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--- |
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license: apache-2.0 |
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tags: |
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- yolo |
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- object-detection |
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- cargo |
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- packages |
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- forklift |
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- truck |
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datasets: |
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- custom-cargo-package-dataset |
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model-index: |
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- name: YOLOv8 Cargo Package Counter |
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results: |
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- task: |
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type: object-detection |
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dataset: |
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name: custom-cargo-package-dataset |
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type: object-detection |
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split: train |
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metrics: |
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- type: precision |
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value: 0.77187 |
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- type: recall |
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value: 0.11111 |
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- type: mAP50 |
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value: 0.09188 |
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- type: mAP50-95 |
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value: 0.06383 |
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- type: F1 |
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value: 0.19426 |
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language: |
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- en |
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base_model: YOLOv8 |
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pipeline_tag: object-detection |
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metrics: |
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- precision |
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- recall |
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- f1 |
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--- |
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# YOLOv8 Cargo Package Counter |
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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. |
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## Model Description |
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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. |
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- **Model Architecture**: YOLOv8 |
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- **Number of Classes**: 1 (`cargo-package`) |
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- **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. |
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## Evaluation Results |
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The model was evaluated on the validation set using the following metrics: |
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| Metric | Value | |
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| ------------- | ------- | |
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| Precision | 0.77187 | |
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| Recall | 0.11111 | |
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| mAP50 | 0.09188 | |
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| mAP50-95 | 0.06383 | |
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| F1 Score | 0.19426 | |
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These metrics were obtained using a threshold of 0.5 for IoU (Intersection over Union). |
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## How to Use |
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You can load the model using the `ultralytics` library, as shown below: |
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```python |
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from ultralytics import YOLO |
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# Load the model from Hugging Face |
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model = YOLO('https://huggingface.co/poudel/yolov8-cargo-package-counter/resolve/main/best.pt') |
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# Run inference on an image |
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results = model('path_to_image.jpg') |
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# Display the results |
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results.show() |
