TF2-Base | old Team Fortress 2 source code ported to Source SDK | SDK library

Since you don't need the object localization, stick to classification only.

Although you will be tempted to use the standard off-the-shelf network of multi-class multi-label object detection because of its re-usability, but realize that you are asking the model to do more things. If you have tons of data - not a problem. Or if your objects are similar to the ones used in ImageNet/COCO etc, you can simply use standard off-the-shelf object detection architecture and fine-tune on your dataset.

However, if you have less data and you need to train from scratch (e.g. medical images, weird objects), then object detection will be an overkill and will give you inferior results.

Remember, most of the object detection networks re-cycle the classification architectures with modifications added to last layers to incorporate additional outputs for object detection coordinates. There is a loss function associated with those additional outputs. During training in order to get best loss value, some of the classification accuracy is compromised for the sake of getting better object localization coordinates. You don't need that compromise. So, you can modify the last layer of object detection network and remove the outputs for coordinates.

Again, all this hassle is worth only if you have less data and you really need to train from scratch.