File size: 1,491 Bytes
0271854 a21ff88 0271854 a21ff88 0271854 a21ff88 0271854 a21ff88 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | ---
language: []
license: mit
tags:
- pytorch
- image-segmentation
- sam2
- glove
- baseball
- sports-analytics
- computer-vision
- custom-model
library_name: pytorch
datasets:
- custom
metrics:
- dice
- iou
inference: true
widget: []
model-index:
- name: glove_labelling
results: []
---
# Glove Labelling Model (SAM2 fine-tuned)
This repository contains a fine-tuned [SAM2](https://github.com/facebookresearch/sam2) hierarchical image segmentation model adapted for high-precision baseball glove segmentation.
### 💡 What it does
Given a frame from a pitching video, this model outputs per-pixel segmentations for:
- `glove_outline`
- `webbing`
- `thumb`
- `palm_pocket`
- `hand`
- `glove_exterior`
Trained on individual pitch frame sequences using COCO format masks.
---
### 🏗 Architecture
- Base Model: `SAM2Hierarchical`
- Framework: PyTorch
- Input shape: `[1, 3, 720, 1280]` RGB frame
- Output: Segmentation logits across 6 glove-related classes
---
### 🔧 Usage
To use the model for inference:
```python
import torch
from PIL import Image
import torchvision.transforms as T
model = torch.load("pytorch_model.bin", map_location="cpu")
model.eval()
transform = T.Compose([
T.Resize((720, 1280)),
T.ToTensor()
])
img = Image.open("example.jpg").convert("RGB")
x = transform(img).unsqueeze(0)
with torch.no_grad():
output = model(x)
# Convert logits to class labels
pred_mask = output.argmax(dim=1).squeeze().cpu().numpy()
|