README.md

---
license: apache-2.0
pipeline_tag: image-segmentation
---
# Model Card for Model ID

<!-- YOLO‑based single‑class photocard detector trained on 156 hand‑annotated images. Designed to detect selfie‑style photocards (~55×85 mm) in real‑world scenes. Achieved 97.4% mAP@50, 94.4% precision, 94.8% recall -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## 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)

https://app.roboflow.com/priyas-workspace-nntro/bdata-497-final-project/models/bdata-497-final-project/1
### 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