更多模型训练参数介绍可参见文档paddlex.cls.MobileNetV3_large_ssld,在如下代码中,模型训练过程每间隔save_interval_epochs轮会保存一次模型在save_dir目录下,同时在保存的过程中也会在验证数据集上计算相关指标,模型训练过程中相关日志的含义可参见文档 num_classes = len(train_dataset.labels) model = pdx.cls....
MobileNetV3是在MobileNetV2的基础上提出的网络,其计算量小、参数少,相比其他轻量级网络,依然取得了较好的成绩;同时以ResNeXt101_32x16d_wsl为teacher模型,运用SSLD(简单的半监督标签知识蒸馏)方式蒸馏出MobileNetV3_large模型,作为预训练模型;相对比原有的MobileNetV3预训练模型,在参数量不变的情况下,MobileNetV3_ssld...
2021-08-16 18:23:00 [INFO] Loading pretrained model from output/yolov3_mobilenet/pretrain/MobileNetV3_large_x1_0_ssld_pretrained.pdparams2021-08-16 18:23:00 [WARNING] neck.yolo_block.0.conv_module.conv0.conv.weight is not in pretrained model2021-08-16 18:23:00 [WARNING] neck.yolo_...
4.2 目标检测 对于目标检测任务,使用PaddleDection3开发的轻量级PicoDet作为Baseline方法。表4显示了PP-LCNet和MobileNetV3为Backbone的目标检测结果。与MobileNetV3相比,PP-LCNet大大提高了COCO上的mAP和推理速度。 4.3 语义分割 使用MobileNetV3作为Backbone进行比较。如表5所示,PP-LCNet-0.5x表现更好MobileNetV3-large-...
对于目标检测任务,使用PaddleDection3开发的轻量级PicoDet作为Baseline方法。表4显示了PP-LCNet和MobileNetV3为Backbone的目标检测结果。与MobileNetV3相比,PP-LCNet大大提高了COCO上的mAP和推理速度。 4.3 语义分割 使用MobileNetV3作为Backbone进行比较。如表5所示,PP-LCNet-0.5x表现更好MobileNetV3-large-0.5x在mIoU上...
10分钟快速上手使用PaddleX——MobileNetV3_small_ssld 图像分类石头分类 PaddleX简介:PaddleX是飞桨全流程开发工具,集飞桨核心框架、模型库、工具及组件等深度学习开发所需全部能力于一身,打通深度学习开发全流程,并提供简明易懂的Python API,方便用户根据实际生产需求进行直接调用或二次开发,为开发者提供飞桨全流程开...
mobilenetv3_large_100.ra4_e3600_r224_in1k - 77.16 @ 256, 76.31 @ 224 Aug 21, 2024 Updated SBB ViT models trained on ImageNet-12k and fine-tuned on ImageNet-1k, challenging quite a number of much larger, slower models modeltop1top5param_countimg_size vit_mediumd_patch16_reg4_gap_...
mobilenetv4_hybrid_large.ix_e600_r384_in1k 83.990 16.010 96.702 3.298 37.76 384 mobilenetv4_hybrid_medium.ix_e550_r384_in1k 83.394 16.606 96.760 3.240 11.07 448 mobilenetv4_hybrid_medium.ix_e550_r384_in1k 82.968 17.032 96.474 3.526 11.07 384 mobilenetv4_hybrid_medium.ix_e550_r256_in1k 82.49...
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288 - 86.5 @ 288, 86.7 @ 320 March 31, 2023 Add first ConvNext-XXLarge CLIP -> IN-1k fine-tune and IN-12k intermediate fine-tunes for convnext-base/large CLIP models. modeltop1top5img_sizeparam_countgmacsmacts convnext_xxlarge.clip_laion...
'Bag of Tricks' / Gluon C, D, E, S variations - https://arxiv.org/abs/1812.01187 Weakly-supervised (WSL) Instagram pretrained / ImageNet tuned ResNeXt101 - https://arxiv.org/abs/1805.00932 Semi-supervised (SSL) / Semi-weakly Supervised (SWSL) ResNet/ResNeXts - https://arxiv.org/...