Regarding the output shape of your YOLOv8 detection model being `(1, 7, 8400)` for 3 classes, instead of perhaps what you might have expected, this is actually the **correct and expected raw output format** for YOLOv8 before post-processing.
Let's break down the meaning of this shape:
1: Represents the **batch size**. It's typically `1` for single-image inference.
7: This dimension contains all the relevant information for each prediction location. For a detection task with `3` classes, this `7` is the sum of the **prediction scores for the 3 classes** plus the **4 parameters for each bounding box** (`x`, `y`, `width`, `height`). Thus, `7 = 3 (number of classes) + 4 (bounding box parameters)`. Each of the `8400` locations outputs these `7` values.
8400: Represents the total number of **potential detection locations** considered across all output levels (different scales) by the model. YOLOv8 makes predictions on feature maps of different sizes, and `8400` is the flattened total count of these prediction locations.
Contrast this with the standard YOLOv8 detection model (trained on 80 COCO classes), whose raw detection output shape is typically (1, 84, 8400). Here, `84` also follows the same pattern: `80 (number of classes) + 4 (bounding box parameters) = 84`. This further confirms that the output dimension structure is "number of classes + 4".
This (1, 7, 8400) tensor is the raw prediction result generated by the YOLOv8 model after the network layers. It still needs to go through **post-processing steps**, such as confidence thresholding and Non-Maximum Suppression (NMS), to obtain the final list of detected bounding boxes (e.g., each detection including location, confidence, class ID, etc.). The final detection results you typically work with are the output after these post-processing steps, not this raw (1, 7, 8400) tensor itself.
Please note that within the YOLOv8 model family, the output shapes for different tasks (such as detection vs. segmentation) are different. For example, the output of a YOLOv8 segmentation model (like YOLOv8n-seg) might include a tensor with a shape like (1, 116, 8400) (combining classes, box parameters, and mask coefficients) and another output for prototype masks. This also illustrates that the output shape structure is determined by the specific task and configuration of the model.