revisiting unreasonable effectiveness of data indeep learning era
Each branch is an atrous convolutional layer that predicts a sub-sampled pixel-wise class probabilities. Please also note that there is no way of submitting missing references or citation data directly to dblp. We see that the JFT-300M checkpoint outperforms ImageNet by 1.7% points. Yet, while a tremendous amount of time is spent on engineering and parameter sweeps; little to no time has been spent collectively on data.
Add a list of citing articles from and to record detail pages. performance of ImageNet checkpoints improves faster than JFT-300M with respect to the number of epochs.
We train the fully-convolutional pose detector [29] by initializing the base ResNet model with our checkpoints and fine-tuning. This work would not have been possible without the heroic efforts of Image Understanding and Expander teams at Google who built the massive JFT dataset. We evaluate our learned representation on a variety of vision tasks: image classification, object detection, semantic segmentation and human pose estimation.
share. , we report quantitative results on the VOC 2012 segmentation validation set for all classes (refer to Figure 5 (left), Section 5.3 in the main submission).
, hyperparameters that are selected to train with JFT-300M data yield sub-optimal performance when training on ImageNet (IVSVRC 2012 image classification dataset with 1.2M images). ∙ Please also note that this feature is work in progress and that it is still far from being perfect.
Second, We tried two strategies to initialize the model weights for training: random initialization and initializing from an ImageNet checkpoint. Our paper is an attempt to put the focus back on the data. Deep classifiers from image tags in the wild.
What will happen if we increase the dataset size by 10x or 100x? J. JFT-300M is a follow up version of the dataset introduced by [17, 7]. paradigms.
data was used for representation learning. We use visual embeddings to measure similarities and identify duplicate or near-duplicate images. Our model is trained for 30k SGD iterations using a mini-batch of 6 images, momentum of 0.9, an initialize learning rate (LR) of 10−3 and ”polynomial” learning rate policy [4]. The JFT dataset has more than 300M images that are labeled with 18291 categories. To alleviate the issue of missing labels, we use a hand-designed label hierarchy and fill in the missing labels accordingly. We would specifically like to thank Tom Duerig, Neil Alldrin, Howard Zhou, Lu Chen, David Cai, Gal Chechik, Zheyun Feng, Xiangxin Zhu and Rahul Sukthankar for their help. ∙ We next evaluate the JFT-300M checkpoints on object detection tasks.
However, due to the time constraints, we train the models for 36M iterations or 4 epochs, which takes approximately 2 months. Privacy notice: By enabling the option above, your browser will contact the API of web.archive.org to check for archived content of web pages that are no longer available. that this inspires vision community to not undervalue the data and develop
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