SOTA 生成性能:在 MAETok 训练的扩散模型(675M 参数,128 token)在 256×256 ImageNet 生成任务上达到了与先前最佳模型相当的性能,并在 512 分辨率上超越了 2B USiT,取得了 1.69 gFID 和 304.2 IS。 结果展示 总结速览 解决的问题 现有的扩散模型通常使用变分自编码器(VAE)作为tokenizer,但VAE的变分约束可能限...
目标检测和实例分割:作者使用了一个Mask R-CNN检测头,在1 \times的调度下进行训练,来评估在COCO数据集上的ImageNet-1K预训练TransNeXt在目标检测和实例分割方面的性能。实验结果如图1所示。与先前的最先进模型相比,作者的模型在各方面都具有全面的优越性。值得注意的是,即使作者的微型模型在A P^b方面也超过了Focal...
转载自CSDN博客 本月1日起,上海正式开始了“史上最严“垃圾分类的规定,扔错垃圾最高可罚200元。全国其它46个城市也要陆续步入垃圾分类新时代。各种被垃圾分类逼疯的段子在社交媒体上层出不穷。 其实从人工智能的角度看垃圾分类就是图像处理中图像分类任务的一种应用,而这在2012年以来的ImageNet图像分类任务的评比...
(512, (3,3), padding='same', activation='relu')(conv11) conv13 = Conv2D(512, (3,3), padding='same', activation='relu')(conv12) pool5 = MaxPooling2D(pool_size=2)(conv13) # 扁平层 flat = Flatten()(pool5) # 全联接层 fc1 = Dense(4096, activation='relu')(flat) fc2 = ...
512, and 1024, respectively. Then, we downsample the high-resolution representations by a 2-strided 3x3 convolution outputting 256 channels and add them to the representations of the second-high-resolution representations. This process is repeated two times to get 1024 channels over the small resol...
Train:In each iteration of training, images are resized to have 256 pixel at their smaller dimension and then a random crop of\(224\times 224\)is selected for training. We run the training algorithm for 16 epochs with batche size equal to 512. We use negative-log-likelihood over the sof...
tb.log_trn_times(timer.batch_time.val, timer.data_time.val, input.size(0)) tb.log_trn_loss(losses.val, top1.val, top5.val) recv_gbit, transmit_gbit = net_meter.update_bandwidth() tb.log("sizes/batch_total", batch_total) tb.log('net/recv_gbit', recv_gbit) tb.log('net/...
However, larger networks and largerdatasets result in longer training times that impede re-search and development progress. Distributed synchronousSGD offers a potential solution to this problem by dividingSGD minibatches over a pool of parallel ... ...
输入被分成 N 个时空patch,每个块的大小为t \times h \times w \times 3。 Omnivorous visual encoder 作者使用统一视觉编码器,它使用相同的参数处理图像和视频。编码器对来自图像和视频的 N 个时空patch进行操作。编码器可以自然地处理来自图像和视频的可变数量 N 个patch,因为它使用了 Transformer 架构。编码器...
在极端情况下，本文所提架构可视作一种特殊CNN，它采用1\times 1卷积进行channel mixing，全感受野、...