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UppuluriKalyani / ML-Nexus

ML Nexus is an open-source collection of machine learning projects, covering topics like neural networks, computer vision, and NLP. Whether you're a beginner or expert, contribute, collaborate, and grow together in the world of AI. Join us to shape the future of machine learning!
https://ml-nexus.vercel.app/
MIT License
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Human Detection Project #38

Closed pavitraag closed 1 month ago

pavitraag commented 1 month ago

Human Detection Project

PROJECT TITLE

Human Detection Project

GOAL

To create a DL model which will identify the humans in given image.

DATASET

The dataset used for this project can be found at link to dataset.

DESCRIPTION

This project aims to identify the humans in the image. It is trained on the dataset containing CCTV footage images(as indoor as outdoor).

WHAT I HAD DONE

  1. Data collection: From the link of the dataset given above.
  2. Data preprocessing: Created a Image generators which helped to generate more images in order to increase the accuracy.
  3. Model selection: Chose traditional CNN along with Image detection architecture VGG16, Inception and Xception for Image detection.
  4. Comparative analysis: Compared the accuracy score of all the models.

MODELS USED

  1. CNN
  2. VGG16
  3. Inception
  4. Xception

LIBRARIES NEEDED

The following libraries are required to run this project:

EVALUATION METRICS

The evaluation metrics I used to assess the models:

RESULTS

Results on Val dataset:

Model Accuracy Loss
CNN 0.756 0.591
VGG16 0.859 0.304
Inception 0.886 0.33
Xception 0.843 0.481
Fine-tuned Inception 0.902 0.312

CONCLUSION

Based on results we can draw following conclusions:

  1. CNN Model: The CNN model achieved an accuracy of 75.6% and a loss of 0.591. While it demonstrates reasonable performance, there is room for improvement compared to the other models.

  2. VGG16 Model: The VGG16 model outperformed the CNN model with an accuracy of 85.9% and a lower loss of 0.304. It shows the effectiveness of using a pre-trained model like VGG16 for human detection, achieving a higher accuracy and lower loss than the CNN model.

  3. Inception Model: The Inception model achieved an accuracy of 88.6% and a loss of 0.33. It performs well, with a higher accuracy than both the CNN and VGG16 models. This suggests that the Inception architecture is effective in capturing human features and distinguishing them from non-human objects.

  4. Xception Model: The Xception model achieved an accuracy of 84.3% and a loss of 0.481. While it performs decently, it is slightly behind the VGG16 and Inception models in terms of accuracy.

  5. Fine-tuned Inception: The fine-tuned Inception model showed further improvement with an accuracy of 90.2% and a loss of 0.312. Fine-tuning the Inception model likely helped to adapt it more specifically to the task of human image detection, resulting in increased accuracy.

github-actions[bot] commented 1 month ago

Thank you for creating this issue! 🎉 We'll look into it as soon as possible. In the meantime, please make sure to provide all the necessary details and context. Your contributions are highly appreciated! 😊

UppuluriKalyani commented 1 month ago

@pavitraag Go for it!

alo7lika commented 1 month ago

assign me this work. Looking forward to solve the issue

github-actions[bot] commented 1 month ago

Hello @pavitraag! Your issue #38 has been closed. Thank you for your contribution!