Overview: The success of the deep detection model largely requires a large amount of data for training. Under the condition of fewer training samples, the model is easy to overfit and the detection effect is unsatisfactory. In view of the model that is easy to overfit and cause the target misdetection and missed detection in the absence of training samples, we present the Few-Shot Object Detection via the Online Inferential Calibration (FSOIC) framework by using the Faster R-CNN as detector. Through its excellent detection performance and powerful ability to distinguish the foreground and background, it effectively solves the problem that the single-stage detector cannot locate the target when the training samples are scarce. The bottom-layer features have a larger size and stronger location information, but the lack of global vision leads to weak semantic information, while the top-layer features are the opposite. To make full use of the sample information, the framework is designed to possess a new Attention-FPN network, which selectively the fuses features through modeling the dependencies between the feature channels, and directs the RPN module to extract the correct novel classes of the foreground objects by combined with the hierarchical freezing learning mechanism. The channel attention mechanism compresses the feature map and spreads it into a one-dimensional vector for sigmoid through two fully connected layers. The weight is generated for each feature channel, and the correlation between each channel is established. The weight of the input features is allocated according to the category, and the dependence relationship between each channel is modeled. Due to the closed nature of the neural network, simple feature fusion is uncertain, and it is difficult to fuse the feature map in a satisfactory direction. To the imbalanced sample features, the candidate targets of the new class are scored too low and filtered in the selection of the prediction box, resulting in false detection and missed detection of the detector. We designed the online calibration module that segmentes and encodes the samples, scored the re-weighted the multiple candidate objects, and corrected the misdetected and missed predicted objects. The performance of our detection algorithm performs better than most comparisons. The experimental results in the VOC Novel Set 1 show that the proposed method improves the average nAP50 of the five tasks by 10.16% and performs better than most comparisons.Considering that the model is easy to overfit and cause the target misdetection and missed detection under the condition of few samples, this paper propose the few-shot object detection via the online inferential calibration (FSOIC) based on the two-stage fine-tuning approach (TFA). In this framework, a novel Attention-FPN network is designed to selectively fuse the features by modeling the dependencies between the feature channels, and direct the RPN module to extract the correct novel classes of the foreground objects in combination with the hierarchical freezing learning mechanism. At the same time, the online calibration module is constructed to encode and segment the samples, reweight the scores of multiple candidate objects, and correct misclassifying and missing objects. The experimental results in the VOC Novel Set 1 show that the proposed method improves the average nAP50 of the five tasks by 10.16% and performs better than most comparisons.