YOLO NAS报错：'ImageDetectionPrediction'对象不可迭代

报错原因是单张图片预测时，best_model.predict()返回的是单个ImageDetectionPrediction对象，而非可迭代的列表，用for循环遍历单个对象自然会触发TypeError。之前能运行可能是依赖库版本更新，或是之前传入的是图片列表而非单张图片路径。

两种修改方式任选其一即可：

方式1：直接使用单个预测结果（推荐，针对单张图片场景） #

去掉外层for循环，直接操作返回的预测对象：

img_url='/content/Retail-Store-1/valid/images/-10_jpg.rf.d5119996f5715cf1105b1e5bf01c0ced.jpg'

image_prediction = best_model.predict(img_url)
class_names = image_prediction.class_names
labels = image_prediction.prediction.labels
confidence = image_prediction.prediction.confidence
bboxes = image_prediction.prediction.bboxes_xyxy

for i, (label, conf, bbox) in enumerate(zip(labels, confidence, bboxes)):
    print("prediction: ", i)
    print("label_id: ", label)
    print("label_name: ", class_names[int(label)])
    print("confidence: ", conf)
    print("bbox: ", bbox)
    print("--" * 10)

方式2：包装成列表（兼容单张/多张图片场景） #

如果需要保持原有代码结构，同时兼容后续传入多张图片的情况，手动将预测结果放入列表：

img_url='/content/Retail-Store-1/valid/images/-10_jpg.rf.d5119996f5715cf1105b1e5bf01c0ced.jpg'

images_predictions = [best_model.predict(img_url)]  # 用列表包裹单个结果
for image_prediction in images_predictions:
    class_names = image_prediction.class_names
    labels = image_prediction.prediction.labels
    confidence = image_prediction.prediction.confidence
    bboxes = image_prediction.prediction.bboxes_xyxy

    for i, (label, conf, bbox) in enumerate(zip(labels, confidence, bboxes)):
        print("prediction: ", i)
        print("label_id: ", label)
        print("label_name: ", class_names[int(label)])
        print("confidence: ", conf)
        print("bbox: ", bbox)
        print("--" * 10)

内容的提问来源于stack exchange，提问作者XYZ