README.md

---
license: mit
datasets:
- alkzar90/NIH-Chest-X-ray-dataset
language:
- en
metrics:
- roc_auc
base_model:
- galactixx/Torchvision-DenseNet121-a639ec97
pipeline_tag: image-classification
tags:
- Ai
- fine-tuned
- image-classification
---
# DenseNet121 Image Classifier

This model is a custom image classifier built with **PyTorch**, leveraging a pretrained **DenseNet121** backbone. The model extracts rich visual features from the pretrained weights and uses a custom classification head to predict the target categories.

## Model Details

- **Developed by:** [Aditya chaubey]
- **Model type:** Image Classification (Convolutional Neural Network)
- **Architecture:** DenseNet121 (Dense Convolutional Network)
- **Library:** PyTorch
- **License:** Mit
- **Pretrained Backbone:** torchvision.models.densenet121

## Intended Uses & Limitations

### Intended Uses
- Automated classification of images into [14] categories.
- Fine-tuning or feature extraction for related computer vision tasks.

### Limitations & Bias
- **Image Resolution:** The backbone expects standard image sizes (typically 322x322  pixels). Performance may degrade on highly distorted or low-resolution images.
- **Domain Bias:** The model inherits the features learned from ImageNet. It may perform poorly on highly niche domains (e.g., specific medical imaging or satellite data) unless heavily fine-tuned on target data.

## How to Get Started with the Model

You can load and run inference with this model directly using standard PyTorch tools. 

First, ensure you have the `huggingface_hub`, `torch`, and `torchvision` libraries installed:
```bash
pip install torch torchvision huggingface_hub pillow
```

Use the snippet below to download the model weights and run a prediction:

```python
import torch
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image
from huggingface_hub import hf_hub_download

# 1. Recreate the model architecture
model = models.densenet121(weights=None)  # Initialize architecture

# Replace the final classifier head to match your custom number of classes
# Example: num_classes = 10 (Change this to your actual number of classes)
num_classes = 10 
model.classifier = torch.nn.Linear(model.classifier.in_features, num_classes)

# 2. Download and load the weights from Hugging Face Hub
# Replace 'your-username/your-model-name' with your repository ID
checkpoint_path = hf_hub_download(repo_id="adityachaubey/ChestNet", filename="model.pt")
model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cpu')))
model.eval()

# 3. Prepare the input image
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

image = Image.open("path_to_your_image.jpg").convert("RGB")
input_tensor = transform(image).unsqueeze(0)  # Add batch dimension

# 4. Run inference
with torch.no_grad():
    outputs = model(input_tensor)
    probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
    predicted_class = torch.argmax(probabilities).item()

print(f"Predicted Class Index: {predicted_class}")
```

## Training Details

### Training Data
The model was fine-tuned on the [NIH-Chest-X-ray-dataste] dataset, which contains [112,120] images across [14] classes.

### Data Preprocessing
Images were resized, cropped to 224x224, and normalized using standard ImageNet mean and standard deviation values:
- **Mean:** `[0.485, 0.456, 0.406]`
- **Std:** `[0.229, 0.224, 0.225]`

### Training Hyperparameters
- **Optimizer:** [Adam]
- **Learning Rate:** [1e-5]
- **Batch Size:** [e.g., 32]
- **Epochs:** [10]

## Evaluation Results

The model achieved the following performance metrics on the validation/test set:
- **Accuracy:** [83%]
- **Loss:** [Insert validation loss]