Topic: inceptionv3-model Goto Github
Some thing interesting about inceptionv3-model
Some thing interesting about inceptionv3-model
inceptionv3-model,ios app using CoreML , Training model : Inceptionv3
User: ahlam96
inceptionv3-model,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.
User: harmanveer-2546
inceptionv3-model,This repo contains the python codes of my final thesis "Analysis of leaf species and detection of diseases using image processing and machine learning methods".
User: iremakalp
inceptionv3-model,Implementation of Image Captioning Model using CNNs and LSTMs
User: ivishalr
inceptionv3-model,This is a project for the udacity nano degree on neural networks and this involves a dog breed classifier using CNN.
User: panda-balarka
inceptionv3-model,Transfer Learning Method is used to detect whether it is cat or dog.
User: sayannath
inceptionv3-model,A Image Classifier which uses InceptionV3 model for multiclassification by Training the Last Layer of the Model with custom Images.
User: shankrsingh
inceptionv3-model,An iOS app used to detect whether the image captured is of Hotdog or not.
User: shubham101096
inceptionv3-model,A Flower Classification Project using pre trained model - InceptionV3 with 78 % accuracy which classifies five different types of flowers.
User: styxoo7
Home Page: https://huggingface.co/spaces/Re1e9/Flower_Classification_using_InceptionV3
inceptionv3-model,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.
User: tushartotla12
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