# Train data path | 设置训练用模型、图片 pretrained_model="./sd-models/down.safetensors" # base model path | 底模路径 is_v2_model=0 # SD2.0 model | SD2.0模型 2.0模型下 clip_skip 默认无效 v_parameterization=0 # parameterization | 参数化 v2 非512基础分辨率版本必须使用。 train_data...
python caffe2/python/examples/resnet50_trainer.py --train_data <path> --test_data <path> --num-gpus <int> --batch-size <int> --dtype float16 --enable-tensor-core --cudnn_workspace_limit_mb 1024 --image_size 224 For more information about the additional command-line arguments, issue...
此外,为了对抗过拟合,提升泛化性,还需要引入合适的正则化方法,如Dropout,BatchNorm,L2-Regularization,Data Augmentation等。有些提升泛化性能的方法可以直接在train组件中实现(如添加L2-Reg,Mixup),有些则需要添加进model中(如Dropout与Batch...
"""Train a YOLOv5 model on a custom dataset在数据集上训练 yolo v5 模型Usage:$ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640训练数据为coco128 coco128数据集中有128张图片 80个类别,是规模较小的数据集""" 这里是开头作者注释的一个部分,意在说明一些项目基本情况。
--dataset_path $OUTPUT_DATASET_PATH --data_processor lm --seq_length 512 其中,--seq_length 用来指定文本长度,越长则训练时消耗的GPU内存越多。 启动增量预训练,使用DeepSpeed ZeRO3 进行加速 代码语言:txt AI代码解释 deepspeed pretrain.py --deepspeed --deepspeed_config models/deepspeed_zero3_config....
data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() loss = criterion(outputs, labels) val_loss += loss.cpu().numpy() val_steps += 1 with tune.checkpoint_dir(epoch) as checkpoint_dir: path = os.path.join(checkpoint_dir, "checkpoint") torch.save((...
deepspeed main.py \ --data_path Dahoas/rm-static Dahoas/full-hh-rlhf Dahoas/synthetic-instruct-gptj-pairwise yitingxie/rlhf-reward-datasets \ --data_split 2,4,4 \ --model_name_or_path meta-llama/Llama-2-7b-hf \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size...
parser.add_argument('--weights',type=str,default='yolov5s.pt',help='initial weights path') parser.add_argument('--cfg',type=str,default='',help='model.yaml path') parser.add_argument('--data',type=str,default='data/coco128.yaml',help='data.yaml path') ...
...先看一下最开始投毒的逻辑,在 PathGeometryWrapper.cpp 定义的 GetStartPoint 方法,返回了本文使用的代码里面传入的包含 NaN 的点的值,如以下代码,拿到的 m_pFigure...->StartPoint 的值是不符合预期的 {X=18.000000000000000 Y=-nan(ind) } 值 const MilPoint2F &PathFigureData::GetStartPoint...__...
Training episode data, specified as an: rlTrainingResult object, when training a single agent. Array of rlTrainingResult objects when training multiple agents. Use this argument to resume training from the exact point at which it stopped. This starts the training from the last values of the age...