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