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?
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 ?
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
this is so so so helpful! i have been stuck for weeks and this is amazing!
Glad it helped!
Perfect content
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
Shouldn't we redefine FC to 2 classes?
THANK YOU SO MUCH!!!! I LOVE YOU!!!
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!
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.
Thank you, Aarohi! :)
Welcome :)
Thank you ma, but how did you get mean and standard deviation?
they are gotten from imagenet after working on millions of images
plz provided the updated code of vgg-16/ resnet50 or any that resolved the version error thanks
What an amazing video
Glad it is useful!
Well explained.❤❤❤
Any videos of medical image classification available
Not yet!
Mam make a video on YOLOv8 model customization or change layer
I will try
Awesome
Thanks!
Nice video
Thanks
Thank you very much
You are welcome
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