Update model card

README.md

@@ -1,25 +1,25 @@
 ---
 tags:
 - image-classification
-- cifar100
+- imagenette
 - resnet50-pytorch
 - pytorch
 datasets:
-- cifar100
+- imagenette
 metrics:
 - accuracy
 ---
 
-# CIFAR-100 resnet50-pytorch
+# ImageNette resnet50-pytorch
 
 ## Model Description
 
-resnet50-pytorch trained on CIFAR-100 dataset with advanced augmentation techniques.
+resnet50-pytorch trained on 10-class ImageNet subset (ImageNette) with advanced augmentation techniques.
 
 ### Model Architecture
 - **Architecture**: resnet50-pytorch
-- **Dataset**: CIFAR-100
-- **Classes**: 100
+- **Dataset**: ImageNette
+- **Classes**: 10
 
 ### Training Configuration
 - **Batch Size**: 128
@@ -32,15 +32,15 @@ resnet50-pytorch trained on CIFAR-100 dataset with advanced augmentation techniq
 - **Gradient Clipping**: 1.0
 
 ### Performance
-- **Best Test Accuracy**: 23.80%
+- **Best Test Accuracy**: 24.28%
 - **Total Epochs Trained**: 2
-- **Final Train Accuracy**: 16.38%
-- **Final Test Accuracy**: 23.80%
+- **Final Train Accuracy**: 16.16%
+- **Final Test Accuracy**: 24.28%
 
 ### Training History
 - **Best Epoch**: 2
-- **Train Loss**: 2.5457 → 2.3992
-- **Test Loss**: 3.7171 → 2.1703
+- **Train Loss**: 2.4702 → 2.3422
+- **Test Loss**: 2.6904 → 2.1273
 
 ### Usage
 
@@ -59,16 +59,16 @@ checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)
 
 # Load model (you'll need to have the model definition)
 # from models import get_model
-# model = get_model('resnet50-pytorch', num_classes=100)
+# model = get_model('resnet50-pytorch', num_classes=10)
 # model.load_state_dict(checkpoint['model_state_dict'])
 # model.eval()
 ```
 
 ### Training Details
-- **Dataset**: CIFAR-100 (50,000 train, 10,000 test)
-- **Classes**: 100
-- **Image Size**: 32×32
-- **Normalization**: mean=(0.5071, 0.4865, 0.4409), std=(0.2673, 0.2564, 0.2761)
+- **Dataset**: ImageNette (9469 train, 3925 test)
+- **Classes**: 10
+- **Image Size**: 160×160 or 224×224
+- **Normalization**: mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)
 
 ### Files
 - `best_model.pth` - Best performing model checkpoint
@@ -82,8 +82,8 @@ MIT
 
 ### Citation
 ```bibtex
-@misc{resnet50-pytorch-cifar100,
-  title = {CIFAR-100 resnet50-pytorch},
+@misc{resnet50-pytorch-imagenette,
+  title = {ImageNette resnet50-pytorch},
   year = {2025},
   publisher = {HuggingFace},
   url = {https://huggingface.co/pandurangpatil/imagenet10trial}