Update README.md

README.md

@@ -1,5 +1,5 @@
 # Model Card for ResNet-152 Text Detector
-This model was trained with the intent to quickly classify whether or not an image contains legible text or not. It was trained as a binary classification problem on the COCO-Text dataset together with some images from LLaVAR. This came out to a total of ~70k images, where 50% of them had text and 50% of them had no legible text.
+This model was trained with the intent to quickly classify whether or not an image contains legible text or not. It was trained as a binary classification problem on the COCO-Text dataset together with some images from LLaVAR. This came out to a total of ~140k images, where 50% of them had text and 50% of them had no legible text.
 
 # Model Details
 ## How to Get Started with the Model
@@ -16,7 +16,7 @@ model = AutoModelForImageClassification.from_pretrained(
 processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50", do_resize=False)
 
 url = "http://images.cocodataset.org/train2017/000000044520.jpg"
-image = Image.open(requests.get(url, stream=True).raw).convert('RGB').resize((256,256))
+image = Image.open(requests.get(url, stream=True).raw).convert('RGB').resize((300,300))
 
 inputs = processor(image, return_tensors="pt").pixel_values