Update README.md

README.md

@@ -38,24 +38,23 @@ No validation metrics available
 
 ### How to use
 
-Here is how to use this model to classify an image of :
+Here is how to use this model to identify a neutrophil from a picture of a blood sample:
 
 ```python
-from transformers import AutoImageProcessor, ResNetForImageClassification
-import torch
-from datasets import load_dataset
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
+import requests
 
-dataset = load_dataset("huggingface/cats-image")
-image = dataset["test"]["image"][0]
+processor = AutoImageProcessor.from_pretrained("NeuronZero/MRI-Reader")
+model = AutoModelForImageClassification.from_pretrained("NeuronZero/MRI-Reader")
 
-processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
-model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
+#dataset URL: https://www.kaggle.com/datasets/paultimothymooney/blood-cells
 
-inputs = processor(image, return_tensors="pt")
+image_url = https://storage.googleapis.com/kagglesdsdata/datasets/9232/29380/dataset-master/dataset-master/JPEGImages/BloodImage_00014.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=databundle-worker-v2%40kaggle-161607.iam.gserviceaccount.com%2F20240404%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240404T094650Z&X-Goog-Expires=345600&X-Goog-SignedHeaders=host&X-Goog-Signature=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
+image = Image.open(requests.get(image_url, stream=True).raw)
 
-with torch.no_grad():
-    logits = model(**inputs).logits
-
-# model predicts one of the 1000 ImageNet classes
-predicted_label = logits.argmax(-1).item()
-print(model.config.id2label[predicted_label])
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_class_idx = logits.argmax(-1).item()
+print("Predicted class:", model.config.id2label[predicted_class_idx])