ResNet-18 Sports Category Classification
| Property | Value |
|---|---|
| Model Architecture | ResNet-18 |
| Task Type | Image Classification |
| Input Size | 224x224 pixels |
| Number of Classes | 9 |
| Accuracy | 92.4% |
| Framework | PyTorch |
What is resnet18-sports-category-classification?
This is a specialized image classification model based on the ResNet-18 architecture, fine-tuned specifically for recognizing nine different sports categories. The model demonstrates robust performance with a 92.4% accuracy rate, making it particularly useful for automated sports image analysis and categorization.
Implementation Details
The model utilizes the ResNet-18 architecture with modifications to the final fully connected layer to accommodate 9 output classes. It's trained using the Adam optimizer with a learning rate of 1e-4 and Cross-Entropy loss function. The training process involved 10 epochs with a batch size of 32 on the 100 Sports Image Classification dataset.
- Pretrained ResNet-18 backbone with custom classification head
- Input images are normalized using ImageNet statistics
- Supports real-time inference on both CPU and CUDA-enabled devices
- Comprehensive evaluation metrics including precision (88.2%) and recall (82.8%)
Core Capabilities
- Accurate classification of 9 sports categories: cricket, archery, football, basketball, tennis, baseball, hockey, golf, and boxing
- Robust performance across various image conditions
- Easy integration with PyTorch workflows
- Efficient inference with reasonable computational requirements
Frequently Asked Questions
Q: What makes this model unique?
This model combines the proven ResNet-18 architecture with specialized training for sports classification, achieving high accuracy while maintaining efficiency. Its focused scope on 9 specific sports categories allows for more precise classification compared to general-purpose image classifiers.
Q: What are the recommended use cases?
The model is ideal for sports content management systems, automated sports image tagging, sports analytics platforms, and research applications requiring automated sports classification. It's particularly useful for organizations handling large volumes of sports-related media content.
