Beta

Explore
Marketplace
Neural Labs
Chat
Wallet
Docs

Beta

Explore Marketplace Neural Labs Wallet Docs

Build

Manage

My assets Manage wallet Organization

Community

Discord X (Twitter)Telegram

Docs Privacy Policy Terms and Conditions

Proud member of

Proud member of

Docs Cookie Policy

Privacy Policy Terms and Conditions

© 2026 AlphaNeural AI. All rights reserved.

rfdetr-vehcile-39 – AI Model by schelian-uta | AlphaNeural AI

You can deploy this model and start earning money today!

schelian-uta/
rfdetr-vehcile-39

like

0

object-detection

computer-vision

Views

No views yet

RF-DETR MEDIUM Model

This is an RF-DETR (Real-time Transformer-based Object Detector) model fine-tuned on a custom dataset.

Model Details

Model Architecture: RFDETRMedium
Framework: RF-DETR (PyTorch)
Image Resolution: 640x640
Training Epochs: 50
Batch Size: 4 (with gradient accumulation steps: 4)

Classes

The model is trained to detect the following classes: car, truck

Usage

Using RF-DETR Library (Recommended)

python

from rfdetr import RFDETRMedium
from PIL import Image

# Load the model
model = RFDETRMedium(
    pretrain_weights="hf://schelian-uta/rfdetr-vehcile-39/checkpoint_best_total.pth"
)
model.optimize_for_inference()

# Run inference
image = Image.open("your_image.jpg")
detections = model.predict(image, threshold=0.5)

print(f"Found {len(detections)} detections")

Using PyTorch Directly

python

import torch
from huggingface_hub import hf_hub_download

# Download the model weights
model_path = hf_hub_download(
    repo_id="schelian-uta/rfdetr-vehcile-39",
    filename="pytorch_model.bin"
)

# Load the state dict
state_dict = torch.load(model_path, map_location='cpu')

Training Details

Optimizer: AdamW
Learning Rate Schedule: Cosine with warmup
Data Format: COCO JSON
Framework Version: rfdetr>=1.2.1

Files in this Repository

checkpoint_best_total.pth: Original RF-DETR checkpoint (best model)
pytorch_model.bin: Standard PyTorch weights for compatibility
config.json: Model configuration
class_names.txt: List of detection classes
results.json: Training results and metrics
metrics_plot.png: Visualization of training metrics
log.json: Full training log

License

This model is released under the Apache 2.0 License.