I have a custom trained detectron2 model for instance segmentation that I load and use for inference as below:

cfg = get_cfg()
cfg.merge_from_file(config_path)
cfg.MODEL.WEIGHTS = weights_path
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.7
cfg.MODEL.DEVICE='cpu'

predictor = DefaultPredictor(cfg)
outputs = predictor(im)

I have followed this tutorial from OpenVino in order to convert it to an OV model:

model = build_model(cfg)
DetectionCheckpointer(model).load(cfg.MODEL.WEIGHTS)
model.eval()

ov_model = convert_detectron2_model(model, im)

core = ov.Core()
ov_model = core.read_model("../model/model.xml")
compiled_model = ov.compile_model(ov_model)
results = compiled_model(sample_input[0]["image"])

However, I don’t obtain the expected result from the complied ov model, ie. I normally should obtain one instance in the outputs and this is the case when using the classic detectron2 model, but when using the complied ov model, I don’t get any instance detected.

Instances(num_instances=0, image_height=3024, image_width=4032, fields=[pred_boxes: Boxes(tensor([], size=(0, 4))), scores: [], pred_classes: [], pred_masks: tensor([], size=(0, 3024, 4032), dtype=torch.bool)])

Moreover, when looking at the ov model, I’m quite confused about how it looks like as it is very different from the original model.

<Model: 'Model4'
inputs[
<ConstOutput: names[args] shape[?,?,?] type: u8>
]
outputs[
<ConstOutput: names[tensor, 2193, 2195, 2190, 2188, 2172, 2167] shape[..100,4] type: f32>,
<ConstOutput: names[] shape[..100] type: i64>,
<ConstOutput: names[] shape[?,1,28,28] type: f32>,
<ConstOutput: names[] shape[..100] type: f32>,
<ConstOutput: names[image_size] shape[2] type: i64>
]>