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@ -53,6 +53,14 @@ predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label]) print(model.config.id2label[predicted_label])
``` ```
```python
import gradio as gr
pipe = pipeline("image-classification", model="ailab/resnet-dogcat")
gr.Interface.from_pipeline(pipe).launch(server_name="0.0.0.0")
```
![1716790696165.png](https://img2.imgtp.com/2024/05/27/hIlNpCRj.png)
## Conclusion ## Conclusion
This repository provides a comprehensive solution for training and performing inference on a Cat-Dog classification task using a ResNet-50 model. The training script demonstrates how to preprocess data, train the model, and save the trained model. The inference script shows how to use the trained model to classify new images. This repository provides a comprehensive solution for training and performing inference on a Cat-Dog classification task using a ResNet-50 model. The training script demonstrates how to preprocess data, train the model, and save the trained model. The inference script shows how to use the trained model to classify new images.