Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using this raw template.
Model Details
Architecture: YOLO object detector (Roboflow hosted training)
Task: Single‑class object detection
Output: Bounding boxes around photocards
Intended use: Stage 1 of a photocard processing pipeline (cataloging, classification, pricing)
Model Description
- Developed by: Priya Rasal
- Model type: YOLO object detector (bounding box detection)
- Finetuned from model: Pretrained YOLO backbone provided by Roboflow
Uses
Direct Use
Detecting photocards in images
Cropping photocards for downstream tasks
Preprocessing for classification, cataloging, or pricing systems
Identifying photocards in cluttered or real‑world scenes
Downstream Use
Photocard classification (e.g., identifying the idol or version)
Marketplace automation (auto‑detecting cards in listings)
Inventory management for collectors
Dataset creation for future models
Out-of-Scope Use
Detecting people or faces
Identifying which idol is on the photocard
Detecting non‑photocard rectangular objects (phones, books, receipts)
Any biometric or identity recognition
Bias, Risks, and Limitations
May detect rectangular objects as photocards in rare cases
May miss photocards with extreme glare, reflections, or occlusion
Performance depends on lighting and background diversity
Only trained on 156 images — limited exposure to rare edge cases
Not suitable for identity recognition or personal data analysis
Recommendations
Use a confidence threshold appropriate for your application
Validate predictions manually in high‑stakes use cases
Retrain or fine‑tune with more diverse data for improved robustness
How to Get Started with the Model
Use the code below to get started with the model.
from ultralytics import YOLO
model = YOLO("path/to/your/model.pt") results = model("your_image.jpg") results.show()
Training Details
Training Data
Total images: 156
Source: Personal photocard collection + marketplace images
Annotation method: Foundation model auto‑labeling + manual correction
Class definition: Photocard = selfie‑style, rectangular, ~55×85 mm
Excluded: postcards, album inclusions, sleeves without cards, binder pockets
Training Procedure
Training platform: Roboflow Hosted Training
Training approach: Transfer learning from pretrained YOLO backbone
Image size: 640×640
Epochs: ~50–100 (auto‑selected)
Batch size: Auto‑selected
Learning rate: Warmup + cosine decay
Optimizer: AdamW or SGD (Roboflow default)
Precision: Mixed precision (fp16)
Speeds, Sizes, Times [optional]
Training time: ~10–20 minutes (depending on GPU)
Model size: Depends on YOLO variant exported (typically 10–40 MB)
Evaluation
Testing Data, Factors & Metrics
Testing Data
Validation and test splits generated automatically by Roboflow (70/20/10)
Factors
Lighting variation
Background clutter
Card orientation
Sleeve reflections
Flash glare
Occluding shadows
Overlaps
Metrics
mAP@50: 97.4%
Precision: 94.4%
Recall: 94.8%
Results
Summary
The model demonstrates strong localization accuracy and generalization across diverse real‑world scenes.
High precision and recall indicate low false positives and low missed detections.
Photocard class annotated/identified 1023 times
Model Card Author
Priya Rasal