Cardcaptor v3
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 (tradingcard) with arbitrary rotations Exports:...
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 80,1–80,6 MB
- Versión
- vRevision 621af57
- Actualizado
- 16 sept 2026
- Descargas
- 0
- Fuente
- Hugging Face
Descripción de la fuente
Trading Card Detector · YOLO11m OBB (v3)
Example 1
🚀 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
| File | Description |
|---|---|
weights/cardcaptor_v3_best.pt | Full precision PyTorch checkpoint (best epoch) |
onnx/cardcaptor_v3_best.onnx | Optimized ONNX export (80 MB) with built-in NMS |
📚 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)
| Metric | Value |
|---|---|
| mAP@50 | 0.983 |
| mAP@50-95 | 0.963 |
| Precision | 0.943 |
| Recall | 0.986 |
| Validation set | 55 hand-crafted photos / 210 cards |
🔍 Example Predictions
| Example 1 | Example 2 |
|---|---|
| Example 1 | Example 2 |
| Example 3 |
|---|
| Example 3 |
🧪 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
Questions or improvements? Please open an issue or PR!
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2 extractos--- language: en license: mit tags: - object-detection - oriented-bounding-box - yolo - trading-cards - webgpu-ready - onnx metrics: - mAP50 - mAP50-95 --- # 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](https://mlapi.us/cardcam) ## 📦 Artifacts | File | Description | |------|-------------| | `weights/cardcaptor_v3_best.pt` | Full precision PyTorch checkpoint (best epoch) | | `onnx/cardcaptor_v3_best.onnx` | Optimized ONNX export (80 MB) with built-in NMS | ## 📚 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) | Metric | Value | |--------|-------| | mAP@50 | **0.983** | | mAP@50-95 | **0.963** | | Precision | 0.943 | | Recall | 0.986 | | Validation set | 55 hand-crafted photos / 210 cards | ## 🔍 Example Predictions | Example 1 | Example 2 | |-----------|-----------| |  |  | | Exa...
Source context: 0 downloads · 1 likes · Pipeline object-detection · Repo AlecKarfonta/cardcaptor-v3
