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📃: adding Dog breed classification model #139
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@jaidh01 This project is not a collection of different deployed ML model or python programs. It's created from the non-code user point of view. So, There is no relevance for this feature. This project can be modified as if you can provide the details of different dog breeds by scanning the image or by scanning from a live camera. |
Ya, it will take an image as an input. And then it will tell us the dog breed by analyzing the input image. |
Not only the dog breed but the necessary details related to that dog breed. |
Hey, I am working on it, it is taking some time as I have to recreate the data to display the discription of the dog breed and after that I have to retrain the model. |
Ok np, But you can use any api or internet to get information about that breed after classify by the model. |
@jaidh01 whats the update on this? |
I am working on it, actually I am facing some issues in my trained model, because of which I am not able to integrate it in overall project. |
@jaidh01 @Avdhesh-Varshney i would like to take this on my one question is like we will save pretrained weights for it right but how will we access it through the file is it with some gdrive or something else |
Yes, I'll share a folder with write access on gdrive on demand. |
great will soon add a pr for it assign me this issue |
@Avdhesh-Varshney i got the model could u temprarily share me the drive i will upload the model |
Mention your email id, I will provide the access. Then review on mail. |
@Avdhesh-Varshney @jaidh01 |
after this it stoped because of early stopping |
@Avdhesh-Varshney under which label should i make this lik object detection or chatbot ? |
Object Detection Models! |
@Avdhesh-Varshney |
@lukiod you should communicate through discord. |
🔴 Title: Dog breed classification
🔴 Aim: To build a model which is able to classify dog breed.
🔴 Brief Explanation:
Using over 10,000+ images and MobileNetV2 with transfer learning, this model would be able to classifies different dog breeds with impressive accuracy.
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Screenshots 📷
✅ To be Mentioned while taking the issue :
Happy Contributing 🚀
All the best. Enjoy your open source journey ahead. 😎
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