Update README.md

README.md

@@ -4,9 +4,10 @@ license: mit
 tags:
   - pytorch
   - image-segmentation
-  - sam
+  - sam2
   - glove
   - baseball
+  - sports-analytics
   - computer-vision
   - custom-model
 library_name: pytorch
@@ -15,8 +16,63 @@ datasets:
 metrics:
   - dice
   - iou
-inference: false
+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()