmentation Training Guide
Overview
This dataset is designed for training segmentation models, specifically for human/CHIP segmentation tasks using YOLO models.
Dataset Structure
After extracting CHIP_dataset.zip, the directory structure should include:
train/andvalid/folders with imagesdata.yamlconfiguration file for YOLO training
Prerequisites
- Python environment
- GPU with CUDA support (recommended)
Installation
pip install ultralytics tqdm
Download and Extract Dataset
# Download dataset
wget https://huggingface.co/datasets/OSAS-AI/CHIP-Segmentation/resolve/main/CHIP_dataset.zip
# Extract
from zipfile import ZipFile
from tqdm import tqdm
import os
zip_path = "CHIP_dataset.zip"
extract_dir = "."
os.makedirs(extract_dir, exist_ok=True)
with ZipFile(zip_path, "r") as zip_ref:
members = zip_ref.infolist()
for member in tqdm(members, desc="Extracting", unit="file"):
zip_ref.extract(member, extract_dir)
print(f"Extracted to: {extract_dir}")
Download Pre-trained Model
wget https://huggingface.co/Ultralytics/YOLO26/resolve/main/yolo26s-seg.pt
Training
from ultralytics import YOLO
# Load model
model = YOLO('yolo26s-seg.pt') # small variant – good balance
# Train
results = model.train(
data='data.yaml',
epochs=300,
imgsz=640,
batch=32, # reduce if OOM
device="0,1", # adjust for your GPUs
workers=8,
project='runs/train',
name='human_segmentation',
exist_ok=False,
patience=15,
save=True,
val=True,
plots=True
)
Key Training Parameters
- Model: YOLOv26s-seg (segmentation)
- Image Size: 640x640
- Epochs: 300
- Batch Size: 32 (adjust based on VRAM)
- Multi-GPU: Supports
device="0,1"
Inference / Usage
After training, the best model will be saved in runs/train/human_segmentation/weights/best.pt
model = YOLO('path/to/best.pt')
results = model('path/to/image.jpg')
Dataset on Hugging Face
For more details, refer to Ultralytics YOLO documentation.
You can copy the entire block above and save it as README.md. Let me know if you want any changes!
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