Glove Labelling Model (SAM 2.1 Fine-Tuned)

This model is a fine-tuned Segment Anything Model (SAM 2.1) designed specifically for baseball glove segmentation. It identifies fine-grained regions on a pitcher’s glove from video frames, with the goal of analyzing glove position, shape, and movement across pitches.

🔍 Model Details

Architecture: SAM 2.1 Hiera-L variant
Framework: PyTorch
Training Type: Image-only fine-tuning on custom glove segmentation data
Losses: Dice, IoU, and mask loss
Epochs: 50
Batch Size: 2
Dataset: Custom COCO-format sequences of glove mask annotations split by pitch

🏷️ Labels (Classes)

This model supports six segmentation classes:

glove_outline
webbing
thumb
palm_pocket
hand
glove_exterior

📁 Files in This Repo

File	Description
`pytorch_model.bin`	Trained PyTorch weights (`.pt` file)
`config.json`	Model and dataset configuration
`README.md`	You're reading it

🚀 Deployment Options

You can deploy this model using:

Google Cloud Vertex AI (via Model Garden)
TorchServe
CVAT (via a custom segmentation model)
Hugging Face Inference Endpoints (manual handler required)

🔗 Author

Created and maintained by caball21
Please cite if used in academic or production applications.