Update README.md

README.md

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 ---
 license: mit
+pipeline_tag: object-detection
+tags:
+- table-detection
+- spreadsheets
+- object-detection
+- faster-rcnn
+- pytorch
 ---
+
+# ExcelTableCNN: VEnron2 table detector
+
+A Faster R-CNN table-boundary detector for spreadsheets, from
+[ExcelTableCNN](https://github.com/Flagro/ExcelTableCNN), a license-clean
+reimplementation of the TableSense approach. Given a sheet's cell grid it
+predicts a bounding box (cell range) for each table, with PBR boundary snapping
+and a grid-context backbone.
+
+- Input: 30 per-cell feature channels, built by the package from `.xls`/`.xlsx`.
+- Output: table bounding boxes as cell ranges, each with a confidence score.
+- Weights: `final.pt` (about 201 MB), trained 160 epochs on the full VEnron2 set.
+
+## Metrics
+
+Evaluated on the held-out VEnron2 test split (197 sheets, 342 tables) at score
+threshold 0.5, using the strict Error-of-Boundary metric (EoB-0 = cell-exact,
+EoB-2 = within 2 cells):
+
+| Metric | Precision | Recall |
+|---|---|---|
+| EoB-0 (exact) | 47.7% | 48.2% |
+| EoB-2 (within 2 cells) | 66.5% | 67.3% |
+
+The reloaded checkpoint reproduces these numbers exactly (RoI pooling scale
+pinned to 1.0, see PR #8). For reference, the TableSense paper reports EoB-2
+precision 86.5% / recall 91.3%, trained on about 25x more hand-labeled sheets.
+
+## Usage
+
+Install the package (it handles featurization and decoding):
+
+```bash
+pip install git+https://github.com/Flagro/ExcelTableCNN.git
+```
+
+Download the weights and detect tables from the command line:
+
+```bash
+huggingface-cli download flagro/exceltablecnn-venron2 final.pt --local-dir .
+excel-table-cnn-detect report.xls --weights final.pt
+# Sheet1!B2:H45   score=0.973
+```
+
+Or from Python:
+
+```python
+from huggingface_hub import hf_hub_download
+from excel_table_cnn import load_checkpoint
+
+path = hf_hub_download("flagro/exceltablecnn-venron2", "final.pt")
+model = load_checkpoint(path, device="cpu")  # or "cuda"
+```
+
+## Training
+
+- Data: VEnron2, 1,288 training sheets, via the ExcelTableCNN pipeline.
+- Schedule: 160 epochs, batch size 1, SGD (lr 0.005, momentum 0.9, weight decay
+  5e-4), 100-step warmup, constant LR, mixed precision.
+- Featurization caps: default 2,048 x 512 cells. Seed 42.
+- Hardware: single NVIDIA T4 (Kaggle), about 4 hours.
+- Architecture: 30-channel input, grid-context backbone (stride 1), Faster
+  R-CNN detection head, PBR boundary-snapping head, 2 classes (table vs
+  background).
+- Code: excel-table-cnn 0.3.0, commit `0ed4205`.
+
+## Limitations
+
+- Trained only on VEnron2 (Enron-derived financial and operational
+  spreadsheets); expect lower accuracy on very different domains or layouts.
+- Cell-exact (EoB-0) detection is still under 50%; boundaries are often off by 1
+  to 2 cells, which is why EoB-2 is much higher. Use EoB-2 for downstream
+  extraction that tolerates small boundary error.
+- Sheets larger than 2,048 x 512 cells are clipped during featurization.
+
+## License and data
+
+Code and weights are MIT-licensed (see the
+[repository](https://github.com/Flagro/ExcelTableCNN)). The training data is the
+public VEnron2 corpus, derived from the Enron email dataset; confirm its terms
+permit your intended redistribution or use.
+
+## Citation
+
+Built on the method from TableSense (Dong et al., AAAI 2019). Please cite the
+original paper for the approach and link this repository for the implementation.