The checkpoint state_dict contains keys that are not used by the model:

[rabahMoulai]

When loading the faster-rcnn configs from detectron config file

PS C:\Users\rabah.moulai\Desktop\Detectron_faster_Rcnn> & C:/Users/rabah.moulai/Anaconda3/envs/detectron_env/python.exe c:/Users/rabah.moulai/Desktop/Detectron_faster_Rcnn/run.py
Some model parameters or buffers are not found in the checkpoint:
backbone.fpn_lateral2.{bias, weight}
backbone.fpn_lateral3.{bias, weight}
backbone.fpn_lateral4.{bias, weight}
backbone.fpn_lateral5.{bias, weight}
backbone.fpn_output2.{bias, weight}
backbone.fpn_output3.{bias, weight}
backbone.fpn_output4.{bias, weight}
backbone.fpn_output5.{bias, weight}
proposal_generator.rpn_head.anchor_deltas.{bias, weight}
proposal_generator.rpn_head.conv.{bias, weight}
proposal_generator.rpn_head.objectness_logits.{bias, weight}
roi_heads.box_head.fc1.{bias, weight}
roi_heads.box_head.fc2.{bias, weight}
roi_heads.box_predictor.bbox_pred.{bias, weight}
roi_heads.box_predictor.cls_score.{bias, weight}
The checkpoint state_dict contains keys that are not used by the model:
fc1000.{bias, weight}
C:\Users\rabah.moulai\Anaconda3\envs\detectron_env\lib\site-packages\torch\functional.py:445: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\Users\builder\tkoch\workspace\pytorch\pytorch_1647970138273\work\aten\src\ATen\native\TensorShape.cpp:2157.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]

my config setup

#load model config and pretrained model 
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.data import MetadataCatalog
from detectron2.utils.visualizer import ColorMode, Visualizer
from detectron2 import model_zoo

import cv2
import numpy as np

class Detector :
    def __init__(self):
        self.cfg=get_cfg()

        
        self.cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml"))
        self.cfg.MODEL.WEIGHS=model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml")
        
        self.cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST=0.7
        self.cfg.MODEL.DEVICE="cpu"
        self.predictor=DefaultPredictor(self.cfg)

    def onImage(self,imagePath):
        image=cv2.imread(imagePath)
        predictions=self.predictor(image)
        viz = Visualizer(image[:, :, ::-1], MetadataCatalog.get(self.cfg.DATASETS.TRAIN[0]), scale=1.2)

        # viz=Visualizer(image[:,:,::1], metadata= MetadataCatalog.get(self.cfg.DATASETS.TRAIN[0]),
        # instance_mode=ColorMode.IMAGE_BW)

        output=viz.draw_instance_predictions(predictions["instances"].to("cpu"))

        cv2.imshow("Result",output.get_image()[:,:,::-1])
        cv2.waitKey(0)

  from Detectron import *

detector= Detector()
detector.onImage("Des-policiers.jpg")

[rabahMoulai]

when I run the program with an image as input, I have the same image as output (without any detection) neither boundinx box nor classes name