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--- |
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language: |
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- en |
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pipeline_tag: image-classification |
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tags: |
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- cards |
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- computervision |
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- imageclassification |
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--- |
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# Cards Image Classification Model |
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This model is trained to classify images of cards using a custom dataset. |
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## Model Details |
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- Architecture: ResNet18 |
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- Dataset: Cards Image Dataset-Classification |
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- Number of Classes: 53 |
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- Training Epochs: 25 |
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- Optimizer: Adam |
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- Loss Function: CrossEntropyLoss |
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## Usage |
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To use this model, follow the example code below: |
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```python |
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from transformers import AutoModelForImageClassification, AutoFeatureExtractor |
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from PIL import Image |
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import requests |
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model_name = "sabrilben/cards_image_classification" |
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model = AutoModelForImageClassification.from_pretrained(model_name) |
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_name) |
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url = "path/to/image.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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logits = outputs.logits |
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predicted_class_idx = logits.argmax(-1).item() |
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print("Predicted class:", model.config.id2label[predicted_class_idx]) |
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``` |
