cardcaptor-v3

Trading Card Detector · YOLO11m OBB (v3)

🚀 Summary

• Architecture: YOLOv11m with oriented bounding box (OBB) head (7-value outputs: cx, cy, w, h, conf, class, angle)
• Input resolution: 1088 × 1088
• Targets: Single class (trading_card) with arbitrary rotations
• Exports: PyTorch checkpoint + ONNX (built-in NMS, WebGPU/WebAssembly ready)
• Training script: training_app/src/training/train_yolo_obb.py

Try iy out here: https://mlapi.us/cardcam

📦 Artifacts

📚 Dataset

• Name: merged_dataset_50x3
• Composition: ~60K synthetic renders + ~220 handcrafted captures × deterministic 50× photometric multiplier (~11K effective hand-crafted samples)
• Validation: 55 hand-crafted frames (210 labeled cards) with 90% handcrafted ratio to match real-world deployment
• Label format: YOLO OBB (four polygon corners per box)

🎨 Augmentation Highlights

• Deterministic photometric pre-augmentation for hand-crafted oversampling (brightness/contrast/gamma/noise/blur/HSV shifts)
• YOLO runtime augments: ±60° rotation, ±0.15 translation, ±0.6 scale, ±20° shear, perspective jitter, flipud 0.3, fliplr 0.5
• Composition augments: mosaic (1.0), mixup (0.1), copy-paste (0.15), RandAugment, random erasing (0.4)

🏋️ Training Recipe

• Batch size: 8
• Epochs: 50 (early stop patience 5)
• Optimizer: Ultralytics default (SGD w/ warmup)
• Loss head: OBB-specific DFL + cls losses
• Validation cadence: Every 5 epochs with periodic visualization dumps
• Hardware: NVIDIA RTX 5090, CUDA 12.8

📊 Evaluation (best epoch)

🔍 Example Predictions

🧪 Inference

from ultralytics import YOLO

model = YOLO("weights/cardcaptor_v3_best.pt")
results = model("card_photo.jpg", conf=0.25)

ONNX (WebGPU / WebAssembly) example:

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession("onnx/cardcaptor_v3_best.onnx", providers=["CPUExecutionProvider"])
input_name = session.get_inputs()[0].name
outputs = session.run(None, {input_name: image_tensor})

✅ Responsible Use

This model only predicts bounding boxes for trading cards. It does not read card text or assess authenticity.

📎 Resources

• Training script reference: training_app/src/training/train_yolo_obb.py
• Detailed training log & analyzer outputs stored under trading_cards_obb/yolo11m_obb_merged_50x3/
• Claude design doc: https://claude.ai/share/2886b94f-64a3-4b46-a42c-ba308f106902

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Questions or improvements? Please open an issue or PR!