作者使用具有不同参数量的Vision Transformer和Swin Transformer,ViT-S/16、ViT-B/16、ViT-L/16和SwinT/ {7,14} 作为主干网络f。默认情况下,作者使用AdamW优化器和1024的batch size在ImageNet-1K训练集上预训练iBOT。作者预训练了ViT-...
作者使用具有不同参数量的Vision Transformer和Swin Transformer,ViT-S/16、ViT-B/16、ViT-L/16和SwinT/ {7,14} 作为主干网络f。默认情况下,作者使用AdamW优化器和1024的batch size在ImageNet-1K训练集上预训练iBOT。作者预训练了ViT-S/16 800个epoch,ViT-B/16 400个epoch,Swin-T/ { 7,14 } 300个epoch。
作者使用具有不同参数量的Vision Transformer和Swin Transformer,ViT-S/16、ViT-B/16、ViT-L/16和SwinT/ {7,14} 作为主干网络f。默认情况下,作者使用AdamW优化器和1024的batch size在ImageNet-1K训练集上预训练iBOT。作者预训练了ViT-S/16 800个epoch,ViT-B/16 400个epoch,Swin-T/ { 7,14 } 300个epoch。
source_val_dir = os.path.join(SOURCE_PATH,"val") output_train_dir = os.path.join(TARGET_SIZE,"train") output_val_dir = os.path.join(TARGET_SIZE,"val") os.makedirs(output_train_dir, exist_ok=True) os.makedirs(output_val_dir, exist_ok=True) forclsinsubset_classes: os.symlink(os....
首先,我修改了您的batch_size的赋值语句,因为它会报错;同时,我还使用了推荐的torchrun,应该之前的已经推荐废弃了。我的训练脚本如下: #!/bin/bash DATA_PATH=/ai/dataset/imagenet ALL_BATCH_SIZE=1024 NUM_GPU=2 GRAD_ACCUM_STEPS=4 # Adjust according to your GPU numbers and memory size. ...
During inference, the image size for ADE20K val and Cityscapes val is set to 512×2048 and 1024×2048, respectively. We do inference on Cityscapes with sliding window test by cropping 1024×1024 patches. B. More experiments B.1. Different block assignment strategies Block Assignment on...
batch_size_per_gpu: 8 student: block_chunks: 4 ``` I have already created this .yaml in this repo, so we can just copy it over to the configs folder in the DinoV2 repo: Remember to first, change the path to the root and extra folders in the YAML, unless they are actually at ...
The parameter-free identity shortcuts are particularly important for the bottleneck architectures. If the identity shortcut is replaced with projection, one can show that the time complexity and model size are doubled, as the shortcut is connected to the two high-dimensional ends. So identity sho...
默认情况下,作者使用AdamW优化器和1024的batch size在ImageNet-1K训练集上预训练iBOT。作者预训练了ViT-S/16 800个epoch,ViT-B/16 400个epoch,Swin-T/ { 7,14 } 300个epoch。 ▊4.实验 4.1 ImageNet-1K上的分类 k-NN and Linear Probing 为了评估预训练特征的质量,作者在冻结表示上使用k-近邻分类器(k...
--token-label--token-label-size14--token-label-data/path/to/token_label_data 对于MNMG 训练案例,它需要将训练集群详细信息作为命令行输入的一部分。首先,我们根据节点和集群架构设置 CPU 、 MEM 、 IB 绑定。预训练阶段的集群是 DGX A100 POD ,每个 CPU 插槽有四个 NUMA 域,每个 A100 GPU 有一个 IB...