metadata
license: mit
datasets:
- competitions/aiornot
language:
- en
metrics:
- accuracy
tags:
- classification
- computer vision
Usage:
Follow the following code example to use this model.
# import libraries
from transformers import AutoModel, AutoModelForImageClassification
import torch
from datasets import load_dataset
# load dataset
dataset = load_dataset("competitions/aiornot")
# list of images
images = dataset["test"][10:20]["image"]
# load models
feature_extractor = AutoModel.from_pretrained(
"RishiDarkDevil/ai-image-det-resnet152", trust_remote_code=True).to('cuda')
classifier = AutoModelForImageClassification.from_pretrained(
"RishiDarkDevil/ai-image-det-resnet152", trust_remote_code=True).to('cuda')
# extract features from images
inputs = feature_extractor(images)
# classification using extracted features
with torch.no_grad():
logits = classifier(inputs)['logits']
# model predicts one of the 2 classes
predicted_label = logits.argmax(-1)
# predictions
print(predicted_label) # 0 is Not AI, 1 is AI
Backbone for Feature Extraction: ResNet152
Performance
- Trained MLP Fine-tuning layers for 150 epochs.
- Accuracy: 0.9250 on validation data (~5% of the training data).