|
--- |
|
datasets: |
|
- cifar10 |
|
metrics: |
|
- accuracy |
|
library_name: pytorch |
|
pipeline_tag: image-classification |
|
--- |
|
|
|
# custom-cnn-cifar2 |
|
|
|
Custom convolutional neural network (CNN) trained on CIFAR-2 (a subset of CIFAR-10 for classifying 'airplane' vs. 'bird'). |
|
|
|
This model pertains to Exercise 1 of Chapter 8 of the book "Deep Learning with PyTorch" by Eli Stevens, Luca Antiga, and Thomas Viehmann. |
|
|
|
**Note:** In the exercise, we tried out `(5, 5)` and `(1, 3)` convolution kernel sizes. However, these didn't outperform the baseline network with `(3, 3)` kernel size. Hence, this checkpoint sticks to the `(3, 3)` kernel size. |
|
|
|
Code: https://github.com/sambitmukherjee/dlwpt-exercises/blob/main/chapter_8/exercise_1.ipynb |
|
|
|
Experiment tracking: https://wandb.ai/sadhaklal/custom-cnn-cifar2 |
|
|
|
## Usage |
|
|
|
``` |
|
!pip install -q datasets |
|
|
|
from datasets import load_dataset |
|
|
|
cifar10 = load_dataset("cifar10") |
|
label_map = {0: 0, 2: 1} |
|
class_names = ['airplane', 'bird'] |
|
cifar2_train = [(example['img'], label_map[example['label']]) for example in cifar10['train'] if example['label'] in [0, 2]] |
|
cifar2_val = [(example['img'], label_map[example['label']]) for example in cifar10['test'] if example['label'] in [0, 2]] |
|
|
|
example = cifar2_val[0] |
|
img, label = example |
|
|
|
import torch |
|
from torchvision.transforms import v2 |
|
|
|
tfms = v2.Compose([ |
|
v2.ToImage(), |
|
v2.ToDtype(torch.float32, scale=True), |
|
v2.Normalize(mean=[0.4915, 0.4823, 0.4468], std=[0.2470, 0.2435, 0.2616]) |
|
]) |
|
img = tfms(img) |
|
batch = img.unsqueeze(0) |
|
|
|
import torch.nn as nn |
|
import torch.nn.functional as F |
|
from huggingface_hub import PyTorchModelHubMixin |
|
|
|
class Net(nn.Module, PyTorchModelHubMixin): |
|
def __init__(self): |
|
super().__init__() |
|
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1, stride=1) |
|
self.conv2 = nn.Conv2d(16, 8, kernel_size=3, padding=1, stride=1) |
|
self.fc1 = nn.Linear(8 * 8 * 8, 32) |
|
self.fc2 = nn.Linear(32, 2) |
|
|
|
def forward(self, x): |
|
out = F.max_pool2d(torch.tanh(self.conv1(x)), kernel_size=2, stride=2) # Output shape: (batch_size, 16, 16, 16) |
|
out = F.max_pool2d(torch.tanh(self.conv2(out)), kernel_size=2, stride=2) # Output shape: (batch_size, 8, 8, 8) |
|
out = out.view(-1, 8 * 8 * 8) # Output shape: (batch_size, 512) |
|
out = torch.tanh(self.fc1(out)) # Output shape: (batch_size, 32) |
|
out = self.fc2(out) # Output shape: (batch_size, 2) |
|
return out |
|
|
|
model = Net.from_pretrained("sadhaklal/custom-cnn-cifar2") |
|
model.eval() |
|
|
|
with torch.no_grad(): |
|
logits = model(batch) |
|
pred = logits[0].argmax().item() |
|
proba = torch.softmax(logits, dim=1) |
|
|
|
print(f"Predicted class: {class_names[pred]}") |
|
print(f"Predicted class probabilities ('airplane' vs. 'bird'): {proba[0].tolist()}") |
|
``` |
|
|
|
## Metric |
|
|
|
Accuracy on `cifar2_val`: 0.8995 |
