|
--- |
|
license: mit |
|
datasets: |
|
- competitions/aiornot |
|
language: |
|
- en |
|
metrics: |
|
- accuracy |
|
- f1 |
|
pipeline_tag: image-classification |
|
--- |
|
|
|
Fatima 2023 Application |
|
|
|
|
|
This project is about an image classification task of artificial and natural classes. |
|
|
|
|
|
Setup: |
|
|
|
pip install -r requirements.txt |
|
|
|
|
|
Inference: |
|
|
|
|
|
from torchvision import transforms |
|
from PIL import Image |
|
import torch |
|
|
|
|
|
inference_transform = transforms.Compose([ |
|
transforms.Resize(128), |
|
transforms.ToTensor(), |
|
transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], |
|
std=[0.2023, 0.1994, 0.2010]), |
|
]) |
|
|
|
#load image and model |
|
img_example = Image.open("image_example.png").convert('RGB') |
|
print("image loaded!") |
|
model_loaded = torch.load("fatima_challenge_model_exp3.pt") |
|
model_loaded.eval() |
|
print("model loaded!") |
|
|
|
|
|
img_example_transformed = inference_transform(img_example) |
|
out = model_loaded(img_example_transformed.to(torch.device("cuda:0")).unsqueeze(0)) # Generate predictions |
|
_, outs = torch.max(out, 1) |
|
prediction = "natural" if int(outs.cpu().numpy())==0 else "artificial" |
|
print("prediction = ",prediction) |
|
|
|
|