GitHub topics: inceptionv3-model

Repositories

SD7Campeon/DeepFake-Classification

Advanced deepfake detection using InceptionV3 with a novel Fourier Attention layer. Leverages FFT to detect synthetic video frames. Features robust data pipeline, EDA, and visualizations. Built with TensorFlow, OpenCV, and more. Scalable, interpretable, MIT-licensed.

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harmanveer-2546/Dust-Detection-on-solar-panel-using

The accumulation of dust, snow, bird drops etc. on the surface of solar panels reduces the efficiency of the solar modules and hence the amount of produced energy. Monitoring and cleaning solar panels is a crucial task, hence developing an optimal procedure to monitor and clean these panels is very important in order to increase modules efficiency.

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shubham101096/Hotdog-CoreML

An iOS app used to detect whether the image captured is of Hotdog or not.

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tushartotla12/fire-detection

A artificial intelligence project which detects the fire and smoke before the accident causing huge loses. The work depicts the techniques like object detection, which are in todays world taking great heights. A proposal to a new method based on a deep learning approach, which uses a convolutional neural network that employs dilated convolutions has been approched. The surveillance cameras keep capturing the images, video clips of the region, which then are acted as input to the proposed system and design. It then checks the if the image has been observed with some fire or not and if found rings the alarm and thus the fire management is known in advance and the fire is prevented to avoid any kind of small damage even. In this article, two custom CNN models have been implemented for a cost-effective fire detection CNN architecture for surveillance videos. The first model is a customized basic CNN architecture inspired by AlexNet architecture. We will implement and see its output and limitations and create a customized InceptionV3 model. To balance the efficiency and accuracy, the model is fine-tuned considering the nature of the target problem and fire data. In addition, the method would be designed to be well generalized for unseen data, which offers effective generalization and reduces the number of false alarms.

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