I would choose the training dataset to contain all variants that I can think of (e.g. angle, background, etc). I think it increases the probability of recognizing them.

Also, I think ML systems are more successful when trained on large datasets.

he quality, size, diversity, and relevance of the dataset can significantly impact how well the AI model learns and generalizes to new data. Here are some ways in which the dataset affects the performance of an AI model:

In conclusion, the dataset significantly affects the performance of an AI model. Providing high-quality, diverse, and relevant data for training, along with appropriate data preprocessing and augmentation techniques, can lead to improved performance and better generalization on new, unseen data. It's crucial to analyze and understand the dataset thoroughly before training an AI model to achieve optimal results.

Thank you @Hariharasitaraman S. for such an elaborated answer. I have created a dataset based on similar lines, covering most of the aspects described in your answer. But I did not consider height as a factor, most of the dataset is from 1 meter, whereas I am trying to detect and classify from 10meters. The model fails at this distance but works most of the time at 1 -3 meters. Do you see any problem with my dataset? Thanks in advance

Thank you @octavian patrascoiu for your response

You may use basic methods of object detection (Exhaustive Search, R-CNN, Fast R-CNN and Faster R-CNN) in order for the model to be able to learn the class and the position of the object in the image and try to understand the technical details

The dataset has a significant impact on the performance of an AI model. The quality, size, diversity, and relevance of the dataset directly influence the model's ability to learn and make accurate predictions. A well-curated and representative dataset with sufficient examples and balanced classes can lead to better model performance. Conversely, a small, biased, or incomplete dataset may result in limited generalization and poor performance. Therefore, a high-quality dataset is crucial for training and evaluating AI models effectively.

Iterative training with manual correction

Start with a small dataset and train the model. Then, analyze the model's performance and manually correct any incorrect inferences. Gradually increase the size of the dataset and repeat the training process iteratively, refining the model. This approach allows for fine-tuning and the correction of specific errors.

The dataset plays a crucial role in determining the performance of an AI model. Here are several ways in which the dataset affects the performance:

tajinder kumar Saini Ricardo Pires da Silva Aparna Sathya Murthy Shraddha jain Sharma Arpita Maheriya Hariharasitaraman S Octavian Patrascoiu Thank you all for your answers, it makes sense what you are advising, but please go through the detailed description of my question. If possible kindly suggest along those lines.

Regards

Nitin Kumar

The dataset plays a crucial role in the performance of an AI model for the training, detection, and classification of small objects like loosened nuts and bolts on a railway track. Here's how the dataset affects model performance and considerations for collecting an ideal dataset:

Additionally, it is recommended to seek expert knowledge from railway track professionals or domain experts to guide the collection and annotation process effectively. Regular updates and additions to the dataset over time can further improve the model's performance as it encounters new variations and scenarios.

Iteratively training the model by manually correcting every incorrect inference can be beneficial, but may be time-consuming. If you already have a large dataset, you can train the model all at once without going through multiple steps. It'll be quicker, but the model might not be as precise.

The dataset waltzes as the AI's dance partner. 🕺🏻🎶 A splendid dataset polishes AI's moves, enhancing performance. 🌟 Inadequate data leads to AI stumbling like a startled cat. 🐱😵 So, choose data wisely, and let the AI dance like nobody's watching! 💃🕺