| --- |
| license: apache-2.0 |
| tags: |
| - segmentation |
| size_categories: |
| - 10K<n<100K |
| --- |
| # 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/` and `valid/` folders with images |
| - `data.yaml` configuration file for YOLO training |
|
|
| ## Prerequisites |
| - Python environment |
| - GPU with CUDA support (recommended) |
|
|
| ## Installation |
| ```bash |
| pip install ultralytics tqdm |
| ``` |
|
|
| ## Download and Extract Dataset |
| ```bash |
| # 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 |
| ```bash |
| wget https://huggingface.co/Ultralytics/YOLO26/resolve/main/yolo26s-seg.pt |
| ``` |
|
|
| ## Training |
| ```python |
| 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` |
|
|
| ```python |
| model = YOLO('path/to/best.pt') |
| results = model('path/to/image.jpg') |
| ``` |
|
|
| ## Dataset on Hugging Face |
| [CHIP-Segmentation](https://huggingface.co/datasets/OSAS-AI/CHIP-Segmentation) |
|
|
| For more details, refer to [Ultralytics YOLO documentation](https://docs.ultralytics.com/). |
|
|
|
|
| You can copy the entire block above and save it as `README.md`. Let me know if you want any changes! |
