<em id="rw4ev"></em>

      <tr id="rw4ev"></tr>

      <nav id="rw4ev"></nav>
      <strike id="rw4ev"><pre id="rw4ev"></pre></strike>
      合肥生活安徽新聞合肥交通合肥房產生活服務合肥教育合肥招聘合肥旅游文化藝術合肥美食合肥地圖合肥社保合肥醫院企業服務合肥法律

      代寫CS 6476、代做Python/Java程序

      時間:2024-04-26  來源:合肥網hfw.cc  作者:hfw.cc 我要糾錯



      GEORGIA TECH’S CS 6**6 COMPUTER VISION
      Final Project : Classification and Detection with
      Convolutional Neural Networks
      April 1, 2023
      PROJECT DESCRIPTION AND INSTRUCTIONS
      Description
      For this topic you will design a digit detection and recognition system which takes in a single
      image and returns any sequence of digits visible in that image. For example, if the input image
      contains a home address 123 Main Street, you algorithm should return “123”. One step in your
      processing pipeline must be a Convolutional Neural Network (CNN) implemented in TensorFlow or PyTorch . If you choose this topic, you will need to perform additional research about
      CNNs. Note that the sequences of numbers may have varying scales, orientations, and fonts,
      and may be arbitrarily positioned in a noisy image.
      Sample Dataset: http://ufldl.stanford.edu/housenumbers/
      Related Lectures (not exhaustive): 8A-8C, 9A-9B
      Problem Overview
      Methods to be used: Implement a Convolutional Neural Network-based method that is capable of detecting and recognizing any sequence of digits visible in an image.
      RULES:
      • Don’t use external libraries for core functionality You may use TensorFlow, keras and Pytorch and are even required to use pretrained models as part of your pipeline.
      • However, you will receive a low score if the main functionality of your code is provided
      via an external library.
      • Don’t copy code from the internet The course honor code is still in effect during the final
      project. All of the code you submit must be your own. You may consult tutorials for
      libraries you are unfamiliar with, but your final project submission must be your own
      work.
      1
      • Don’t use pre-trained machine learning pipelines If you choose a topic that requires the
      use of machine learning techniques, you are expected to do your own training. Downloading and submitting a pre-trained models that does all the work is not acceptable for
      this assignment. For the section on reusing pre-trained weights you expected to use a
      network trained for another classification task and re-train it for this one.
      • Don’t rely on a single source We want to see that you performed research on your chosen topic and incorporated ideas from multiple sources in your final results. Your project
      must not be based on a single research paper and definitely must not be based on a single
      online tutorial.
      Please do not use absolute paths in your submission code. All paths must be relative
      to the submission directory. Any submissions with absolute paths are in danger of receiving a penalty!
      Starter Code
      There is no starter code for this project
      Programming Instructions
      In order to work with Convolutional Neural Networks we are providing a conda environment
      description with the versions of the libraries that the TA will use in the grading environment
      in canvas->files->Project files. This environment includes PyTorch, Tensorflow, Scikit-learn,
      and SciPy. You may use any of these. It is your responsibility to use versions of libraries that
      are compatible with those in the environment. It is also up to you to organize your files and
      determine the code’s structure. The only requirement is that the grader must only run one
      file to get your results. This, however, does not prevent the use of helper files linked to this
      main script. The grader will not open and run multiple files. Include a README.md file with
      usage instructions that are clear for the grader to run your code.
      Write-up Instructions
      The report must be a PDF of 4-6 pages including images and references. Not following this
      requirement will incur a significant penalty and the content will be graded only up to page 6.
      Note that the report will be graded subject to a working code. There will be no report templates
      provided with the project materials.
      The report must contain:
      You report must be written to show your work and demonstrate a deep understanding of your
      chosen topic. The discussion in your report must be technical and quantitative wherever possible.
      • A clear and concise description of the algorithms you implemented. This description
      must include references to recently published computer vision research and show a deep
      understanding of your chosen topic.
      • Results from applying your algorithm to images or video. Both positive and negative results must be shown in the report and you must explain why your algorithm works on
      some images, but not others.
      2
      How to Submit
      Similar to the class assignments, you will submit the code and the report to Gradescope (note:
      there will be no autograder part). Find the appropriate project and make your submission into
      the correct project. Important: Submissions sent to Email, Piazza or anything that is not
      Gradescope will not be graded.
      Grading
      The report will be graded following the scheme below:
      • Code (30%): We will verify that the methods and rules indicated above have been followed.
      • Report (70%): Subject to a working code.
      • Description of existing methods published in recent computer vision research.
      • Description of the method you implemented.
      • Results obtained from applying your algorithms to images or videos.
      • Analysis on why your method works on some images and not on others. (with images)
      • References and citations.
      ASSIGNMENT OVERVIEW
      This project requires you to research how Convolutional Neural Networks work and their application to number detection and recognition. This is not to be a replica of a tutorial found
      online. Keep in mind this content is not widely covered in this course lectures and resources.
      The main objective of this assignment is to demonstrate your understanding of how these tools
      work. We allow you to use a very powerful training framework that helps you to avoid many of
      the time-consuming implementation details because the emphasis of this project will be on
      the robustness of your implementation and in-depth understanding of the tools you are using.
      Installation and Compatibility
      The provided environment yml description gives you with the versions of the libraries the TA’s
      will during grading. We recommend you use conda to install the environment. Make sure the
      forward pass of your pipeline runs in a reasonable amount of time when using only a CPU as
      some TA’s do not have a GPU.
      OS Warning:
      Be warned that TA’s may grade on linux, Windows or Mac machines. Thus, it is your responsibility to make sure that your code is platform independent. This is particularly important when
      using paths to files. If your code doesn’t run during grading due to some incompatibility you
      will incur a penalty.
      Classifier Requirements
      Your classification pipeline must be robust in the following ways:
      1. Scale Invariance:
      3
      The scale of the sequence of numbers in an image in vary.
      2. Location Invariance:
      The location of the sequence of numbers in the image may vary.
      3. Font Invariance:
      You are expected to detect numbers despite their fonts.
      4. Pose Invariance:
      The sequence of numbers can be at any angle with respect to the frame of the image.
      5. Lighting Invariance:
      We expect robustness to the lighting conditions in which the image was taken.
      6. Noise Invariance:
      Make sure that your pipeline is able to handle gaussian noise in the image.
      Pipeline Overview:
      The final pipeline should incorporate the following preprocessing and classification components. We expect you to clearly explain in your report what you did at each stage and why.
      Preprocessing
      Your pipeline should start from receiving an image like this:
      Notice that this is not the type of image your classification network trained on. You will have to
      do some preprocessing to correctly detect the number sequence in this image.
      In the preprocessing stage your algorithm should take as input an image like the one above and
      return region of interest. Those ROI will be regions in the image where there is a digit. In order
      to perform this preprocessing step you can use the MSER and/or sliding window algorithm with
      image pyramid approach. (see https://docs.opencv.org/4.1.0/d3/d28/classcv_1_1MSER.html)
      Note: The region proposal stage has to be separated from the classification stage. For this
      project we will use MSER and/or sliding window to detect the ROI. This means that one-stage
      approaches (detection + classification) such as YOLO are not allowed.
      4
      Noise Management
      We expect to see you handle gaussian noise and varying lighting conditions in the image. Please
      explain what you do in order to handle these types of perturbations and still have your classifier
      work.
      Location Invariance
      Since you don’t know where the numbers will appear on the image you will have to search for
      them using a sliding window method.
      Scale Invariance
      Make sure to implement an image pyramid with non-maxima suppression to detect numbers
      at any scale.
      Performance Considerations
      Running your full classifier through a sliding window can be very expensive. Did you do anything to mitigate forward pass runtime?
      Classification
      This section is concerned with the implementation of a number classifier based on the sample
      dataset.
      Model Variation
      There are several approaches to implementing a classifier and we want you get exposure to all
      of them:
      1. Make your own architecture and train it from scratch.
      (https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) (without pre-trained weights).
      2. Use a VGG 16 implementation and train it with pre-trained weights.
      (Note: Final Linear layer will have 11 classes,
      https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html(finetuning-the-convnet)
      Make sure you mention in your report what changes you made to the VGG16 model in order to
      use it for your particular classification task. What weights did you reuse and why? Did you train
      over the pre-trained weights?
      Training Variation
      We want you to have some familiarity with stochastic gradient descent. For this reason we
      want you to explain your choice of loss function during training. We also want an explanation
      for your choice of batch size and learning rate. In the report we expect a definition of these
      parameters and an explanation of why you chose the numbers you did. We also want to see
      5
      how you decided to stop the training procedure.
      Evaluating Performance
      In order to evaluate the performance of your learning model we expect you to include training curves with validation, training and test set errors. When you compare the performance of
      each model we also want you include tables with the test set performance of the each model.
      We want to see a discussion of your performance in each of the models outlined above and we
      want to see empirical data demonstrating which is better. Your final pipeline should use the
      model and training that empirically demonstrates better performance.
      FINAL RESULTS
      Image Classification Results
      During grading, TAs expect to be able to run a python 3 file named run.py that writes five images to a graded_images folder in the current directory. The images should be named 1.png,
      2.png, 3.png, 4.png and 5.png.
      You can pick these images; however, across the five of them we will be checking that you
      demonstrate following:
      1. Correct classification at different scales
      2. Correct classification at different orientations
      3. Correct classification at different locations within the image.
      4. Correct classification with different lighting conditions.
      Notice, that since we allow you to pick the images, we expect good results.
      In addition, add extra images showing failure cases of your implementation in the report. Analyse and comment why your algorithm is failing on those images.

       

      請加QQ:99515681  郵箱:99515681@qq.com   WX:codinghelp




















       

      掃一掃在手機打開當前頁
    1. 上一篇:代做COMP10002、c++編程設計代寫
    2. 下一篇:去菲律賓旅游免簽嗎(什么方法可以免簽)
    3. 無相關信息
      合肥生活資訊

      合肥圖文信息
      挖掘機濾芯提升發動機性能
      挖掘機濾芯提升發動機性能
      戴納斯帝壁掛爐全國售后服務電話24小時官網400(全國服務熱線)
      戴納斯帝壁掛爐全國售后服務電話24小時官網
      菲斯曼壁掛爐全國統一400售后維修服務電話24小時服務熱線
      菲斯曼壁掛爐全國統一400售后維修服務電話2
      美的熱水器售后服務技術咨詢電話全國24小時客服熱線
      美的熱水器售后服務技術咨詢電話全國24小時
      海信羅馬假日洗衣機亮相AWE  復古美學與現代科技完美結合
      海信羅馬假日洗衣機亮相AWE 復古美學與現代
      合肥機場巴士4號線
      合肥機場巴士4號線
      合肥機場巴士3號線
      合肥機場巴士3號線
      合肥機場巴士2號線
      合肥機場巴士2號線
    4. 幣安app官網下載 短信驗證碼 丁香花影院

      關于我們 | 打賞支持 | 廣告服務 | 聯系我們 | 網站地圖 | 免責聲明 | 幫助中心 | 友情鏈接 |

      Copyright © 2024 hfw.cc Inc. All Rights Reserved. 合肥網 版權所有
      ICP備06013414號-3 公安備 42010502001045

      成人久久18免费网站入口