Update readme

README.md

@@ -1,110 +1,110 @@
----
-license: apache-2.0
-tags:
-  - RyzenAI
-  - Int8 quantization
-  - Face Restoration
-  - PSFRGAN
-  - ONNX
-  - Computer Vision
-metrics:
-  - PSNR
-  - MS_SSIM
-  - FID
----
-
-# PSFRGAN for face restoration
-
-The model operates at 512x512 resolution and is particularly effective at restoring faces with various degradations including blur, noise, compression artifacts, and low resolution.
-
-It was introduced in the paper _Progressive Semantic-Aware Style Transformation for Blind Face Restoration_ by Chaofeng Chen et al. at CVPR 2021.
-
-We have developed a modified version optimized for [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html).
-
-## Model description
-
-PSFRGAN (Progressive Semantic-aware Face Restoration Generative Adversarial Network) is a deep learning model designed for blind face restoration, capable of recovering high-quality face images from severely degraded inputs.
-
-## Intended uses & limitations
-
-You can use this model for face restoration tasks. See the [model hub](https://huggingface.co/models?search=amd/ryzenai-psfrgan) for all available psfrgan models.
-
-## How to use
-
-### Installation
-
-```bash
-# inference only
-pip install -r requirements-infer.txt
-# inference & evaluation
-pip install -r requirements-eval.txt
-```
-
-### Data Preparation (optional: for accuracy evaluation)
-
-1. Download `CelebA-Test (LQ)` and `CelebA-Test (HQ)` from [GFP-GAN homepage](https://xinntao.github.io/projects/gfpgan)
-2. Organize the dataset directory as follows:
-
-```Plain
-└── datasets
-     └── celeba_512_validation
-           ├── 00000000.png
-           ├── ...
-     ├── celeba_512_validation_lq
-           ├── 00000000.png
-           ├── ...
-
-```
-
-### Test & Evaluation
-
-- Run inference on images
-
-```bash
-python onnx_inference.py --onnx psfrgan_nchw_fp32.onnx --latent latent.npy --input /Path/To/Image --out-dir outputs
-python onnx_inference.py --onnx psfrgan_nhwc_int8.onnx --latent latent.npy --input /Path/To/Image --out-dir outputs
-```
-
-**Arguments:**
-
-- `--input`: Accepts either a single image file path or a directory path. If it's a file, the script will process that image only. If it's a directory, the script will recursively scan for .png, .jpg, and .jpeg files and process all of them.
-- `--latent`: (Optional) Path to the latent code file (.npy). If not provided, random latent values will be generated with a fixed seed for reproducibility.
-- `--out-dir`: Output directory where the restored images will be saved.
-
-- Evaluate the quantized model
-
-```bash
-# eval fp32
-python onnx_eval.py \
-      --onnx psfrgan_nchw_fp32.onnx \
-      --latent latent.npy \
-      --hq-dir datasets/celeba_512_validation \
-      --lq-dir datasets/celeba_512_validation_lq \
-      --out-dir outputs/fp32 -clean
-
-# eval int8
-python onnx_eval.py \
-      --onnx psfrgan_nhwc_int8.onnx \
-      --latent latent.npy \
-      --hq-dir datasets/celeba_512_validation \
-      --lq-dir datasets/celeba_512_validation_lq \
-      --out-dir outputs/int8 -clean
-```
-
-### Performance
-
-| Model          | PSNR(↑) | MS_SSIM(↑) | FID(↓) |
-| -------------- | ------- | ---------- | ------ |
-| PSFRGAN (fp32) | 25.27   | 0.8500     | 21.99  |
-| PSFRGAN (int8) | 25.27   | 0.8487     | 24.34  |
-
----
-
-```bibtex
-@inproceedings{ChenPSFRGAN,
-    author = {Chen, Chaofeng and Li, Xiaoming and Lingbo, Yang and Lin, Xianhui and Zhang, Lei and Wong, Kwan-Yee~K.},
-    title = {Progressive Semantic-Aware Style Transformation for Blind Face Restoration},
-    Journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-    year = {2021}
-}
-```
+---
+license: apache-2.0
+tags:
+  - RyzenAI
+  - Int8 quantization
+  - Face Restoration
+  - PSFRGAN
+  - ONNX
+  - Computer Vision
+metrics:
+  - PSNR
+  - MS_SSIM
+  - FID
+---
+
+# PSFRGAN for face restoration
+
+The model operates at 512x512 resolution and is particularly effective at restoring faces with various degradations including blur, noise, and low resolution.
+
+It was introduced in the paper _Progressive Semantic-Aware Style Transformation for Blind Face Restoration_ by Chaofeng Chen et al. at CVPR 2021.
+
+We have developed a modified version optimized for [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html).
+
+## Model description
+
+PSFRGAN (Progressive Semantic-aware Face Restoration Generative Adversarial Network) is a deep learning model designed for blind face restoration, capable of recovering high-quality face images from severely degraded inputs.
+
+## Intended uses & limitations
+
+You can use this model for face restoration tasks. See the [model hub](https://huggingface.co/models?search=amd/ryzenai-psfrgan) for all available psfrgan models.
+
+## How to use
+
+### Installation
+
+```bash
+# inference only
+pip install -r requirements-infer.txt
+# inference & evaluation
+pip install -r requirements-eval.txt
+```
+
+### Data Preparation (optional: for evaluation)
+
+1. Download `CelebA-Test (LQ)` and `CelebA-Test (HQ)` from [GFP-GAN homepage](https://xinntao.github.io/projects/gfpgan)
+2. Organize the dataset directory as follows:
+
+```Plain
+└── datasets
+     └── celeba_512_validation
+           ├── 00000000.png
+           ├── ...
+     ├── celeba_512_validation_lq
+           ├── 00000000.png
+           ├── ...
+
+```
+
+### Test & Evaluation
+
+- Run inference on images
+
+```bash
+python onnx_inference.py --onnx psfrgan_nchw_fp32.onnx --latent latent.npy --input /Path/To/Image --out-dir outputs
+python onnx_inference.py --onnx psfrgan_nhwc_int8.onnx --latent latent.npy --input /Path/To/Image --out-dir outputs
+```
+
+**Arguments:**
+
+- `--input`: Accepts either a single image file path or a directory path. If it's a file, the script will process that image only. If it's a directory, the script will recursively scan for .png, .jpg, and .jpeg files and process all of them.
+- `--latent`: (Optional) Path to the latent code file (.npy). If not provided, random latent values will be generated with a fixed seed for reproducibility.
+- `--out-dir`: Output directory where the restored images will be saved.
+
+- Evaluate the quantized model
+
+```bash
+# eval fp32
+python onnx_eval.py \
+      --onnx psfrgan_nchw_fp32.onnx \
+      --latent latent.npy \
+      --hq-dir datasets/celeba_512_validation \
+      --lq-dir datasets/celeba_512_validation_lq \
+      --out-dir outputs/fp32 -clean
+
+# eval int8
+python onnx_eval.py \
+      --onnx psfrgan_nhwc_int8.onnx \
+      --latent latent.npy \
+      --hq-dir datasets/celeba_512_validation \
+      --lq-dir datasets/celeba_512_validation_lq \
+      --out-dir outputs/int8 -clean
+```
+
+### Performance
+
+| Model          | PSNR(↑) | MS_SSIM(↑) | FID(↓) |
+| -------------- | ------- | ---------- | ------ |
+| PSFRGAN (fp32) | 25.27   | 0.8500     | 21.99  |
+| PSFRGAN (int8) | 25.27   | 0.8487     | 24.34  |
+
+---
+
+```bibtex
+@inproceedings{ChenPSFRGAN,
+    author = {Chen, Chaofeng and Li, Xiaoming and Lingbo, Yang and Lin, Xianhui and Zhang, Lei and Wong, Kwan-Yee~K.},
+    title = {Progressive Semantic-Aware Style Transformation for Blind Face Restoration},
+    Journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year = {2021}
+}
+```