Instructions to use rujutashashikanjoshi/yolo26-vehicle-detection-4139_full_v1.10-100m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use rujutashashikanjoshi/yolo26-vehicle-detection-4139_full_v1.10-100m with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("rujutashashikanjoshi/yolo26-vehicle-detection-4139_full_v1.10-100m") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
YOLO26 MEDIUM Model
Fine-tuned YOLO26 model for object detection.
Model Details
- Architecture: YOLO26Medium
- Framework: Ultralytics YOLO26
- Resolution: 640x640
- Epochs: 100
- Batch Size: 8
Classes
car, truck
Usage
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="rujutashashikanjoshi/yolo26-vehicle-detection-4139_full-100m", filename="best.pt")
model = YOLO(model_path)
results = model("your_image.jpg", conf=0.25)
results[0].show()
Training Config
{
"model_type": "medium",
"epochs": 100,
"batch_size": 8,
"image_size": 640,
"learning_rate": 0.01,
"weight_decay": 0.0005,
"momentum": 0.937,
"warmup_epochs": 3,
"workers": 2,
"patience": 50,
"save_period": 10,
"conf_threshold": 0.25,
"iou_threshold": 0.45,
"output_dir": "./output/4139_100m"
}
License
AGPL-3.0
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