Updated PYNQ Image Download Link for ZCU104 in the README.md

README.md

@@ -1,6 +1,5 @@
 :trophy: [1st place winner](https://www.openhw.eu/2023-results-gallery) in the PhD category of the 2023 [AMD Open Hardware Competition](https://www.openhw.eu/) :trophy:
 
-
 # YOLOv5 Object Detection on FPGAs with fpgaConvNet
 
 This repository provides an FPGA-based solution for executing object detection, focusing specifically on the popular YOLOv5 model architecture. By leveraging the power of Field-Programmable Gate Arrays (FPGAs) and utilising both the [**fpgaConvNet**](https://github.com/AlexMontgomerie/fpgaconvnet-model) and the [**Xilinx PYNQ**](http://www.pynq.io/) frameworks, this solution enables high-performance and efficient execution of object detection tasks.
@@ -20,67 +19,71 @@ This repository provides an FPGA-based solution for executing object detection,
 - [License](#license)
 - [Citation](#citation)
 
-
-
 ## Getting Started
 
 ### Setup FPGA with PYNQ
 
-1. Download the latest PYNQ image for your FPGA board from [here](http://www.pynq.io/board.html). For this project, we used the `ZCU104` board which is officially supported by PYNQ. If you are using a different board, you may need to build the PYNQ image yourself. See [here](https://github.com/Xilinx/PYNQ/issues/1425#issuecomment-1601772627) how we built the PYNQ image for the `ZCU106` board.
-***Important Notice 1:*** *Make sure to download or build the ***v3.0.1*** version of the PYNQ image which is compatible with the Xilinx ***Vivado 2022*** version which is used to generate the final bistream.*
+1. Download the latest PYNQ image for your FPGA board from [here](https://download.amd.com/opendownload/xlnx/zcu104_v3.0.1.zip). For this project, we used the `ZCU104` board which is officially supported by PYNQ. If you are using a different board, you may need to build the PYNQ image yourself. See [here](https://github.com/Xilinx/PYNQ/issues/1425#issuecomment-1601772627) how we built the PYNQ image for the `ZCU106` board.
+   **_Important Notice 1:_** \*Make sure to download or build the **_v3.0.1_** version of the PYNQ image which is compatible with the Xilinx **_Vivado 2022_** version which is used to generate the final bistream.\*
 2. Flash the PYNQ image to an SD card.
 3. Insert the SD card into the FPGA board and power it on.
-2. Follow the instructions [here](https://pynq.readthedocs.io/en/latest/getting_started/zcu104_setup.html) for the initial setup of the `ZCU104` board. (similar procedure is also applicable to other boards)
-4. Once you have connected the board to your network connect to it via SSH. The default credentials are `xilinx` for both the username and password.
+4. Follow the instructions [here](https://pynq.readthedocs.io/en/latest/getting_started/zcu104_setup.html) for the initial setup of the `ZCU104` board. (similar procedure is also applicable to other boards)
+5. Once you have connected the board to your network connect to it via SSH. The default credentials are `xilinx` for both the username and password.
 
 ### Python Environment Setup
+
 Once you have connected to the FPGA board via SSH, navigate to the `/home/xilinx/jupyter_notebooks` folder and clone this repository.
+
 ```shell
-$ cd /home/xilinx/jupyter_notebooks
-$ git clone --recurse-submodules https://github.com/ptoupas/amd-open-hardware-23
+cd /home/xilinx/jupyter_notebooks
+git clone --recurse-submodules https://github.com/ptoupas/amd-open-hardware-23
 ```
+
 Navigate to the cloned repository and run the following commands to install the necessary dependencies:
+
 ```shell
-$ cd amd-open-hardware-23
-$ sudo pip install -r requirements.txt
+cd amd-open-hardware-23
+sudo pip install -r requirements.txt
 ```
 
 ## Usage
 
 We provide a set of Jupyter notebooks that demonstrate the usage of the FPGA-based object detection solution. The notebooks are located in the root directory of this repository. The following table provides a brief description of each notebook:
 
-| Notebook | Description |
-| --- | --- |
-| `comparison.ipynb` | This notebook provides a comparison between the performance of the FPGA-based solution and the CPU-based solution with ONNX. |
-| `predict_yolo.ipynb` | This notebook demonstrates how to use the FPGA-based solution to perform object detection through a webcam. It executes the YOLOv5 model on the FPGA and displays the results on the screen in real-time. |
-| `validate_yolo.ipynb` | This notebook provides a validation of the FPGA-based solution on the coco128 dataset. It executes the YOLOv5 model on the FPGA and displays the validation results on the screen. |
+| Notebook              | Description                                                                                                                                                                                               |
+| --------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `comparison.ipynb`    | This notebook provides a comparison between the performance of the FPGA-based solution and the CPU-based solution with ONNX.                                                                              |
+| `predict_yolo.ipynb`  | This notebook demonstrates how to use the FPGA-based solution to perform object detection through a webcam. It executes the YOLOv5 model on the FPGA and displays the results on the screen in real-time. |
+| `validate_yolo.ipynb` | This notebook provides a validation of the FPGA-based solution on the coco128 dataset. It executes the YOLOv5 model on the FPGA and displays the validation results on the screen.                        |
 
-***Important Notice 3:*** *All of the above notebooks can be configured through various parameters that can be easily modified at the `yolov5n.toml` file located in the root directory of this repository. For more information about the available parameters, please refer to the [documentation](#documentation) section.*
+**_Important Notice 3:_** _All of the above notebooks can be configured through various parameters that can be easily modified at the `yolov5n.toml` file located in the root directory of this repository. For more information about the available parameters, please refer to the [documentation](#documentation) section._
 
 ## Documentation
 
 The following table provides a brief description of the available parameters that can be configured through the `yolov5n.toml` file:
 
-| Parameter | Description |
-| --- | --- |
-| `onnx_model_path` | The path to the ONNX model file used for the onnxruntime inference (cpu) and for executing the head of the YOLOv5 model on the FPGA scenario. |
-| `visualize` | Whether to visualize the results of the inference or not. |
-| `val_data_path` | The path to the coco128 dataset used for the validation scenario. |
-| `inf_exec` | The target device for the inference execution. Can be either `cpu` or `fpga`. |
-| `source` | The source of the input data. Can be either `webcam` or a path to a video file (an example video file is provided in the `input_video` directory). |
-| `input_data.imgsz` | The size of the input image. |
-| `predictor.conf_thres` | The confidence threshold to be used during the detection. |
-| `predictor.iou_thres` | The IoU threshold to be used during the detection. |
-| `predictor.classes` | The classes to be identified during the detection. Leave empty to identify all classes. |
-| `hardware.bitstream_path` | The path to the bitstream file generated by Vivado. |
-| `hardware.weights_path` | The path to the weights directory containing the weights of the YOLOv5 model that will be loaded to the FPGA. |
+| Parameter                 | Description                                                                                                                                        |
+| ------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `onnx_model_path`         | The path to the ONNX model file used for the onnxruntime inference (cpu) and for executing the head of the YOLOv5 model on the FPGA scenario.      |
+| `visualize`               | Whether to visualize the results of the inference or not.                                                                                          |
+| `val_data_path`           | The path to the coco128 dataset used for the validation scenario.                                                                                  |
+| `inf_exec`                | The target device for the inference execution. Can be either `cpu` or `fpga`.                                                                      |
+| `source`                  | The source of the input data. Can be either `webcam` or a path to a video file (an example video file is provided in the `input_video` directory). |
+| `input_data.imgsz`        | The size of the input image.                                                                                                                       |
+| `predictor.conf_thres`    | The confidence threshold to be used during the detection.                                                                                          |
+| `predictor.iou_thres`     | The IoU threshold to be used during the detection.                                                                                                 |
+| `predictor.classes`       | The classes to be identified during the detection. Leave empty to identify all classes.                                                            |
+| `hardware.bitstream_path` | The path to the bitstream file generated by Vivado.                                                                                                |
+| `hardware.weights_path`   | The path to the weights directory containing the weights of the YOLOv5 model that will be loaded to the FPGA.                                      |
 
 ## License
 
 This project is licensed under the GNU GPLv3 License - see the [LICENSE](https://github.com/ptoupas/amd-open-hardware-23/blob/main/LICENSE) file for details.
 
 ## Citation
+
 If you find this project useful in your research, please consider citing our work:
+
 ```BibTeX
 @article{toupas2023harflow3d,
         author={Toupas, Petros and Montgomerie-Corcoran, Alexander and Bouganis, Christos-Savvas and Tzovaras, Dimitrios},