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README.md

@@ -26,17 +26,17 @@ This model is a ResNet50 architecture trained on the ImageNet dataset for image
 ## Usage
 
 ```python
-from transformers import AutoImageProcessor, AutoModelForImageClassification
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 import torch
 from PIL import Image
 
-# Load model and processor
+# Load model and feature extractor
 model = AutoModelForImageClassification.from_pretrained("jatingocodeo/ImageNet")
-processor = AutoImageProcessor.from_pretrained("jatingocodeo/ImageNet")
+feature_extractor = AutoFeatureExtractor.from_pretrained("jatingocodeo/ImageNet")
 
 # Prepare image
 image = Image.open("path/to/image.jpg")
-inputs = processor(image, return_tensors="pt")
+inputs = feature_extractor(image, return_tensors="pt")
 
 # Get predictions
 with torch.no_grad():
@@ -56,5 +56,6 @@ The model was trained on the ImageNet dataset with the following configuration:
 ## Preprocessing
 
 The model expects images to be preprocessed as follows:
-- Resize to 224x224
+- Resize shortest edge to 224
+- Center crop to 224x224
 - Normalize with mean [0.485, 0.456, 0.406] and std [0.229, 0.224, 0.225]

preprocessor_config.json

@@ -1 +1 @@
-{"do_normalize": true, "do_resize": true, "image_mean": [0.485, 0.456, 0.406], "image_processor_type": "AutoImageProcessor", "image_std": [0.229, 0.224, 0.225], "resample": 2, "size": {"height": 224, "width": 224}}
+{"crop_size": {"height": 224, "width": 224}, "do_center_crop": true, "do_normalize": true, "do_resize": true, "feature_extractor_type": "ImageFeatureExtractor", "image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225], "resample": 2, "size": {"shortest_edge": 224}}