YOLOv10 - Pascal Visual Object Classes (VOC) Vanilla
YOLOv10 model fine-tuned on Pascal VOC dataset to mitigate hallucination on out-of-distribution data for improved general object detection performance.
Model Details
- Model Type: YOLOv10 Object Detection
- Dataset: Pascal Visual Object Classes (VOC)
- Training Method: fine-tuned to mitigate hallucination on out-of-distribution data
- Framework: PyTorch/Ultralytics
- Task: Object Detection
Dataset Information
This model was trained on the Pascal Visual Object Classes (VOC) dataset, which contains the following object classes:
aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, tvmonitor
Dataset-specific Details:
Pascal Visual Object Classes (VOC) Dataset:
- Standard benchmark dataset for object detection
- Contains 20 object classes representing common objects
- Widely used for evaluating computer vision models
- High-quality annotations with precise bounding boxes
Usage
This model can be used with the Ultralytics YOLOv10 framework:
1from ultralytics import YOLO
2
3# Load the model
4model = YOLO('path/to/best.pt')
5
6# Run inference
7results = model('path/to/image.jpg')
8
9# Process results
10for result in results:
11 boxes = result.boxes.xyxy # bounding boxes
12 scores = result.boxes.conf # confidence scores
13 classes = result.boxes.cls # class predictions
Model Performance
This model was fine-tuned to mitigate hallucination on out-of-distribution data on the Pascal Visual Object Classes (VOC) dataset using YOLOv10 architecture.
Fine-tuning Objective: This model was specifically fine-tuned to mitigate hallucination on out-of-distribution (OOD) data, improving robustness when encountering images that differ from the training distribution.
Intended Use
- Primary Use: Object detection in general computer vision applications
- Suitable for: Research, development, and deployment of object detection systems
- Limitations: Performance may vary on images significantly different from the training distribution
Citation
If you use this model, please cite:
1@article{yolov10,
2 title={YOLOv10: Real-Time End-to-End Object Detection},
3 author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
4 journal={arXiv preprint arXiv:2405.14458},
5 year={2024}
6}
License
This model is released under the MIT License.
Keywords
YOLOv10, Object Detection, Computer Vision, Pascal-VOC, Autonomous Driving, Deep Learning
