Downloads · 30 days
0
yolocar
wuhp/yolocar
yolocar is a machine learning model from wuhp. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Architecture: YOLOv11 Training Epochs: 75 Batch Size: 32 Optimizer: auto Learning Rate: 0.0005 Data Augmentation Level: Moderate
Access
Public
Updated Jan 28, 2025
Repo size
5.5 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt5.5 MB · 100%
At a glance
- Access
- Public
- Created
- Jan 28, 2025
- Updated
- Jan 28, 2025
- SHA
- 2c0d74cd
Model card
From the Hugging Face model README
car-75e-11n
Model Overview
Architecture: YOLOv11
Training Epochs: 75
Batch Size: 32
Optimizer: auto
Learning Rate: 0.0005
Data Augmentation Level: Moderate
Training Metrics
- [email protected]: 0.88072
Class IDs
| Class ID | Class Name |
|---|---|
| 0 | Vehicle |
Datasets Used
- aerial-cars-rqcqh_v2
- bikedetection-7bpwy_v2
- car-detection-pyxz2_v4
- cars-bytt8_v35
- transport-rhkah_v8
- vehiclecount_v4
- vehicles-q0x2v-8kns4_v1
Class Image Counts
| Class Name | Image Count |
|---|---|
| Vehicle | 15163 |
Description
This model was trained using the YOLOv11 architecture on a custom dataset. The training process involved 75 epochs with a batch size of 32. The optimizer used was auto with an initial learning rate of 0.0005. Data augmentation was set to the Moderate level to enhance model robustness.
Usage
To use this model for inference, follow the instructions below:
from ultralytics import YOLO
# Load the trained model
model = YOLO('best.pt')
# Perform inference on an image
results = model('path_to_image.jpg')
# Display results
results.show()
- Created
- Jan 28, 2025
- Updated
- Jan 28, 2025