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---
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() |