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--- |
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language: en |
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tags: |
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- object-detection |
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- dataset |
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- YOLO |
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- cattle |
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- agriculture |
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license: cc-by-4.0 |
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datasets: |
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- cattle-body-parts |
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model-index: |
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- name: YOLOv7X Cattle Body Parts Detection |
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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: Cattle Body Parts Dataset |
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type: custom |
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metrics: |
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- type: mAP |
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value: 0.996 |
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--- |
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# Cattle Body Parts Image Dataset for Object Detection |
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<div style="display: flex; gap: 10px; flex-wrap: wrap;"> |
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<img src="https://img.shields.io/github/license/AliKHaliliT/Cattle-Body-Parts-Dataset-for-Object-Detection" alt="License"> |
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<img src="https://img.shields.io/github/last-commit/AliKHaliliT/Cattle-Body-Parts-Dataset-for-Object-Detection" alt="Last Commit"> |
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<img src="https://img.shields.io/github/issues/AliKHaliliT/Cattle-Body-Parts-Dataset-for-Object-Detection" alt="Open Issues"> |
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</div> |
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<br/> |
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## Intro |
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This dataset is a curated collection of images featuring various cattle body parts aimed at facilitating object detection tasks. The dataset contains a total of 428 high-quality photos, meticulously annotated with three distinct classes: "Back," "Head," and "Leg." |
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The dataset can be downloaded using [this link](https://www.kaggle.com/datasets/alikhalilit98/cattle-body-parts-dataset-for-object-detection). The dataset is also available at Roboflow Universe. |
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<p align="center"> |
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<a href="https://universe.roboflow.com/ali-khalili/cattle-body-parts-dataset-for-object-detection"> |
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<img src="https://app.roboflow.com/images/download-dataset-badge.svg"></img> |
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</a> |
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</p> |
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A YOLOv7X model has been trained using the dataset and achieved a mAP of 99.6%. You can access the trained weights through [this link](https://huggingface.co/alikhalilit98/Cattle-Body-Parts-Dataset-for-Object-Detection/blob/main/yolov7_cattle_parts_final.pt). |
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<!-- |
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### Acquisition |
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The dataset creation involved the following steps: |
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- **Initial Data:** Images were collected and annotated to create a base dataset for training. |
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- **Model Training:** A [YOLOv7](https://github.com/WongKinYiu/yolov7) model was trained to recognize target objects in the annotated images. |
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- **Data Acquisition Script:** An automated script fetched videos from the internet. |
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- **Conversion and Filtering:** Videos were turned into frames; similar frames were filtered out using Cosine Similarity. |
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- **Object Detection:** The trained model identified objects in the new images. |
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- **Quality Check:** A comprehensive review ensured dataset accuracy and consistency. |
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--> |
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## Motivation |
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Accurate and reliable identification of different cattle body parts is crucial for various agricultural and veterinary applications. This dataset aims to provide a valuable resource for researchers, developers, and enthusiasts working on object detection tasks involving cattle, ultimately contributing to advancements in livestock management, health monitoring, and related fields. |
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## Data |
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### Overview |
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- Total Images: 428 |
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- Classes: Back, Head, Leg |
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- Annotations: Bounding boxes for each class |
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Below is an example image from the dataset. |
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<div align="center"> |
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<img src="util_resources/readme/sample.png"/> |
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</div> |
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### Contents |
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``` |
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📦 Cattle_Body_Parts_OD.zip |
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┣ 📂 images |
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┃ ┣ 📜 image1.jpg |
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┃ ┣ 📜 image2.jpg |
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┃ ┗ ... |
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┗ 📂 annotations |
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┣ 📜 image1.json |
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┣ 📜 image2.json |
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┗ ... |
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``` |
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### Annotation Format |
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Each annotation file corresponds to an image in the dataset and is formatted as per the [LabelMe](https://github.com/wkentaro/labelme) [JSON](https://www.json.org/json-en.html) standard. These annotations define the bounding box coordinates for each labeled body part, enabling straightforward integration into object detection pipelines. |
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## License |
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<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>. |
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## Disclaimer |
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This dataset has been collected from publicly available sources. I do not claim ownership of the data and have no intention of infringing on any copyright. The material contained in this dataset is copyrighted to their respective owners. I have made every effort to ensure the data is accurate and complete, but I cannot guarantee its accuracy or completeness. If you believe any data in this dataset infringes on your copyright, please get in touch with me immediately so I can take appropriate action. |
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