yolov8-crowdhuman

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yolov8-crowdhuman

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YOLOv8 trained on CrowdHuman dataset and supports ONNX Runtime Inference for Image, Video and Webcam

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# YOLOv8 Human Detection

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Human/person detection using YOLOv8 trained on CrowdHuman dataset with ONNX Runtime inference.

## Features

- CrowdHuman to YOLO format conversion
- YOLOv8n training pipeline
- ONNX Runtime GPU (CUDA) inference
- TorchVision NMS for real-time performance
- Support for image, video, and webcam

## Installation

### Clone the Repository

```bash
git clone https://github.com/yakhyo/yolov8-crowdhuman.git
cd yolov8-crowdhuman
```

### Install Required Packages

```bash
pip install -r requirements.txt
```

**Note:** Requires CUDA for GPU acceleration. For CPU-only, replace `onnxruntime-gpu` with `onnxruntime` in `requirements.txt`.

## Dataset Preparation

Convert CrowdHuman annotations to YOLO format:

```bash
python utils/crowdhuman2yolo.py
```

Edit the script to specify your paths:
```python
convert_odgt_to_yolo("annotation_val.odgt", "data/val/images", "data/val/labels")
```

Expected dataset structure:
```
CrowdHuman/
├── data/
│   ├── train/
│   │   ├── images/
│   │   └── labels/
│   └── val/
│       ├── images/
│       └── labels/
└── dataset.yaml
```

## Training

Train YOLOv8n on CrowdHuman:

```python
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
model.train(
    data='dataset.yaml',
    epochs=100,
    imgsz=640,
    batch=16,
    name='yolov8n-person',
    project='runs/train'
)
```

Export to ONNX:

```python
from ultralytics import YOLO

model = YOLO("runs/train/yolov8n-person/weights/best.pt")
model.export(format="onnx", simplify=True, dynamic=True)
```

## Training Results

Training curves and metrics (100 epochs on CrowdHuman):

Precision-Recall Curve:

Confusion Matrix:

Sample Predictions:

## Weights

Pre-trained weights are available in the `weights/` directory:

| Model | PyTorch | ONNX |
|-------|---------|------|
| YOLOv8n-person (best) | [yolov8n_best.pt](https://github.com/yakhyo/yolov8-crowdhuman/releases/download/weights/yolov8n_best.pt) | [yolov8n_best.onnx](https://github.com/yakhyo/yolov8-crowdhuman/releases/download/weights/yolov8n_best.onnx) |
| YOLOv8n-person (last) | [yolov8n_last.pt](https://github.com/yakhyo/yolov8-crowdhuman/releases/download/weights/yolov8n_last.pt) | [yolov8n_last.onnx](https://github.com/yakhyo/yolov8-crowdhuman/releases/download/weights/yolov8n_last.onnx) |

## Usage

```bash
# Webcam (display auto-enabled)
python inference.py --weights weights/yolov8n_best.onnx --source 0

# Image
python inference.py --weights weights/yolov8n_best.onnx --source assets/test.jpg --save-img --view-img

# Video
python inference.py --weights weights/yolov8n_best.onnx --source video.mp4 --save-img --view-img
```

### Arguments

```
python inference.py -h
usage: inference.py [-h] [--weights WEIGHTS] [--source SOURCE] [--conf-thres CONF_THRES] [--iou-thres IOU_THRES] [--max-det MAX_DET] [--save-img] [--view-img]

YOLOv8 Human Detection ONNX Inference

options:
  -h, --help            show this help message and exit
  --weights WEIGHTS     Path to ONNX model file
  --source SOURCE       Path to image/video file or webcam index
  --conf-thres CONF_THRES
                        Confidence threshold
  --iou-thres IOU_THRES
                        NMS IoU threshold
  --max-det MAX_DET     Maximum detections per image
  --save-img            Save detected images
  --view-img            Display results (auto-enabled for webcam)
```

## Output

The model returns detections as numpy arrays:

- **boxes**: `[N, 4]` bounding boxes in (x1, y1, x2, y2) format - `numpy.float32`
- **scores**: `[N]` confidence scores - `numpy.float32`
- **class_ids**: `[N]` class indices - `numpy.int32`

## Reference

- **Ultralytics**: [ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)
- **CrowdHuman**: [CrowdHuman Dataset](https://www.crowdhuman.org/)

yolov8-crowdhuman

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