69 lines
2.8 KiB
Markdown
69 lines
2.8 KiB
Markdown
# Cat-Dog Classification Model
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## Introduction
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This repository contains a Cat-Dog classification model based on the ResNet-50 architecture. The model is trained to distinguish between images of cats and dogs.
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## ResNet Model
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ResNet-50 is a deep convolutional neural network with 50 layers. It is designed to overcome the vanishing gradient problem, which is common in very deep networks, by using skip connections or residuals. This allows the network to be significantly deeper while still being easy to optimize.
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## Training
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The model is trained on a dataset of cat and dog images. The training process involves the following steps:
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1. **Data Preprocessing**: Images are resized, cropped, and normalized.
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2. **Model Initialization**: A pre-trained ResNet-50 model is loaded and the final fully connected layer is adjusted to output two classes (cat and dog).
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3. **Training Loop**: The model is trained using a standard training loop with stochastic gradient descent (SGD) and a learning rate scheduler.
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4. **Model Evaluation**: The best model is selected based on validation accuracy and saved for inference.
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### Training Code
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Here is a simplified version of the training code:
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[train.py](./train.py)
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## Inference
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To perform inference, you can use the following code. The inference is based on the transformer model with model ID `ailb/resnet-dogcat`.
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### Inference Code
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**You need set env HF_ENDPOINT=http://10.0.101.71**
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```python
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from transformers import AutoImageProcessor, ResNetForImageClassification
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from PIL import Image
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import requests
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import torch
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# Load model
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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processor = AutoImageProcessor.from_pretrained("ailab/resnet-dogcat")
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model = ResNetForImageClassification.from_pretrained("ailab/resnet-dogcat")
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inputs = processor(image, return_tensors="pt")
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with torch.no_grad():
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logits = model(**inputs).logits
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# model predicts one of the 1000 ImageNet classes
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predicted_label = logits.argmax(-1).item()
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print(model.config.id2label[predicted_label])
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```
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```python
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from transformers import pipeline
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import gradio as gr
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pipe = pipeline("image-classification", model="ailab/resnet-dogcat")
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gr.Interface.from_pipeline(pipe).launch(server_name="0.0.0.0")
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```
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*[{'label': 'cat', 'score': 0.5016139149665833}, {'label': 'dog', 'score': 0.49838611483573914}]*
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## Conclusion
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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. |