Update README.md

README.md

@@ -1,7 +1,7 @@
 ---
-model_name: Wheat Anomaly Detection Model
+model_name: Wheat Anomaly Detection Model (TensorFlow)
 tags:
-  - pytorch
+  - tensorflow
   - resnet
   - agriculture
   - anomaly-detection
@@ -10,59 +10,17 @@ tags:
   - pest-detection
   - agricultural-ai
 license: apache-2.0
-library_name: pytorch
+library_name: tensorflow
 datasets:
-  - wheat-dataset   # Replace with the actual dataset name on Hugging Face if available
+  - wheat-dataset   # Replace with the actual dataset name if available
 model_type: resnet50
 preprocessing:
   - resize: 256
   - center_crop: 224
   - normalize: [0.485, 0.456, 0.406]
   - normalize_std: [0.229, 0.224, 0.225]
-framework: pytorch
+framework: tensorflow
 task: image-classification
 pipeline_tag: image-classification
 
 ---
-
-# Wheat Anomaly Detection Model
-
-## Model Overview
-
-This model is trained to detect anomalies in wheat crops, such as pest infections (e.g., Fall Armyworm), diseases, or nutrient deficiencies. The model is based on the **ResNet50** architecture and was fine-tuned on a dataset of wheat images.
-
-## Model Details
-
-- **Model Architecture**: ResNet50
-- **Number of Classes**: 2 (Fall Armyworm, Healthy Wheat)
-- **Input Shape**: 224x224 pixels, 3 channels (RGB)
-- **Training Framework**: PyTorch
-- **Optimizer**: Adam
-- **Learning Rate**: 0.001
-- **Epochs**: 20
-- **Batch Size**: 32
-
-## Training
-
-The model was fine-tuned using a balanced dataset with images of healthy wheat and wheat infected by fall armyworms. The training involved transferring knowledge from a pretrained ResNet50 model and adjusting the final classification layer for the binary classification task.
-
-### Dataset
-
-The model was trained on a dataset hosted on Hugging Face. You can access it here:
-
-- **Dataset**: `your_huggingface_username/your_dataset_name`
-
-## How to Use
-
-To load and use this model in PyTorch, follow the steps below:
-
-### 1. Load the Model
-
-```python
-import torch
-import timm
-
-# Load the pre-trained model (fine-tuned ResNet50 for wheat anomaly detection)
-model = timm.create_model("resnet50", pretrained=False, num_classes=2)
-model.load_state_dict(torch.load("path_to_saved_model.pth"))
-model.eval()