ResNet Image Classifier
Upload an image and let ResNet identify it using deep learning.
Prediction: Waiting for image...
🧪 Lab Instructions: How to Use ResNet
- Click “Choose File” to upload an image (JPG works best).
- Click “Classify” to submit the image to the ResNet model.
- View the predicted label below the button.
- Try different objects, animals, logos, or scenery to test accuracy.
- Now upload the same photo to your favorite GenAI and ask it to classify the image.
What a difference a decade can make huh? This model, ResNet-18 can only choose from 1000 categories, and makes best effort to classify an image.
📚 A Brief History of ResNet
ResNet, or Residual Network, was introduced by Microsoft Research in 2015 and won the ImageNet competition that year with a top-5 error rate of just 3.57%. It addressed the vanishing gradient problem that plagued deep networks by introducing **skip connections**, allowing gradients to flow directly through the network. This architecture made it possible to train extremely deep neural networks (over 100 layers), which dramatically improved performance in image recognition. ResNet set a new standard for convolutional architectures and is still widely used in computer vision tasks today.