Hugging Face's logo

amd
/
yolov5s

Object Detection
ONNX
RyzenAI
vision
YOLO
Pytorch
Model card Files Community
6
yolov5s
File size: 4,886 Bytes 
d857ef1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
581c9a2
d857ef1
 
581c9a2
 
d857ef1
 
 
 
 
 
 
 
581c9a2
 
d857ef1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
581c9a2
d857ef1
 
 
 
581c9a2
d857ef1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
 
---
license: apache-2.0
tags:
- RyzenAI
- object-detection
- vision
- YOLO
- Pytorch
datasets:
- COCO
metrics:
- mAP
---
# YOLOv5s model trained on COCO

YOLOv5s is the small version of YOLOv5 model trained on COCO object detection (118k annotated images) at resolution 640x640. It was released in [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5).

We develop a modified version that could be supported by [AMD Ryzen AI](https://onnxruntime.ai/docs/execution-providers/Vitis-AI-ExecutionProvider.html).


## Model description

YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.


## Intended uses & limitations

You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=amd/yolov5) to look for all available YOLOv5 models.


## How to use

### Installation

   Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI.
   Run the following script to install pre-requisites for this model.
   ```bash
   pip install -r requirements.txt 
   ```


### Data Preparation (optional: for accuracy evaluation)

The dataset MSCOCO2017 contains 118287 images for training and 5000 images for validation.

Download COCO dataset and create 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
  ```
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**
3. modify the coco.yaml like this:
```markdown
path: /path/to/your/datasets/coco  # dataset root dir
train: train2017.txt  # train images (relative to 'path') 118287 images
val: val2017.txt  # val images (relative to 'path') 5000 images
```


### Test & Evaluation

 - Code snippet from [`infer_onnx.py`](infer_onnx.py) on how to use
```python
    args = make_parser().parse_args()
    onnx_path = args.onnx_model
    onnx_weight = onnxruntime.InferenceSession(onnx_path)
    grid = np.load("./grid.npy", allow_pickle=True)
    anchor_grid = np.load("./anchor_grid.npy", allow_pickle=True)
    path = args.image_path 
    new_path = args.output_path
    conf_thres, iou_thres, classes, agnostic_nms, max_det = 0.25, 0.45, None, False, 1000

    img0 = cv2.imread(path)
    img = pre_process(img0)
    onnx_input = {onnx_weight.get_inputs()[0].name: img}
    onnx_output = onnx_weight.run(None, onnx_input)
    onnx_output = post_process(onnx_output)
    pred = non_max_suppression(
        onnx_output[0], conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det
    )
    colors = Colors()
    det = pred[0]
    im0 = img0.copy()
    annotator = Annotator(im0, line_width=2, example=str(names))
    if len(det):
        # Rescale boxes from img_size to im0 size
        det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

        # Write results
        for *xyxy, conf, cls in reversed(det):
            c = int(cls)  # integer class
            label = f"{names[c]} {conf:.2f}"
            annotator.box_label(xyxy, label, color=colors(c, True))
    # Stream results
    im0 = annotator.result()
    cv2.imwrite(new_path, im0)
```

 - Run inference for a single image
  ```python
  python infer_onnx.py --onnx_model ./yolov5s.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 eval_onnx.py --onnx_model ./yolov5s.onnx --ipu --provider_config /Path/To/Your/Provider_config
  ```

### Performance

|Metric |Accuracy on IPU|
| :----:  | :----: |
|AP\@0.50:0.95|0.356|


```bibtex
@software{glenn_jocher_2021_5563715,
  author       = {Glenn Jocher et. al.},
  title        = {{ultralytics/yolov5: v6.0 - YOLOv5n 'Nano' models, 
                   Roboflow integration, TensorFlow export, OpenCV
                   DNN support}},
  month        = oct,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v6.0},
  doi          = {10.5281/zenodo.5563715},
  url          = {https://doi.org/10.5281/zenodo.5563715}
}
```