YOLOv8m Pokemon Classification Model
| Property | Value |
|---|---|
| Framework | PyTorch / Ultralytics |
| Model Type | Image Classification |
| Top-1 Accuracy | 3.279% |
| Top-5 Accuracy | 9.699% |
| Downloads | 4,818 |
What is yolov8m-pokemon-classification?
This is a specialized image classification model based on the YOLOv8 medium architecture, designed specifically for Pokemon recognition. It can identify over 150 different Pokemon species, including all first-generation Pokemon and some regional variants like Alolan Sandslash.
Implementation Details
Built on the ultralytics framework version 8.0.23, this model implements a classification approach using the robust YOLOv8 architecture. It requires the ultralyticsplus package version 0.0.24 for optimal performance and includes confidence threshold customization options.
- Built on YOLOv8 medium architecture
- Supports 150+ Pokemon classes
- Implements confidence threshold customization
- Uses PyTorch backend with Ultralytics framework
Core Capabilities
- Classification of first-generation Pokemon
- Confidence score prediction for each class
- Batch processing support
- Easy integration with Python applications
Frequently Asked Questions
Q: What makes this model unique?
This model specializes in Pokemon classification with an extensive class list covering the entire first-generation Pokemon roster. It's built on the reliable YOLOv8 architecture and provides both top-1 and top-5 accuracy metrics for reliable predictions.
Q: What are the recommended use cases?
The model is ideal for Pokemon recognition in images, automated Pokemon cataloging systems, and educational applications teaching about Pokemon species. It's particularly useful for projects requiring first-generation Pokemon identification.
