def evaluate(self): efficientdetPredict = EfficientdetPredict(os.path.join(self.OUTPUT_PATH,self.DATASET_NAME,"models","efficientdet" + str(self.model) + '_' + self.DATASET_NAME,'pascalCustom_30.h5'), os.path.join(self.OUTPUT_PATH,self.DATASET_NAME, self.DATASET_NAME + "_classes.csv"), self.model) map = Map(efficientdetPredict, self.DATASET_NAME, os.path.join(self.OUTPUT_PATH, self.DATASET_NAME), self.model) map.evaluate()
def evaluate(self): tensorflowPredict = TensorflowPredict(os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "models", self.model + "_" + self.DATASET_NAME + "_final.params"), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "annotations", "label_map.pbtxt"), self.model) map = Map(tensorflowPredict, self.DATASET_NAME, os.path.join(self.OUTPUT_PATH, self.DATASET_NAME), self.model) map.evaluate()
def evaluate(self): # yoloPredict = DarknetPredict(imagePaths,modelWeights,classesFile,modelConfiguration) yoloPredict = DarknetPredict( os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "models", self.DATASET_NAME+"_"+self.model +"train_final.weights"), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "classes.names"), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME,self.DATASET_NAME+"_"+self.model + "train.cfg")) map = Map(yoloPredict, self.DATASET_NAME,os.path.join(self.OUTPUT_PATH, self.DATASET_NAME), self.model) map.evaluate()
def evaluate(self): rcnnPredict = RCNNPredict( os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "models", "mask_rcnn_" + self.DATASET_NAME.lower() + "_0005.h5"), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "classes.names")) map = Map(rcnnPredict, self.DATASET_NAME, os.path.join(self.OUTPUT_PATH, self.DATASET_NAME), self.model) map.evaluate()
def evaluate(self): # yoloPredict = DarknetPredict(imagePaths,modelWeights,classesFile,modelConfiguration) retinanetPredict = RetinanetPredictor( os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "models", "output.h5"), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "classes.names")) map = Map(retinanetPredict, self.DATASET_NAME, os.path.join(self.OUTPUT_PATH, self.DATASET_NAME), self.model) map.evaluate()
def evaluate(self): predictor = mmdetectionPredict( os.path.join('./work_dirs/' + self.model + '/latest.pth'), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, self.DATASET_NAME + "_classes.csv"), self.model, self.model + self.DATASET_NAME) map = Map(predictor, self.DATASET_NAME, os.path.join(self.OUTPUT_PATH, self.DATASET_NAME), self.model) map.evaluate()
def evaluate(self): mxnetPredict = MxNetPredict( os.path.join( self.OUTPUT_PATH, self.DATASET_NAME, "models", self.model + "_" + self.DATASET_NAME + "_final.params"), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "classes.names"), self.model) map = Map(mxnetPredict, self.DATASET_NAME, os.path.join(self.OUTPUT_PATH, self.DATASET_NAME), self.model) map.evaluate()
def evaluate(self): fcosPredict = FcosPredict( os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "models", "fcos_" + str(self.model) + '_' + self.DATASET_NAME, str(self.model) + '_pascalCustom_25.h5'), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, self.DATASET_NAME + "_classes.csv"), self.model) map = Map(fcosPredict, self.DATASET_NAME, os.path.join(self.OUTPUT_PATH, self.DATASET_NAME), self.model) map.evaluate()
def evaluate(self): tinyyoloPredict = DarknetPredict( os.path.join( self.OUTPUT_PATH, self.DATASET_NAME, "models", self.DATASET_NAME + "_" + self.model + "_final.weights"), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME, "classes.names"), os.path.join(self.OUTPUT_PATH, self.DATASET_NAME + "_" + self.model, self.DATASET_NAME + "_" + self.model + ".cfg")) map = Map(tinyyoloPredict, self.DATASET_NAME, os.path.join(self.OUTPUT_PATH, self.DATASET_NAME), self.model) map.evaluate()