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数据集地址

数据集包含 360 张红血细胞图像及其注释文件，分为训练集与验证集。训练文件夹包含 300 张带有注释的图像。测试和验证文件夹都包含 60 张带有注释的图像。我们对原始数据集进行了一些修改以准备此 CBC 数据集，并将数据集分成三部分。在360张涂片图像中，首先使用300张带注释的血细胞图像作为训练集，然后将其余60张带有注释的图像用作测试集。CBC数据集地址如下：

https://github.com/MahmudulAlam/Complete-Blood-Cell-Count-Dataset

模型训练

准备好数据集以后，直接按下面的命令行运行即可：

yolo train model=yolo11n.pt data=cbc_dataset.yaml epochs=25 imgsz=640 batch=4

模型导出与测试

yolo export model=cbc_11.pt format=onnx
yolo predict model=cbc_11.pt source=./cbc

部署推理

转成ONNX格式文件以后，基于OpenVINO-Python部署推理，相关代码如下

frame = cv.imread(os.path.join("D:/cbc_analysis/data/", f))
bgr = format_yolov8(frame)
img_h, img_w, img_c = bgr.shape
​
start = time.time()
image = cv.dnn.blobFromImage(bgr, 1 / 255.0, (640, 640), swapRB=True, crop=False)
​
res = compiled_model([image])[output_layer] # 1x84x8400
rows = np.squeeze(res, 0).T
class_ids = []
confidences = []
boxes = []
x_factor = img_w / 640
y_factor = img_h / 640
​
​
for r in range(rows.shape[0]):
row = rows[r]
classes_scores = row[4:]
_, _, _, max_indx = cv.minMaxLoc(classes_scores)
class_id = max_indx[1]
if (classes_scores[class_id] > .25):
confidences.append(classes_scores[class_id])
class_ids.append(class_id)
x, y, w, h = row[0].item(), row[1].item(), row[2].item(), row[3].item()
left = int((x - 0.5 * w) * x_factor)
top = int((y - 0.5 * h) * y_factor)
width = int(w * x_factor)
height = int(h * y_factor)
box = np.array([left, top, width, height])
boxes.append(box)
​
indexes = cv.dnn.NMSBoxes(boxes, confidences, 0.25, 0.45)
for index in indexes:
box = boxes[index]
color = colors[int(class_ids[index]) % len(colors)]
rr = int((box[2] + box[3])/4)
cv.circle(frame, (box[0]+int(box[2]/2), box[1]+int(box[3]/2)), rr-4, color, 2)
cv.putText(frame, class_list[class_ids[index]], (box[0] + int(box[2] / 2), box[1] + int(box[3] / 2)),cv.FONT_HERSHEY_SIMPLEX, .5, (0, 0, 0))
cv.putText(frame, "gloomyfish@2024", (20, 45), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
​
cv.imshow("YOLO11+OpenVINO2024 RBC(Red Blood Cell) Count", frame)
cv.imwrite("D:/rbc_result.jpg", frame)
cv.waitKey(0)

总结

其实YOLO11 只是 YOLOv8的套娃版本，只要掌握YOLOv8，真的只需要五分钟就可以全面掌握YOLO11各种模型从训练到部署。玩转YOLOv8的分类、对象检测、实例分割、姿态评估、OBB检测、目标跟踪 六大模块