update for regression test

README.md

@@ -43,23 +43,24 @@ You can use the raw model for object detection. See the [model hub](https://hugg
 
 The dataset MSCOCO2017 contains 118287 images for training and 5000 images for validation.
 
-Download COCO dataset and create directories in your code like this:
+Download COCO dataset and create/mount directories in your code like this:
   ```plain
-  └── datasets
-       └── coco
-             ├── annotations
-             |   ├── instances_val2017.json
-             |   └── ...
-             ├── labels
-             |   ├── val2017
-             |   |   ├── 000000000139.txt
-             |       ├── 000000000285.txt
-             |       └── ...
-             ├── images
-             |   ├── val2017
-             |   |   ├── 000000000139.jpg
-             |       ├── 000000000285.jpg
-             └── val2017.txt
+  └── yolov8m
+      └── datasets
+          └── coco
+                ├── annotations
+                |   ├── instances_val2017.json
+                |   └── ...
+                ├── labels
+                |   ├── val2017
+                |   |   ├── 000000000139.txt
+                |       ├── 000000000285.txt
+                |       └── ...
+                ├── images
+                |   ├── val2017
+                |   |   ├── 000000000139.jpg
+                |       ├── 000000000285.jpg
+                └── val2017.txt
   ```
 1. put the val2017 image folder under images directory or use a softlink
 2. the labels folder and val2017.txt above are generate by **general_json2yolo.py**
@@ -87,8 +88,8 @@ for batch in dataset:
     im = preprocess(im)
     if len(im.shape) == 3:
         im = im[None]
-    outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: im.cpu().numpy()})
-    outputs = [torch.tensor(item) for item in outputs]
+    outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: im.permute(0, 2, 3, 1).cpu().numpy()})
+    outputs = [torch.tensor(item).permute(0, 3, 1, 2) for item in outputs]
     preds = post_process(outputs)
     preds = non_max_suppression(
         preds, 0.25, 0.7, agnostic=False, max_det=300, classes=None
@@ -105,12 +106,12 @@ for batch in dataset:
 
  - Run inference for a single image
   ```python
-  python onnx_inference.py -m ./yolov8m_qat.onnx -i /Path/To/Your/Image --ipu --provider_config /Path/To/Your/Provider_config
+  python onnx_inference.py -m ./yolov8m.onnx -i /Path/To/Your/Image --ipu --provider_config /Path/To/Your/Provider_config
   ```
 *Note: __vaip_config.json__ is located at the setup package of Ryzen AI (refer to [Installation](#installation))*
  - Test accuracy of the quantized model
   ```python
-  python onnx_eval.py -m ./yolov8m_qat.onnx --ipu --provider_config /Path/To/Your/Provider_config
+  python onnx_eval.py -m ./yolov8m.onnx --ipu --provider_config /Path/To/Your/Provider_config
   ```
 
 ### Performance