Deep Learning Analysis Report
Budget: $30 – $50 USD
Research Report: Object Detection with YOLOv7 and YOLOv8 in Machine Vision Using a Manually Collected and Annotated Dataset
I need a report written around deep learning
The report should apply well established deep learning techniques to analyse datasets that intrigue you. Your task involves conducting a comprehensive analysis, drawing data-driven conclusions, and presenting your findings in a report.
Your report should provide a detailed account of your experimental process, including exploratory data analysis, data preparation, cleaning, and the algorithms you've chosen. The report should be approximately 3000 words in length (excluding references), following a specified report writing style.
The specific tasks would include
Specific Tasks:
1. Data Collection and Preparation:
Your first task is to collect an image dataset that includes a potential
target variable suitable for object detection. While publicly available
datasets, like the pig posture dataset and the facial emotional
dataset, can be used, I strongly encourage you to explore personally
collected datasets. You also have the option to augment existing
datasets with images from your collection.
2. Object Detection:
In this phase, you will apply headless pretrained models, such as
YOLOv7 and YOLOv8, using frameworks like PyTorch or Ultralytics.
Adapt these models to your specific dataset, and then conduct a
comprehensive analysis. Compare and discuss the performance of
each model, focusing on aspects like mean average precision and
other relevant performance metrics.
3. Comparison of Cloud and Local Computing:
The final task involves implementing your code on both cloud
platforms, such as Google Colab, and on local machines, particularly
within a virtual environment on Linux. You should provide a clear
account of the libraries used in your local environment, including
their versions. Discuss the differences in performance between
cloud-based and local computing methods, including speed and
model training times.
The report should be written in harvard format.
I need a report written around deep learning
The report should apply well established deep learning techniques to analyse datasets that intrigue you. Your task involves conducting a comprehensive analysis, drawing data-driven conclusions, and presenting your findings in a report.
Your report should provide a detailed account of your experimental process, including exploratory data analysis, data preparation, cleaning, and the algorithms you've chosen. The report should be approximately 3000 words in length (excluding references), following a specified report writing style.
The specific tasks would include
Specific Tasks:
1. Data Collection and Preparation:
Your first task is to collect an image dataset that includes a potential
target variable suitable for object detection. While publicly available
datasets, like the pig posture dataset and the facial emotional
dataset, can be used, I strongly encourage you to explore personally
collected datasets. You also have the option to augment existing
datasets with images from your collection.
2. Object Detection:
In this phase, you will apply headless pretrained models, such as
YOLOv7 and YOLOv8, using frameworks like PyTorch or Ultralytics.
Adapt these models to your specific dataset, and then conduct a
comprehensive analysis. Compare and discuss the performance of
each model, focusing on aspects like mean average precision and
other relevant performance metrics.
3. Comparison of Cloud and Local Computing:
The final task involves implementing your code on both cloud
platforms, such as Google Colab, and on local machines, particularly
within a virtual environment on Linux. You should provide a clear
account of the libraries used in your local environment, including
their versions. Discuss the differences in performance between
cloud-based and local computing methods, including speed and
model training times.
The report should be written in harvard format.