Vision Ias Notes In Hindi Pdf Free Download __TOP__
Annegret Mclean
The most common symptom reported in the present cohort was headache (45.7 %), followed by dry eyes (31.1 %) and pain in and around the eyes (28.7 %). Megwas and Daguboshim reported that headache (41.8 %), pain (31.6 %) and eye strain (26.7 %) were the most prevalent visual symptoms among VDT users [19]. Headache was the most commonly reported symptom in computer users in several other similar studies [13, 20, 21]. Headaches is often accompanied by other symptoms of CVS, though many patients do not consider it to be a directly vision-related problem [22]. Human eyes need to adjust themselves in order to see objects from different distances, such as by changing the size of pupil, lengthening or shortening the lens to change eye focus, and contracting extra-ocular muscles to coordinate the two eyes. If computer user needs to view computer screen while looking at a paper on the table from time to time, the eyes have to adjust constantly. In addition, the words and images on a computer screen are difficult for the eyes to focus on due to their poor edge resolution. The eyes tend to change the focus to a resting point and then refocus on the screen. For these reasons, constant focusing and refocusing is required. These constant changes take place thousands of times a day when a computer user stares at a computer screen for hours, which then stresses the eye muscles leading to eye fatigue and discomfort causing headaches [23].
According to the results of the binary logistic regression analysis, the most significant risk factor for development of CVS was pre-existing eye disease (OR: 4.49) followed by use of contact lenses (OR: 3.21). Supporting this finding, a study done in Malaysia has revealed that use of correction spectacle/lenses were significantly associated with CVS (OR: 1.91) in multivariate logistic regression analysis, even after adjustment for other confounding variables [17]. Furthermore, university students who were wearing spectacles experienced symptoms of CVS significantly more often than those who were not wearing spectacles [20]. A study by Logaraj et al. also revealed that medical and engineering students wearing corrective lens (spectacle or contact lens) showed a significantly higher risk of developing headache (OR: 1.80) and blurred vision (OR: 2.10) [14]. Possible explanations for the increased risk of CVS among those using correction spectacles/lenses is because the computer tasks are types of near work where letters on the screen are formed by tiny dots called pixels, rather than a solid image, it causes the eyes which already have some corrective problem to work a bit harder to keep the images in focus [17].
The new transforms in torchvision.transforms.v2 support image classification, segmentation, detection, and video tasks. They are now 10%-40% faster than before! This is mostly achieved thanks to 2X-4X improvements made to v2.Resize(), which now supports native uint8 tensors for Bilinear and Bicubic mode. Output results are also now closer to PIL's! Check out our performance recommendations to learn more.
Additionally, torchvision now ships with libjpeg-turbo instead of libjpeg, which should significantly speed-up the jpeg decoding utilities (read_image, decode_jpeg), and avoid compatibility issues with PIL.
In the previous release 0.15 we BETA-released a new set of transforms in torchvision.transforms.v2 with native support for tasks like segmentation, detection, or videos. We have now stabilized the design decisions of these transforms and made further improvements in terms of speedups, usability, new transforms support, etc.
The API is completely backward compatible with the previous one, and remains the same to assist the migration and adoption. We are now releasing this new API as Beta in the torchvision.transforms.v2 namespace, and we would love to get early feedback from you to improve its functionality. Please reach out to us if you have any questions or suggestions.
We're grateful for our community, which helps us improve torchvision by submitting issues and PRs, and providing feedback and suggestions. The following persons have contributed patches for this release:
Following up on the multi-weight support API that was released on the previous version, we have added a new model registration API to help users retrieve models and weights. There are now 4 new methods under the torchvision.models module: get_model, get_model_weights, get_weight, and list_models. Here are examples of how we can use them:
We would like to thank Haoqi Fan, Yanghao Li, Christoph Feichtenhofer and Wan-Yen Lo for their work on PyTorchVideo and their support during the development of the MViT model. We would like to thank Sophia Zhi for her contribution implementing the S3D model in torchvision.
The Swin Transformer and EfficienetNetV2 are two popular classification models which are often used for downstream vision tasks. This release includes 6 pre-trained weights for their classification variants. Here is how to use the new models:
Torchvision now supports optical flow! Optical flow models try to predict movement in a video: given two consecutive frames, the model predicts where each pixel of the first frame ends up in the second frame. Check out our new tutorial on Optical Flow!
Vision Transformer (ViT) and ConvNeXt are two popular architectures which can be used as image classifiers or as backbones for downstream vision tasks. In this release we include 8 pre-trained weights for their classification variants. The models were trained on ImageNet and can be used as follows:
Up until now, torchvision would almost never remove deprecated APIs. In order to be more aligned and consistent with pytorch core, we are updating our deprecation policy. We are now following a 2-release deprecation cycle: deprecated APIs will raise a warning for 2 versions, and will be removed after that. To reflect these changes and to smooth the transition, we have decided to:
There are many more such vision papers; a few are listed in the footnotes2. But hopefully you get the idea. Most vision papers quickly fade into obscurity. But the most successful often play crucial roles in pioneering new fields, including computer science, geoengineering, gravitational wave astronomy, genomics, and many others.
The immediate motivator for the present notes is a combination of observations I find surprising: (a) vision papers often play a crucial role in instigating new fields of science; and yet (b) the kind of thinking they involve is of a type that scientists often don't publicly do much of; indeed (c) the style of thinking involved is sometimes disparaged by many (not all) scientists. Often, the papers contain few or no technical results, and little or no data. They may, in fact, not hew to the usual standards of any existing field. As a consequence, the contents of vision papers tend to be radically different than most conventional scientific papers. They're often storytelling or narrative creation, with few technical results, and sometimes apppear superficially closer to literature than what people ordinarily consider science.
When vision papers are published, they're often ignored; they're certainly rarely much respected. Several people who work fulltime (today) on quantum computing, told me in the 1990s that the founding papers of the field were vague and wishy-washy, "not real physics". It took time for those fundamental papers to become celebrated. This is a common pattern: many important vision papers are ignored early, with success coming much later, if at all.
This all seems like a curious combination. What's going on? When are these papers valuable (or not)? If it's true that vision papers sometimes play a crucial role in science, then is it a bad thing they may be regarded negatively? Indeed: why are they regarded negatively? With Kanjun Qiu, I've been wondering: would there be any benefits to soliciting visions, perhaps as part of some kind of Vision Prize competition? Would it be possible to speed up the creation of new fields of science this way? And then there's personal questions, as well: is it useful to develop this style of thinking? What role should it play in one's own work? And part of me shares the instinctive aversion to vision papers I mentioned above; indeed, I sometimes feel a little sheepish about vision papers I've written. At the same time, this feeling of sheepishness seems utterly bonkers.
The purpose of these notes is to engage these questions. I must admit: I'm a little embarassed to be writing the notes! It feels like so much faffing around, visions-of-visions, like blogging about blogging. But those concerns are wrong. Vision papers are connected to fundamental questions like the way scientific fields are founded. And they're interesting as a distinct kind of epistemological object, something that no-one, as far as I know, has ever thought about as a particular type of knowledge. They're worth understanding better.
Throat-clearing aside, let's get back to the subject. I've asserted writing vision papers is often regarded as lightweight. It's difficult to prove this. I've certainly heard scientists say negative things about such work; occasionally the comments are scathing. More often, such work is ignored, or regarded as "fun speculation, but not real science". There are few venues for such work. I'm certain the median number of vision papers in an issue of Physical Review Letters is zero3.
This description may perhaps be taken as an implicit criticism of Kay's paper, and praise for Kitaev's. That's not the point. Quite the reverse: the point is that whether a vision paper is successful seems surprisingly independent of the strength of the technical results contained therein.
By contrast, the key element of a vision paper isn't a new fact about the world. It's effectively a would-be prophet standing up and proclaiming: "I see a wonderful opportunity over there, that looks [something like this]. Let's go explore!" Kay's is a vision of new media transforming the way people learn and think. Kitaev's is a vision of materials with properties so radically different to the ordinary world that they would make an invisibility cloak seems mundane.
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