hey i have a question that,as we have copied the weights and biases from the loaded model to the new model, then shouldn't we use new_model for performing inference as well?
hi good tuto. One question i try to create my own dataset. You don't tell in the video if the label process is required ? with labelimg for example ? thx
Mam. Freezing all layers except the final classification layer is called transfer learning. And customized with our own dataset so it's also fine tuned model. The way I understand is correct ?
this is so so so helpful! i have been stuck for weeks and this is amazing!
Glad it helped!
THANK YOU SO MUCH!!!! I LOVE YOU!!!
Perfect content
What an amazing video
Glad it is useful!
Thank you, Aarohi! :)
Welcome :)
Shouldn't we redefine FC to 2 classes?
hey i have a question that,as we have copied the weights and biases from the loaded model to the new model, then shouldn't we use new_model for performing inference as well?
Yes
hi good tuto. One question i try to create my own dataset. You don't tell in the video if the label process is required ? with labelimg for example ?
thx
For Image classification task, You don't need to annotate the images. Just create separate folder for each class and then put related images.
Mam. Freezing all layers except the final classification layer is called transfer learning. And customized with our own dataset so it's also fine tuned model. The way I understand is correct ?
Yes, your understanding is correct!
plz provided the updated code of vgg-16/ resnet50 or any that resolved the version error thanks
Well explained.❤❤❤
Any videos of medical image classification available
Not yet!
Thank you very much
You are welcome
Awesome
Thanks!
Mam make a video on YOLOv8 model customization or change layer
I will try
Thank you ma, but how did you get mean and standard deviation?
they are gotten from imagenet after working on millions of images
Nice video
Thanks
Your link is broken
github.com/AarohiSingla/Image-Classification-Using-Pytorch
it is giving error I am taking 11 classes for this for emoji prediction
from PIL import Image
from torchvision import transforms
# Load and preprocess the unseen image
image_path = '/content/test.png' # Replace with the path to your image
image = Image.open(image_path)
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
input_tensor = preprocess(image)
input_batch = input_tensor.unsqueeze(0) # Add a batch dimension
RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 0
Are you married?
Yes