metadata
license: mit
datasets:
- ds4sd/DocLayNet
language:
- en
metrics:
- accuracy
pipeline_tag: object-detection
How to Get Started with the Model
Install ultralytics YOLO package
$ pip install ultralytics
Perform Inference as per kurkurzz
from ultralytics import YOLO
from json import dumps
checkpoint_path = "path/to/model/weight.pt" # e.g weights/best.pt in this directory
model = YOLO(checkpoint_path)
image_path = "path/to/image"
infered = model(image_path)
results = infered[0]
boxes = result.boxes.data[:,:4]
confs = result.boxes.conf
clss = result.boxes.cls
class_name = result.names
#detected = results[0].boxes.xywh # or xywhn, xyxy pr xyxyn
detections = []
threshold = 0.3 # 0 < threshold <= 1
for box, conf, cls in zip(boxes, confs, clss):
label = class_name[int(cls)]
if conf >= threshold:
# must be in this format
detections.append({
'confidence': str(float(conf)),
'label': label,
'points': box.tolist(),
'type': 'rectangle',
})
detected_objects = dumps(detections)
print(detected_objects)