I tried image classification using trained model and its working well but some images could not find perfectly in that time have to get that image and label from users so my doubt is..Is it possible to add new data into already trained model?
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No model can predict with 100% accuracy if it does it's an ideal model. And if you want to add more data to your train model you have to retrain the model with the new data. so if you want to get better accuracy train your model with more data. Without retraining, you can't add data to you trained model.coding_ninza– coding_ninza2020-07-15 06:33:50 +00:00Commented Jul 15, 2020 at 6:33
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No, during inference time you use the weights of the trained model for predictions. Which basically means that at the time your model is deployed the capabilities of your image classifier are fixed by the weights. If you wish to improve your model, you would have to retrain your model with the new - data. However, there is another paradigm of learning called "Online Learning" where the model is continuously learning and modifying the weights. In this case your weights are not fixed and your model is continuously updating its weights with each training input. However afaik this is not usually recommended for CNNs, because the backward pass of gradients is computationally intensive and your inference will be slow because of this.
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DejaVuSansMono
This is all true, although with an ASIC like TPU you can actually get this to work because it speeds up the inference time considerably.
No model can predict with 100% accuracy if it does it's an ideal model. And if you want to add more data to your train model you have to retrain the model with the new data. Having more data is always a good idea. It allows the “data to tell for itself,” instead of relying on assumptions and weak correlations. Presence of more data results in better and accurate models. So if you want to get better accuracy you have to train your model with more data. Without retraining, you can't add data to your trained model.
