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@@ -19,8 +19,19 @@ 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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  #Inference Pipeline
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- --
 
 
 
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  from transformers import AutoModelForImageClassification, AutoProcessor
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  model = AutoModelForImageClassification.from_pretrained("ozair23/autotrain-w5nk2-rvmqx")
@@ -30,10 +41,4 @@ 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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-
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- # Model Trained Using AutoTrain
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-
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- - Problem type: Image Classification
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-
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- ## Validation Metrics
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- No validation metrics available
 
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  - A2H0H0R1/plant-disease-new
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  license: apache-2.0
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  ---
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+
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+ # Model Trained Using AutoTrain
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+
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+ - Problem type: Image Classification
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+
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+ ## Validation Metrics
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+ No validation metrics available
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+
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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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+
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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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  inputs = processor(images=image, return_tensors="pt")
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  outputs = model(**inputs)
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  return outputs
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+ ```