Update README.md

README.md

@@ -16,45 +16,6 @@ SerialNo_Height_Weight_Gender_Age.png/jpg
 Example: 1021_5.5h_51w_female_26a.png
 ```
 
-## Setup
-
-### 1. Install Dependencies
-
-```bash
-pip install -r ../requirements.txt
-```
-
-Key dependencies:
-- `torch>=2.0.0` - PyTorch for deep learning
-- `transformers>=4.30.0` - Hugging Face transformers library
-- `accelerate>=0.20.0` - For efficient training
-
-### 2. Verify Dataset Location
-
-Ensure your dataset is located at:
-```
-D:\fit_model\finetune_model\Celeb-FBI Dataset
-```
-
-## Usage
-
-### Step 1: Parse Dataset (Optional)
-
-If you haven't created the CSV file yet, run:
-
-```bash
-python dataset_parser.py
-```
-
-This will create `dataset_labels.csv` with parsed height and weight labels from filenames.
-
-### Step 2: Fine-tune the Model
-
-Run the training script:
-
-```bash
-python train_vit.py
-```
 
 #### Training Parameters (Optimized for 4GB GPU)
 
@@ -65,18 +26,7 @@ The script uses memory-efficient techniques:
 - **Learning rate**: 2e-5 (standard for fine-tuning)
 - **Epochs**: 10 (adjustable)
 
-#### Custom Training Arguments
-
-```bash
-python train_vit.py \
-    --dataset_dir "D:\fit_model\finetune_model\Celeb-FBI Dataset" \
-    --csv_file "D:\fit_model\finetune_model\dataset_labels.csv" \
-    --output_dir "D:\fit_model\finetune_model\checkpoints" \
-    --batch_size 4 \
-    --accumulation_steps 8 \
-    --epochs 10 \
-    --learning_rate 2e-5
-```
+
 
 **Arguments:**
 - `--dataset_dir`: Path to Celeb-FBI Dataset directory
@@ -104,15 +54,6 @@ The training script includes several optimizations:
 3. **Mixed Precision**: Uses FP16 training to reduce memory usage by ~50%
 4. **Efficient Data Loading**: Uses `pin_memory` and multiple workers for faster data transfer
 
-## Output Files
-
-After training, the following files will be created in the output directory:
-
-- `best_model.pt`: Best model checkpoint (lowest validation loss)
-- `final_model.pt`: Final model after all epochs
-- `checkpoint_epoch_N.pt`: Periodic checkpoints every 5 epochs
-- `dataset_stats.json`: Dataset statistics (mean, std) for denormalization
-
 ## Loading the Trained Model
 
 ```python
@@ -120,7 +61,7 @@ import torch
 from model import ViTHeightWeightModel
 
 # Load checkpoint
-checkpoint = torch.load('checkpoints/best_model.pt')
+checkpoint = torch.load('Rithankoushik/Finetuned_VITmodel/best_model.pt')
 dataset_stats = checkpoint['dataset_stats']
 
 # Initialize model
@@ -140,7 +81,7 @@ import torch
 from model import ViTHeightWeightModel
 
 # Load model and processor
-checkpoint = torch.load('checkpoints/best_model.pt')
+checkpoint = torch.load('Rithankoushik/Finetuned_VITmodel/best_model.pt')
 model = ViTHeightWeightModel(model_name=checkpoint['model_name'])
 model.load_state_dict(checkpoint['model_state_dict'])
 model.eval()
@@ -186,31 +127,6 @@ If you encounter OOM errors:
 - Use SSD storage for faster data loading
 - Consider using a smaller model variant if needed
 
-## Files Structure
-
-```
-finetune_model/
-├── Celeb-FBI Dataset/          # Dataset directory
-├── dataset_parser.py           # Parse filenames to extract labels
-├── vit_dataset.py              # PyTorch Dataset class
-├── model.py                    # ViT model architecture
-├── train_vit.py                # Main training script
-├── dataset_labels.csv          # Generated CSV with labels
-├── checkpoints/                # Saved model checkpoints
-│   ├── best_model.pt
-│   ├── final_model.pt
-│   └── dataset_stats.json
-└── README.md                   # This file
-```
-
-## Notes
-
-- The model normalizes height and weight during training for better convergence
-- Training time: ~2-4 hours on RTX 3050 (4GB) for 10 epochs
-- The model uses a multi-task approach, learning height and weight simultaneously
-- Early stopping can be implemented by monitoring validation loss
-
-