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EfficientNet.json

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+{
+  "model_architecture": "efficientnet_b0",
+  "input_size": [3, 224, 224],
+  "num_classes": 10,
+  "dataset": "Custom Dataset",
+  "loss_function": "CrossEntropyLoss",
+  "optimizer": "AdamW",
+  "learning_rate": 0.001,
+  "epochs": 3,
+  "batch_size": 32,
+  "validation_accuracy": 85.3,
+  "class_to_idx": {"class_0": 0, "class_1": 1, "class_2": 2, "class_3": 3, "class_4": 4, "class_5": 5, "class_6": 6, "class_7": 7, "class_8": 8, "class_9": 9},
+  "device": "cuda",
+  "description": "This is an EfficientNet-B0 model trained on a custom dataset for image classification tasks."
+}

EfficientNetREADME.md

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+# 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:
+
+```python
+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
+efficientnet-results-and-model.pth: Trained model weights
+efficientnet-config.json: Model configuration file
+efficientnet-README.md: Documentation for this model
+Acknowledgments
+Framework: PyTorch
+Pretrained Weights: TorchVision
+Training: Mixed precision using torch.cuda.amp for efficient training on GPU.

best_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:48f6d560a736d516aa22f49f2dd388285a91ae42ab00679de29b09d216df7616
+size 50127726

efficientnet_results.txt

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+Using device: cpu
+Epoch 1/3 - Loss: 354.2215, Accuracy: 59.81%
+Validation Accuracy: 91.69%
+Epoch 2/3 - Loss: 138.5075, Accuracy: 82.07%
+Validation Accuracy: 93.89%
+Epoch 3/3 - Loss: 102.8507, Accuracy: 86.63%
+Validation Accuracy: 95.23%
+Training complete. Best validation accuracy: 95.23%