Answers.Body: <pre><code>learn = cnn_learner(data, 
                      models.resnet50, 
                      metrics=error_rate)
  </code></pre>
  <p>resnet50 is one of many pre-trained 2D CNN computer vision models which use transfer learning. If you've classifying a common image such as a given mammal you're sorted because they'll be loads of pictures of that mammal all over resnet50. However, 'a cell' I dunno.</p>
  <p>When the computer vision challenges were in full swing between the tech giants each put their trained models onto publication release and are easily accessible e.g. via keras, fastai etc... I'd have to look them all up and each has different architecture, they are serious architecture like 50 layer NN. The competition was eventually stopped because computer vision had a lower error rate than human vision, at which point its pointless judging between DL algorithms because we can't say what it is how can we judge an algorithm?</p>
  <p>Transfer learning places CNN at significant advantage over RNN.</p>
  <p>The other possibility is google images</p>
  <pre><code>pip install google_images_download
  </code></pre>
  <p>I don't think its on conda. Then see if you get lucky with the following</p>
  <pre><code>from google_images_download import google_images_download
  response = google_images_download.googleimagesdownload()
  arguments = {&quot;keywords&quot;:&quot;cells,organelles, membranes&quot;,&quot;limit&quot;:60,&quot;print_urls&quot;:False}
  paths = response.download(arguments)
  print(paths)
  </code></pre>
  <p>Please note I don't do 2D CNN,i.e. images.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-05-05T23:11:23.993Z
Answers.LastActivityDate: 2022-05-06T02:25:35.670Z
Answers.LastEditDate: 2022-05-06T02:25:35.670Z
Answers.UserDisplayName: M__
Body: <p>As a practice data analysis I am trying to train a convolutional neural network (CNN) on <a href="https://www.kaggle.com/xhlulu/recursion-2019-load-resize-and-save-images/data" rel="nofollow noreferrer">some cellular images</a> made publicly available on Kaggle in 2019 by the company Recursion via supervised deep learning.</p>
  <p>I want to increase the predictive power using transfer learning at the lower layers of my CNN preferably from a public imaging model.</p>
  <p>Are there any appropriate models available?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-05-02T15:51:54.967Z
Comments.Text: Good question, the HUGE advantage of CNN is transfer learning. Google do a huge photo database for transfer-learning and if that dataset had cell images then you've made it. If not - you could start the trend and train up a database.
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-05-05T20:29:11.367Z
Comments.Text: Hmm very cool thanks for the suggestion
Comments.UserDisplayName: Angus Campbell
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-05-05T20:29:22.680Z
Comments.Text: Would you consider andwering so I can lay this questuion to rest?
Comments.UserDisplayName: Angus Campbell
Tags: <machine-learning><deep-learning>
Title: Transfer learning for a Convolutional NN for Recursion dataset
ThreadText: # Transfer learning for a Convolutional NN for Recursion dataset
  
  (Asked by: username_0 on 2021-10-07)
  
  As a practice data analysis I am trying to train a convolutional neural network (CNN) on [some cellular images](https://www.kaggle.com/xhlulu/recursion-2019-load-resize-and-save-images/data) made publicly available on Kaggle in 2019 by the company Recursion via supervised deep learning.
  
  I want to increase the predictive power using transfer learning at the lower layers of my CNN preferably from a public imaging model.
  
  Are there any appropriate models available?
  
  **Tags:** machine-learning, deep-learning
  
  > **Comments:**
  > **username_1**: Good question, the HUGE advantage of CNN is transfer learning. Google do a huge photo database for transfer-learning and if that dataset had cell images then you've made it. If not - you could start the trend and train up a database.  
  > **username_0**: Hmm very cool thanks for the suggestion  
  > **username_0**: Would you consider andwering so I can lay this questuion to rest?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-05-05. Score: 1 | Accepted answer
  
  ```
  learn = cnn_learner(data, 
                      models.resnet50, 
                      metrics=error_rate)
  
  ```
  
  resnet50 is one of many pre-trained 2D CNN computer vision models which use transfer learning. If you've classifying a common image such as a given mammal you're sorted because they'll be loads of pictures of that mammal all over resnet50. However, 'a cell' I dunno.
  
  When the computer vision challenges were in full swing between the tech giants each put their trained models onto publication release and are easily accessible e.g. via keras, fastai etc... I'd have to look them all up and each has different architecture, they are serious architecture like 50 layer NN. The competition was eventually stopped because computer vision had a lower error rate than human vision, at which point its pointless judging between DL algorithms because we can't say what it is how can we judge an algorithm?
  
  Transfer learning places CNN at significant advantage over RNN.
  
  The other possibility is google images
  
  ```
  pip install google_images_download
  
  ```
  
  I don't think its on conda. Then see if you get lucky with the following
  
  ```
  from google_images_download import google_images_download
  response = google_images_download.googleimagesdownload()
  arguments = {"keywords":"cells,organelles, membranes","limit":60,"print_urls":False}
  paths = response.download(arguments)
  print(paths)
  
  ```
  
  Please note I don't do 2D CNN,i.e. images.
  
  
  ---
  
  