Cards Image Classification Model

This model is trained to classify images of cards using a custom dataset.

Model Details

Architecture: ResNet18
Dataset: Cards Image Dataset-Classification
Number of Classes: 53
Training Epochs: 25
Optimizer: Adam
Loss Function: CrossEntropyLoss

Usage

To use this model, follow the example code below:

from transformers import AutoModelForImageClassification, AutoFeatureExtractor
from PIL import Image
import requests

model_name = "sabrilben/cards_image_classification"
model = AutoModelForImageClassification.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)

url = "path/to/image.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])