--- tags: - yolov5 - yolo - vision - object-detection - pytorch library_name: yolov5 library_version: 7.0.6 inference: false datasets: - keremberke/forklift-object-detection model-index: - name: keremberke/yolov5m-forklift results: - task: type: object-detection dataset: type: keremberke/forklift-object-detection name: keremberke/forklift-object-detection split: validation metrics: - type: precision # since mAP@0.5 is not available on hf.co/metrics value: 0.8515819366709647 # min: 0.0 - max: 1.0 name: mAP@0.5 ---
keremberke/yolov5m-forklift
### 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('keremberke/yolov5m-forklift') # 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 keremberke/yolov5m-forklift --epochs 10 ``` **More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)**