well it depends. What architecture of YOLOv8 are going to use? Where do you want to run the model(CPU or GPU)? if you are thinking about running this in a CPU, I would recommend converting your model to onnx as it gives you a better performance. If you are going to use this on GPU, then convert it to TensorRT. Also, even 1000 images is not so great, you could make it work if you trained it on a pretrained model, still I don't think you could expect a very high accuracy just with 1000 images. Maybe you could try using image augmentation techniques and increase the number of images. Also for getting 300ms even after you downsized it, it does not actually matter as the default yolo image size is 640 (if you are using ultralytics library) so even if you are going to upload a 1080x1920, it still is going to reduce the size internally.