https://github.com/aminrabinia/imagesegmentation-yolov8

Science Score: 10.0%

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Last synced: 6 months ago · JSON representation

Repository

Basic Info

Host: GitHub
Owner: aminrabinia
License: gpl-3.0
Language: Python
Default Branch: main
Size: 1.05 MB

Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 0

Created about 3 years ago · Last pushed about 3 years ago

https://github.com/aminrabinia/ImageSegmentation-Yolov8/blob/main/

## Updates in Latest Commit

Docker file changed to install ultralytics in runtime. 

Fuse bug fixed (calling async function). 

`model.predictor.annotator.result()` will return the annotated image without needing to change `predictor.py`. 

```
async def infer(myimg, conf=0.50):
    if myimg is None:
        return
    results = model.predict(myimg, device='cpu', conf=conf, save=True) 
    print(results)
    if len(results[0].numpy())==0:
        return myimg
    else:
        return model.predictor.annotator.result()
```

## Code Updated

need to modify `/yolo/engine/predictor.py`
```
    @smart_inference_mode()
    def __call__(self, source=None, model=None, verbose=False, stream=False):
        if stream:
            return self.stream_inference(source, model, verbose)
        else:
            _ = list(self.stream_inference(source, model, verbose))  # merge list of Result into one
            output_img = self.annotator.result()
            return output_img
```            
inside `stream_inference`:
```
  if self.args.show:
                    self.annotator = self.write_results(i, self.results, (p, im, im0))
```                    
Also `/yolo/v8/segment/predict.py`
```
def write_results(self, idx, results, batch):

...

if len(result) == 0:
            return self.annotator

....            
    # funtion last line
    return self.annotator
```

There is a bug when `fuse=True` in `model_setup`. I just set it as False to avoid fusing. 







  
    
      
  

[English](README.md) | [](README.zh-CN.md)




    
    
    
    

    
    
    
  
  


[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), developed by [Ultralytics](https://ultralytics.com),
is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces
new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and
easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image
classification tasks.

To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).




    
      
    
    
      
    
    
      
    
    
      
    
    
      
    
    
      
    
    
      
  



## Documentation

See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for
full documentation on training, validation, prediction and deployment.


Install

Pip install the ultralytics package including
all [requirements.txt](https://github.com/ultralytics/ultralytics/blob/main/requirements.txt) in a
[**3.10>=Python>=3.7**](https://www.python.org/) environment, including
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).

```bash
pip install ultralytics
```




Usage

#### CLI

YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:

```bash
yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
```

`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See the YOLOv8
[CLI Docs](https://docs.ultralytics.com/cli) for examples.

#### Python

YOLOv8 may also be used directly in a Python environment, and accepts the
same [arguments](https://docs.ultralytics.com/config/) as in the CLI example above:

```python
from ultralytics import YOLO

# Load a model
model = YOLO("yolov8n.yaml")  # build a new model from scratch
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)

# Use the model
results = model.train(data="coco128.yaml", epochs=3)  # train the model
results = model.val()  # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image
success = model.export(format="onnx")  # export the model to ONNX format
```

[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases). See
YOLOv8 [Python Docs](https://docs.ultralytics.com/python) for more examples.

#### Known Issues / TODOs

We are still working on several parts of YOLOv8! We aim to have these completed soon to bring the YOLOv8 feature set up
to par with YOLOv5, including export and inference to all the same formats. We are also writing a YOLOv8 paper which we
will submit to [arxiv.org](https://arxiv.org) once complete.

- [ ] TensorFlow exports
- [ ] DDP resume
- [ ] [arxiv.org](https://arxiv.org) paper



## Models

All YOLOv8 pretrained models are available here. Detection and Segmentation models are pretrained on the COCO dataset,
while Classification models are pretrained on the ImageNet dataset.

[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.

Detection

See [Detection Docs](https://docs.ultralytics.com/tasks/detection/) for usage examples with these models.

| Model                                                                                | size
^{(pixels) | mAP^{val
50-95 | Speed
^{CPU ONNX
(ms) | Speed
^{A100 TensorRT
(ms) | params
^{(M) | FLOPs
^{(B) |
| ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640                   | 37.3                 | 80.4                           | 0.99                                | 3.2                | 8.7               |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640                   | 44.9                 | 128.4                          | 1.20                                | 11.2               | 28.6              |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640                   | 50.2                 | 234.7                          | 1.83                                | 25.9               | 78.9              |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640                   | 52.9                 | 375.2                          | 2.39                                | 43.7               | 165.2             |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640                   | 53.9                 | 479.1                          | 3.53                                | 68.2               | 257.8             |

- **mAP^val** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
  
Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
  instance.
  
Reproduce by `yolo val detect data=coco128.yaml batch=1 device=0/cpu`}}}}}}

Segmentation

See [Segmentation Docs](https://docs.ultralytics.com/tasks/segmentation/) for usage examples with these models.

| Model                                                                                    | size
^{(pixels) | mAP^{box
50-95 | mAP^{mask
50-95 | Speed
^{CPU ONNX
(ms) | Speed
^{A100 TensorRT
(ms) | params
^{(M) | FLOPs
^{(B) |
| ---------------------------------------------------------------------------------------- | --------------------- | -------------------- | --------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-seg.pt) | 640                   | 36.7                 | 30.5                  | 96.1                           | 1.21                                | 3.4                | 12.6              |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-seg.pt) | 640                   | 44.6                 | 36.8                  | 155.7                          | 1.47                                | 11.8               | 42.6              |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-seg.pt) | 640                   | 49.9                 | 40.8                  | 317.0                          | 2.18                                | 27.3               | 110.2             |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-seg.pt) | 640                   | 52.3                 | 42.6                  | 572.4                          | 2.79                                | 46.0               | 220.5             |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-seg.pt) | 640                   | 53.4                 | 43.4                  | 712.1                          | 4.02                                | 71.8               | 344.1             |

- **mAP^val** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
  
Reproduce by `yolo val segment data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
  instance.
  
Reproduce by `yolo val segment data=coco128-seg.yaml batch=1 device=0/cpu`}}}}}}}

Classification

See [Classification Docs](https://docs.ultralytics.com/tasks/classification/) for usage examples with these models.

| Model                                                                                    | size
^{(pixels) | acc
^{top1 | acc
^{top5 | Speed
^{CPU ONNX
(ms) | Speed
^{A100 TensorRT
(ms) | params
^{(M) | FLOPs
^{(B) at 640 |
| ---------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-cls.pt) | 224                   | 66.6             | 87.0             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-cls.pt) | 224                   | 72.3             | 91.1             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-cls.pt) | 224                   | 76.4             | 93.2             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-cls.pt) | 224                   | 78.0             | 94.1             | 163.0                          | 0.87                                | 37.5               | 99.7                     |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-cls.pt) | 224                   | 78.4             | 94.3             | 232.0                          | 1.01                                | 57.4               | 154.8                    |

- **acc** values are model accuracies on the [ImageNet](https://www.image-net.org/) dataset validation set.
  
Reproduce by `yolo val classify data=path/to/ImageNet device=0`
- **Speed** averaged over ImageNet val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
  instance.
  
Reproduce by `yolo val classify data=path/to/ImageNet batch=1 device=0/cpu`}}}}}}}