---
license: apache-2.0
tags:
- yolo
- object-detection
- cargo
- packages
- forklift
- truck
datasets:
- custom-cargo-package-dataset
model-index:
- name: YOLOv8 Cargo Package Counter
  results:
  - task:
      type: object-detection
    dataset:
      name: custom-cargo-package-dataset
      type: object-detection
      split: train
    metrics:
    - type: precision
      value: 0.77187
    - type: recall
      value: 0.11111
    - type: mAP50
      value: 0.09188
    - type: mAP50-95
      value: 0.06383
    - type: F1
      value: 0.19426
language:
- en
base_model: YOLOv8
pipeline_tag: object-detection
metrics:
- precision
- recall
- f1
---

# YOLOv8 Cargo Package Counter

This repository contains a YOLOv8-based model trained to detect and count cargo packages in images. The model was trained on a custom dataset with classe: `cargo-package`. It can be used for various cargo logistics and package counting tasks.

## Model Description

YOLOv8 is a state-of-the-art object detection architecture, known for its speed and accuracy. This model was trained using a custom dataset containing images of cargo packages, forklifts, and trucks, making it specialized for logistics and transportation industries.

- **Model Architecture**: YOLOv8
- **Number of Classes**: 1 (`cargo-package`)
- **Training**: The model was trained using both `best.pt` (the best performing model during training) and `last.pt` (the final checkpoint).- **Use Case**: Object detection and counting of cargo packages, forklifts, and trucks in warehouses, transportation hubs, or logistics centers.

## Evaluation Results

The model was evaluated on the validation set using the following metrics:

| Metric        | Value   |
| ------------- | ------- |
| Precision     | 0.77187 |
| Recall        | 0.11111 |
| mAP50         | 0.09188 |
| mAP50-95      | 0.06383 |
| F1 Score      | 0.19426 |

These metrics were obtained using a threshold of 0.5 for IoU (Intersection over Union).

## How to Use

You can load the model using the `ultralytics` library, as shown below:

```python
from ultralytics import YOLO

# Load the model from Hugging Face
model = YOLO('https://huggingface.co./poudel/yolov8-cargo-package-counter/resolve/main/best.pt')

# Run inference on an image
results = model('path_to_image.jpg')

# Display the results
results.show()