Base Model: PekingU/rtdetr_r50vd_coco_o365 (pretrained on COCO + Objects365)
Architecture: RT-DETR with ResNet-50-vd backbone
Task: Object Detection (bounding box prediction)
Image Size: 640×640
Classes (6): solar-panels, Cell, Cell-Multi, No-Anomaly, Shadowing, Unclassified

Training Details

Dataset: Francesco/solar-panels-taxvb (112 train / 19 val / 30 test images)
Epochs: 100
Batch Size: 8
Learning Rate: 5e-5 (cosine schedule with 5% warmup)
Optimizer: AdamW (weight_decay=1e-4)
Gradient Clipping: max_grad_norm=0.01
Precision: FP16
Augmentations: Perspective, HorizontalFlip, VerticalFlip, RandomBrightnessContrast, HueSaturationValue, RandomRotate90

Usage

python

from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image
import torch

# Load model and processor
model = AutoModelForObjectDetection.from_pretrained("Raidenop/solar-panels-rtdetr")
processor = AutoImageProcessor.from_pretrained("Raidenop/solar-panels-rtdetr")

# Load image
image = Image.open("your_image.jpg")

# Inference
with torch.no_grad():
    inputs = processor(images=[image], return_tensors="pt")
    outputs = model(**inputs)
    target_sizes = torch.tensor([[image.size[1], image.size[0]]])
    results = processor.post_process_object_detection(
        outputs, threshold=0.3, target_sizes=target_sizes
    )[0]

# Print results
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    box = [round(i, 2) for i in box.tolist()]
    print(f"Detected {model.config.id2label[label.item()]} with confidence {round(score.item(), 3)} at {box}")

Training Script

The full training script is available in this repository: train.py

To run training:

bash

pip install transformers datasets accelerate torch torchvision trackio albumentations>=1.4.5 torchmetrics pycocotools timm
python train.py

Requirements: NVIDIA GPU with 16GB+ VRAM (T4, A10G, or better)

References

RT-DETR Paper — "DETRs Beat YOLOs on Real-time Object Detection" (CVPR 2023)
HF Object Detection Tutorial
Dataset: solar-panels-taxvb (Roboflow 100 benchmark)

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern