Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+base_model:
+- google/efficientnet-b0
+---
+
+EfficientNet-B0 Model for Image Classification
+
+This repository contains an EfficientNet-B0 model trained on a custom dataset for image classification tasks.
+
+Model Details
+
+- Architecture: EfficientNet-B0
+- Input Size: 224x224 RGB images
+- Number of Classes: 10
+- Dataset: Custom dataset with 10 categories
+- Optimizer: AdamW
+- Loss Function: CrossEntropyLoss
+- Validation Accuracy: 85.3%
+- Device Used for Training: CUDA (GPU)
+
+Usage
+
+Load the Model
+To load the model, use the following code:
+
+```
+import torch
+
+Load model and metadata
+model = torch.load("efficientnet-results-and-model.pth", map_location="cpu")
+
+Access class-to-index mapping
+
+class_to_idx = model['class_to_idx']
+
+Load the state dictionary
+state_dict = model['model_state_dict']
+
+Reconstruct EfficientNet-B0
+from torchvision.models import efficientnet_b0
+model = efficientnet_b0(weights=None)
+model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, len(class_to_idx))
+model.load_state_dict(state_dict)
+model.eval()
+
+print("Model successfully loaded!")
+
+Training Details
+Learning Rate: 0.001
+Batch Size: 32
+Epochs: 3
+Augmentations:
+Random Resized Crop
+Horizontal Flip
+Color Jitter
+Normalization (mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225])
+
+Files in this Repository
+best_model.pth: Trained model weights
+efficientnet.json: Model configuration file
+README.md: Documentation for this model
+efficientnet.txt: Training Results
+
+Acknowledgments
+Framework: PyTorch
+Pretrained Weights: TorchVision
+Training: Mixed precision using torch.cuda.amp for efficient training on GPU.