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README.md

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+---
+license: mit
+tags:
+  - action-segmentation
+  - temporal-action-segmentation
+  - video-understanding
+  - diffact
+datasets:
+  - gtea
+  - 50salads
+  - breakfast
+---
+
+# TST: Temporal Segment Transformer for Action Segmentation
+
+Pre-trained DiffAct+TST checkpoints for temporal action segmentation on three benchmark datasets.
+
+## Model Description
+
+TST (Temporal Segment Transformer) is a plug-in segment-level refinement module that improves frame-level action segmentation backbones. It uses DETR-style Hungarian matching to refine backbone predictions at the segment level via cross-attention and self-attention.
+
+These checkpoints use **DiffAct** (ICCV'23) as the backbone with TST applied as a Stage 2 refinement head (frozen backbone).
+
+## Results
+
+### GTEA
+
+| Method | F1@10 | F1@25 | F1@50 | Edit | Acc |
+|--------|-------|-------|-------|------|-----|
+| DiffAct | 92.5 | 91.5 | 84.7 | 89.6 | 80.3 |
+| **DiffAct+TST** | **94.2** | **93.0** | **87.1** | **90.9** | **81.4** |
+
+### 50Salads
+
+| Method | F1@10 | F1@25 | F1@50 | Edit | Acc |
+|--------|-------|-------|-------|------|-----|
+| DiffAct | 90.1 | 89.2 | 83.7 | 85.0 | 88.9 |
+| **DiffAct+TST** | **92.3** | **91.8** | **87.4** | **87.4** | **89.7** |
+
+### Breakfast
+
+| Method | F1@10 | F1@25 | F1@50 | Edit | Acc |
+|--------|-------|-------|-------|------|-----|
+| DiffAct | 80.3 | 75.9 | 64.6 | 78.4 | 76.4 |
+| **DiffAct+TST** | **81.2** | **77.1** | **65.9** | **79.0** | **76.9** |
+
+## Checkpoints
+
+```
+gtea/
+├── split_1_best.pth   (21 MB)
+├── split_2_best.pth   (21 MB)
+├── split_3_best.pth   (21 MB)
+└── split_4_best.pth   (21 MB)
+
+50salads/
+├── split_1_best.pth   (21 MB)
+├── split_2_best.pth   (21 MB)
+├── split_3_best.pth   (21 MB)
+├── split_4_best.pth   (21 MB)
+└── split_5_best.pth   (21 MB)
+
+breakfast/
+├── split_1_best.pth   (65 MB)
+├── split_2_best.pth   (65 MB)
+├── split_3_best.pth   (65 MB)
+└── split_4_best.pth   (65 MB)
+```
+
+Total: ~525 MB
+
+## Usage
+
+```python
+import torch
+from tst.wrapper import BackboneWithTST, DiffActAdapter
+from tst.tst_refiner import TSTRefiner
+
+# Load checkpoint
+ckpt = torch.load("gtea/split_1_best.pth", map_location="cpu")
+
+# Build model (see main repo for full setup)
+refiner = TSTRefiner(n_classes=11, feat_dim=192, inner_dim=64)
+model = BackboneWithTST(adapter, refiner, freeze_backbone=True)
+model.load_state_dict(ckpt, strict=False)
+```
+
+See the [GitHub repository](https://github.com/yangbai123/TST-action-segmentation) for full training and evaluation code.
+
+## Training Details
+
+| Dataset | lr | lr_transformer | inner_dim | Epochs | Backbone |
+|---------|-----|---------------|-----------|--------|----------|
+| GTEA | 5e-4 | 5e-5 | 64 | 60 | DiffAct |
+| 50Salads | 5e-4 | 5e-5 | 64 | 60 | DiffAct |
+| Breakfast | 5e-5 | 5e-6 | 128 | 60 | DiffAct |
+
+All trained with Adam optimizer, cosine annealing LR schedule, batch size 1.
+
+## Citation
+
+```bibtex
+@article{tst2024,
+  title={Temporal Segment Transformer for Action Segmentation},
+  author={},
+  year={2024}
+}
+```