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[ECCV2024] Official implementation of Crowd-SAM: SAM as a Smart Annotator for Object Detection in Crowded Scenes - CrowdSAM/train_nclass.py at main · FelixCaae/CrowdSAM
train_examples = len(train_dataset) train_dataloader = \ BertDataLoader(train_dataset, mode="train", max_len=args.max_len, batch_size=args.batch_size, num_workers=4, shuffle=True) dev_dataloader = \ BertDataLoader(dev_dataset, mode="dev", max_len=args.max_len, batch_size=args.batch_...
class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights # 获取类别的名字,然后将分类标签保存至模型 model.names = names 这段代码主要是根据自己数据集的类别数设置分类损失的系数,位置损失的系数。设置类别数,超参数等操作 其中, box: 预测框的损失 cls: ...
@TrainerBase.register("default")classTrainer(TrainerBase):def__init__(self,model:Model,optimizer:torch.optim.Optimizer,iterator:DataIterator,train_dataset:Iterable[Instance],validation_dataset:Optional[Iterable[Instance]]=None,patience:Optional[int]=None,validation_metric:str="-loss",validation_iterator:...
imdb = get_imdb(imdb_name) ##重新获取imdb数据 print 'Loaded dataset `{:s}` for proposal generation'.format(imdb.name) # Load RPN and configure output directory rpn_net = caffe.Net(rpn_test_prototxt, rpn_model_path, caffe.TEST) ##加载rpn_test_prototxt= ##'py-faster-rcnn/models/...
['cls'] *= nc /80.# scale coco-tuned hyp['cls'] to current datasetmodel.nc = nc# attach number of classes to modelmodel.hyp = hyp# attach hyperparameters to modelmodel.gr =1.0# giou loss ratio (obj_loss = 1.0 or giou)model.class_weights = labels_to_class_weights(dataset.labels...
Computing the mAP on test dataset Conclusion… and a bit about the naming saga We begin by cloning the YOLO v5 repository and setting up the dependencies required to run YOLO v5. You might needsudorights to install some of the packages. ...
prepare_question_classifier_dataset(file_path) self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=test_size, random_state=42) return self.question_classifier.train(self.X_train, self.y_train) ...
trainset_rdd = dataset_rdd.subtract(testset_rdd) trainset = trainset_rdd.map(lambdae:LabeledPoint(e[1],e[2:])) testset = testset_rdd.map(lambdae:LabeledPoint(e[1],e[2:])) model = GradientBoostedTrees.trainClassifier(trainset, {}, numIterations=IterNums,learningRate = rate)#model...