
<div align="center">
  <img width="640" alt="uisikdag/yapimakine" src="https://huggingface.co/uisikdag/yapimakine/resolve/main/sample_visuals.jpg">
   Dataset<br> <a href="https://universe.roboflow.com/kfu-ye4kz/construction-management">Link</a>
</div>

### How to use

- Install [yolov5](https://github.com/fcakyon/yolov5-pip):

```bash
pip install -U yolov5
```

- Load model and perform prediction:

```python
import yolov5

# load model
model = yolov5.load('uisikdag/yapimakine')
  
# set model parameters
model.conf = 0.25  # NMS confidence threshold
model.iou = 0.45  # NMS IoU threshold
model.agnostic = False  # NMS class-agnostic
model.multi_label = False  # NMS multiple labels per box
model.max_det = 1000  # maximum number of detections per image

# set image
img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model(img, size=640)

# inference with test time augmentation
results = model(img, augment=True)

# parse results
predictions = results.pred[0]
boxes = predictions[:, :4] # x1, y1, x2, y2
scores = predictions[:, 4]
categories = predictions[:, 5]

# show detection bounding boxes on image
results.show()

# save results into "results/" folder
results.save(save_dir='results/')
```

- Finetune the model on your custom dataset:

```bash
yolov5 train --data data.yaml --img 640 --batch 16 --weights uisikdag/yapimakine --epochs 10
```

**More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)**

{"library_name": "yolov5", "tags": ["yolov5", "yolo", "vision", "object-detection", "pytorch"], "library_version": "7.0.7", "inference": false}
yolov5 tensorboard yolo vision object-detection pytorch model-index region:us
TAGS
#yolov5 #tensorboard #yolo #vision #object-detection #pytorch #model-index #region-us 


<div align="center">
  <img width="640" alt="uisikdag/yapimakine" src="URL
   Dataset<br> <a href="URL
</div>

### How to use

- Install yolov5:



- Load model and perform prediction:



- Finetune the model on your custom dataset:



More models available at: awesome-yolov5-models

### How to use

- Install yolov5:



- Load model and perform prediction:



- Finetune the model on your custom dataset:



More models available at: awesome-yolov5-models
TAGS
#yolov5 #tensorboard #yolo #vision #object-detection #pytorch #model-index #region-us 
 ### How to use

- Install yolov5:



- Load model and perform prediction:



- Finetune the model on your custom dataset:



More models available at: awesome-yolov5-models
TAGS
#yolov5 #tensorboard #yolo #vision #object-detection #pytorch #model-index #region-us 
### How to use

- Install yolov5:



- Load model and perform prediction:



- Finetune the model on your custom dataset:



More models available at: awesome-yolov5-models