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Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects, read printed and handwritten text, and build valuable metadata into your image catalog.

Specific Deep Learning VM Images images are available to suit your choice offramework and processor. There are currently imagessupporting TensorFlow, PyTorch, and generic high-performancecomputing, with versions for both CPU-only and GPU-enabled workflows.To find the image that you want, see the table below.

For most frameworks, Debian 11 is the default OS. Ubuntu 20.04images are available for some frameworks.They are denoted by the -ubuntu-2004suffixes in the image family name (see Listing all availableversions).Debian 10 and Debian 9 images have been deprecated.

Some Deep Learning VM image families are experimental, as indicatedby the table of image families. Experimental images aresupported on a best-effort basis, and may not receiverefreshes on each new release of the framework.

You can reuse the same image even if the latest image is newer. This can beuseful, for instance, if you are trying to create a cluster and youwant to ensure that any images that are used to create new instances arealways the same. You should not use the name of the image family in thissituation because, if the latest image is updated, you'll have different imageson some instances in your cluster.

AWS Deep Learning Containers are available as Docker images in Amazon ECR. Each Docker image is built for training or inference on a specific Deep Learning framework version, python version, with CPU or GPU support.

The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep learning system that uses only the reported diagnoses as labels for training, thereby avoiding expensive and time-consuming pixel-wise manual annotations. We evaluated this framework at scale on a dataset of 44,732 whole slide images from 15,187 patients without any form of data curation. Tests on prostate cancer, basal cell carcinoma and breast cancer metastases to axillary lymph nodes resulted in areas under the curve above 0.98 for all cancer types. Its clinical application would allow pathologists to exclude 65-75% of slides while retaining 100% sensitivity. Our results show that this system has the ability to train accurate classification models at unprecedented scale, laying the foundation for the deployment of computational decision support systems in clinical practice.

We would like to thank the Applied Bioinformatics Laboratories (ABL) at the NYU School of Medicine for providing bioinformatics support and helping with the analysis and interpretation of the data. The Applied Bioinformatics Laboratories are a Shared Resource, partially supported by the Cancer Center Support Grant, P30CA016087 (A.T.), at the Laura and Isaac Perlmutter Cancer Center (A.T.). For this work, we used computing resources at the High-Performance Computing Facility (HPC) at NYU Langone Medical Center. The slide images and the corresponding cancer information were uploaded from the Genomic Data Commons portal ( -portal.nci.nih.gov) and are in whole or in part based upon data generated by the TCGA Research Network ( ). These data were publicly available without restriction, authentication or authorization necessary. We thank the GDC help desk for providing assistance and information regarding the TCGA dataset. For the independent cohorts, we only used whole-slide images; the NYU dataset we used consists of slide images without identifiable information and therefore does not require approval according to both federal regulations and the NYU School of Medicine Institutional Review Board. For this same reason, written informed consent was not necessary. We thank C. Dickerson, from the Center for Biospecimen Research and Development (CBRD), for scanning the whole-slide images from the NYU Langone Medical Center. We also thank T. Papagiannakopoulos, H. Pass and K.-K. Wong or their valuable and constructive suggestions.

Although deep learning has revolutionized computer vision, current approaches have several major problems: typical vision datasets are labor intensive and costly to create while teaching only a narrow set of visual concepts; standard vision models are good at one task and one task only, and require significant effort to adapt to a new task; and models that perform well on benchmarks have disappointingly poor performance on stress tests,[^reference-1][^reference-2][^reference-3][^reference-4] casting doubt on the entire deep learning approach to computer vision.

Finally, CLIP is part of a group of papers revisiting learning visual representations from natural language supervision in the past year. This line of work uses more modern architectures like the Transformer[^reference-32] and includes VirTex,[^reference-33] which explored autoregressive language modeling, ICMLM,[^reference-34] which investigated masked language modeling, and ConVIRT,[^reference-35] which studied the same contrastive objective we use for CLIP but in the field of medical imaging.

We show that scaling a simple pre-training task is sufficient to achieve competitive zero-shot performance on a great variety of image classification datasets. Our method uses an abundantly available source of supervision: the text paired with images found across the internet. This data is used to create the following proxy training task for CLIP: given an image, predict which out of a set of 32,768 randomly sampled text snippets, was actually paired with it in our dataset.

We report two algorithmic choices that led to significant compute savings. The first choice is the adoption of a contrastive objective for connecting text with images.[^reference-31][^reference-17][^reference-35] We originally explored an image-to-text approach, similar to VirTex,[^reference-33] but encountered difficulties scaling this to achieve state-of-the-art performance. In small to medium scale experiments, we found that the contrastive objective used by CLIP is 4x to 10x more efficient at zero-shot ImageNet classification. The second choice was the adoption of the Vision Transformer,[^reference-36] which gave us a further 3x gain in compute efficiency over a standard ResNet. In the end, our best performing CLIP model trains on 256 GPUs for 2 weeks which is similar to existing large scale image models.[^reference-37][^reference-23][^reference-38][^reference-36]

This finding is also reflected on a standard representation learning evaluation using linear probes. The best CLIP model outperforms the best publicly available ImageNet model, the Noisy Student EfficientNet-L2,[^reference-23] on 20 out of 26 different transfer datasets we tested.

Millions of digital content creators across sectors use ThingLink to radically improve engagement and learning results with interactive media: images, videos, virtual tours, 3D models and simulations.

Abstract: Given a portrait image of a person and an environment map of the target lighting, portrait relighting aims to re-illuminate the person in the image as if the person appeared in an environment with the target lighting. To achieve high-quality results, recent methods rely on deep learning. An effective approach is to supervise the training of deep neural networks with a high-fidelity dataset of desired input-output pairs, captured with a light stage. However, acquiring such data requires an expensive special capture rig and time-consuming efforts, limiting access to only a few resourceful laboratories. To address the limitation, we propose a new approach that can perform on par with the state-of-the-art (SOTA) relighting methods without requiring a light stage. In addition to achieving SOTA results, our approach offers several advantages over the prior methods, including controllable glares on glasses and more temporally-consistent results for relighting videos.

We present a physically-rendered synthetic dataset tailored for portrait relighting. The dataset consists of 300k samples, where each sample is rendered under two different HDR illuminations. Besides the RGB images, we also render a suite of other attributes, such as albedo and normal maps.

According to Jeremy Howard, padding a big piece of the image (64x160 pixels) will have the following effect: The CNN will have to learn that the black part of the image is not relevant and does not help distinguishing between the classes (in a classification setting), as there is no correlation between the pixels in the black part and belonging to a given class. As you are not hard coding this, the CNN will have to learn it by gradient descent, and this might probably take some epochs. For this reason, you can do it if you have lots of images and computational power, but if you are on a budget on any of them, resizing should work better.

Therefore, learning the equivariance of different possible positions of the image is what makes training take more time. If you have 1,000,000 images then after resizing you will have the same number of images; on the other hand, if you pad and want to consider different possible locations (10 randomly for each image) then you will have 10,000,000 images. That is, training will take 10 times longer. e24fc04721