This model is a fine-tuned ConvNeXt-Base architecture trained to classify 15 different types of sports balls. The model can accurately distinguish between various sports equipment including footballs, basketballs, tennis balls, and more specialized items like hockey pucks and shuttlecocks.
The model was trained using transfer learning from a ConvNeXt-Base model pre-trained on ImageNet, with the classification head modified for 15-class sports ball classification. It achieves strong performance across all sports ball categories and is optimized for real-world deployment scenarios.
Key features:
High accuracy across 15 sports ball categories
Robust to different lighting conditions and backgrounds
Optimized for 224x224 input images
Fast inference suitable for real-time applications
Base model: convnext_base (pretrained on ImageNet)
Fine-tuning approach: Fine-tuning with frozen backbone
Dataset:
Epochs: 10
Optimizer: AdamW
Learning Rate: 0.001
Batch Size: 32
Additional Information
The model uses standard ImageNet preprocessing with mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].
For best results:
Use clear, well-lit images
Ensure the sports ball occupies a significant portion of the image
Images should be in RGB format
Model performs best on images similar to the training distribution
The training dataset consisted of carefully curated sports ball images with balanced representation across all 15 categories. Data augmentation techniques including rotation, scaling, and color jittering were applied during training to improve generalization.
Limitations
Performance may vary on heavily occluded or poorly lit images
Model is specifically trained for the 15 sports ball categories listed above
May not generalize well to sports balls not in the training set
Best results with single-object images where the sports ball is the primary subject
Performance may decrease with unusual camera angles or extreme close-ups
