Row 7686

Row ID: 7686 | Dataset Entry | Axioma AXP Content Repository

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So I am doing an internship project at a company that is as the title says.I basically need to classify human faces into 7 categories- Anger, disgust, happy, etc. Currently I'm trying to achieve good accuracy on FER 2013 dataset then I'll move to the Real Time capture part

I need to finish this project in like 2 weeks' time. I have tried transfer learning with models like **mobile\_net, VGG19, ResNet50, Inception, Efficient\_net** and my **training accuracy has reached to like 87% but validation accuracy is pretty low \~56%** (MAJOR overfitting, ik).

Can the smart folks here help me out with some suggestions on how to better perform transfer learning, whether I should use data augmentation or not( I have around 28000 training images), and about should I use **vision transformer**, etc. ?

with VGG19 and Inception , for some reason my validation accuracy gets stuck at 24.71% and doesn't change after it

ResNet50, mobile\_net and Efficient\_net are giving the metrics as stated above

This is a sample notebook I've been using for transfer learning [https://colab.research.google.com/drive/1DeJzEs7imQy4lItWA11bFB4mSdZ95YgN?usp=sharing](https://colab.research.google.com/drive/1DeJzEs7imQy4lItWA11bFB4mSdZ95YgN?usp=sharing)

Any and all help is appreciated!

FieldValue
text So I am doing an internship project at a company that is as the title says.I basically need to classify human faces into 7 categories- Anger, disgust, happy, etc. Currently I'm trying to achieve good accuracy on FER 2013 dataset then I'll move to the Real Time capture part I need to finish this project in like 2 weeks' time. I have tried transfer learning with models like **mobile\_net, VGG19, ResNet50, Inception, Efficient\_net** and my **training accuracy has reached to like 87% but validatio…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-17
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url_encoded Z0FBQUFBQm5Lak9IZFVCY1JvaThYQ3pqdV9jbGlZZDVzTUNEdHBQTlI1VmVfUF9iczVkX2ZPRmYzZWtYeFBfamVZeW1idVZlZ2s1MGhtQXJHeFdkMXdCaE9zbmFudHB6NTJ4ZHFIVWZ6NzE5NFlXazRWTFZJZGRfNGZWMTZhZVJTdExXa29KQnVMRXdIaUl1YlowbHVCdDhPRlpIcHJoNEFtWTV4VTFObUVNXzZjM0VkM0VQVEU5NUxrMGg1eTVvQ2FoWDUwSWlYdl80NW5lYWgydDhuZlc2QkJ3cXZvX3hXQT09

Raw Record

{
  "text": "So I am doing an internship project at a company that is as the title says.I basically need to classify human faces into 7 categories- Anger, disgust, happy, etc. Currently I'm trying to achieve good accuracy on FER 2013 dataset then I'll move to the Real Time capture part\n\nI need to finish this project in like 2 weeks' time. I have tried transfer learning with models like **mobile\\_net, VGG19, ResNet50, Inception, Efficient\\_net** and my **training accuracy has reached to like 87% but validation accuracy is pretty low \\~56%** (MAJOR overfitting, ik).\n\nCan the smart folks here help me out with some suggestions on how to better perform transfer learning, whether I should use data augmentation or not( I have around 28000 training images), and about should I use **vision transformer**, etc. ?\n\nwith VGG19 and Inception , for some reason my validation accuracy gets stuck at 24.71% and doesn't change after it\n\nResNet50, mobile\\_net and Efficient\\_net are giving the metrics as stated above\n\nThis is a sample notebook I've been using for transfer learning  \n[https://colab.research.google.com/drive/1DeJzEs7imQy4lItWA11bFB4mSdZ95YgN?usp=sharing](https://colab.research.google.com/drive/1DeJzEs7imQy4lItWA11bFB4mSdZ95YgN?usp=sharing)\n\nAny and all help is appreciated!",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-17",
  "username_encoded": "Z0FBQUFBQm5LakwzbjdSVUFZT1NQcWhCa1hET0JkRDk4VG85WDd0SjJzZjc4ck9yYmN2Z2s3Ti1Ua2hicThjUVBWM3FGc0lMdWUzRXg0MUt4SUNCT2tXODB0SWZSUG5JaFE9PQ==",
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}
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