docs: rename WinML CLI example path to microsoft_table-transformer-detection

README.md

@@ -1,35 +1,35 @@
----
-license: mit
-widget:
-- src: https://www.invoicesimple.com/wp-content/uploads/2018/06/Sample-Invoice-printable.png
-  example_title: Invoice
----
-
-# Table Transformer (fine-tuned for Table Detection) 
-
-Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer). 
-
-Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
-
-## Model description
-
-The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
-
-## Usage
-
-You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info.
-
-### Run as ONNX (CPU / NPU / GPU)
-
-Detect tables ~14× faster on a Windows NPU at half the model size, with mAP within 1% of the original PyTorch checkpoint — by exporting this model to ONNX. You can also export to ONNX to run on CPU or GPU.
-
-Benchmarked on an Intel Core Ultra 7 258V (PubTables-1M validation, 1000 samples):
-
-| Model   | Device       | Precision   | mAP    | mean latency (ms) | p50 latency (ms) | Size (MB) |
-|---------|--------------|-------------|--------|-------------------|------------------|-----------|
-| PyTorch | CPU          | fp32        | 0.9887 | 620.9             | 600.3            | 115       |
-| ONNX    | OpenVINO NPU | w8a16 (QDQ) | 0.9822 | 44.1              | 41.6             | 58        |
-
-- **How to convert** — Export and quantize with [Microsoft's WinML CLI](https://github.com/microsoft/winml-cli). The NPU build is QDQ-quantized to w8a16; fp32 builds for CPU and GPU are also supported. End-to-end build, evaluation, and a Python inference example: [examples/microsoft-table-transformer-detection](https://github.com/microsoft/winml-cli/blob/main/examples/microsoft-table-transformer-detection/README.md).
-- **How to run on Windows** — Use [Windows ML](https://learn.microsoft.com/en-us/windows/ai/new-windows-ml/overview), which manages execution providers for NPU / GPU / CPU and routes ONNX inference to the right backend automatically.
-- **How to run on other platforms** — Use [ONNX Runtime](https://onnxruntime.ai/docs/) with the execution provider of your choice (OpenVINO, QNN, DirectML, CUDA, CPU, etc.).
+---
+license: mit
+widget:
+- src: https://www.invoicesimple.com/wp-content/uploads/2018/06/Sample-Invoice-printable.png
+  example_title: Invoice
+---
+
+# Table Transformer (fine-tuned for Table Detection) 
+
+Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer). 
+
+Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
+
+## Usage
+
+You can use the raw model for detecting tables in documents. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info.
+
+### Run as ONNX (CPU / NPU / GPU)
+
+Detect tables ~14× faster on a Windows NPU at half the model size, with mAP within 1% of the original PyTorch checkpoint — by exporting this model to ONNX. You can also export to ONNX to run on CPU or GPU.
+
+Benchmarked on an Intel Core Ultra 7 258V (PubTables-1M validation, 1000 samples):
+
+| Model   | Device       | Precision   | mAP    | mean latency (ms) | p50 latency (ms) | Size (MB) |
+|---------|--------------|-------------|--------|-------------------|------------------|-----------|
+| PyTorch | CPU          | fp32        | 0.9887 | 620.9             | 600.3            | 115       |
+| ONNX    | OpenVINO NPU | w8a16 (QDQ) | 0.9822 | 44.1              | 41.6             | 58        |
+
+- **How to convert** — Export and quantize with [Microsoft's WinML CLI](https://github.com/microsoft/winml-cli). The NPU build is QDQ-quantized to w8a16; fp32 builds for CPU and GPU are also supported. End-to-end build, evaluation, and a Python inference example: [examples/microsoft_table-transformer-detection](https://github.com/microsoft/winml-cli/blob/main/examples/microsoft_table-transformer-detection/README.md).
+- **How to run on Windows** — Use [Windows ML](https://learn.microsoft.com/en-us/windows/ai/new-windows-ml/overview), which manages execution providers for NPU / GPU / CPU and routes ONNX inference to the right backend automatically.
+- **How to run on other platforms** — Use [ONNX Runtime](https://onnxruntime.ai/docs/) with the execution provider of your choice (OpenVINO, QNN, DirectML, CUDA, CPU, etc.).