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--- |
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tags: |
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- autotrain |
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- image-classification |
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- pytorch |
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- transformers |
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library_name: pytorch |
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base_model: microsoft/resnet-50 |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg |
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example_title: Tiger |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg |
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example_title: Teapot |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg |
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example_title: Palace |
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datasets: |
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- A2H0H0R1/plant-disease-new |
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license: apache-2.0 |
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--- |
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# Model Trained Using AutoTrain |
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- Problem type: Image Classification |
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## Validation Metrics |
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No validation metrics available |
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#Inference Pipeline |
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- |
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-Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: |
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```python |
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from transformers import AutoModelForImageClassification, AutoProcessor |
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model = AutoModelForImageClassification.from_pretrained("ozair23/autotrain-w5nk2-rvmqx") |
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processor = AutoProcessor.from_pretrained("ozair23/autotrain-w5nk2-rvmqx") |
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def predict(image): |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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return outputs |
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``` |
