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README.md

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----
-title: Chest
-emoji: 🐠
-colorFrom: indigo
-colorTo: gray
-sdk: gradio
-sdk_version: 5.49.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+license: apache-2.0
+base_model: google/vit-base-patch16-224-in21k
+tags:
+- medical
+- healthcare
+- image-classification
+- chest-x-ray-pneumonia-detection
+datasets:
+- medical-images
+language:
+- en
+library_name: transformers
+pipeline_tag: image-classification
+---
+
+# Chest X-ray Pneumonia Detection
+
+## Model Description
+
+
+    This model is a fine-tuned Vision Transformer (ViT) for detecting pneumonia in chest X-ray images.
+    It can classify chest X-rays as either NORMAL (healthy) or PNEUMONIA (showing signs of pneumonia infection).
+    
+
+## Intended Uses & Limitations
+
+⚠️ **Important**: This model is for research and educational purposes only. It should **NOT** be used for actual medical diagnosis without proper clinical validation and oversight by qualified medical professionals.
+
+### Intended Uses
+- Research and development in medical AI
+- Educational purposes for learning about medical image classification
+- Proof-of-concept applications with proper disclaimers
+- Academic studies and benchmarking
+
+### Limitations
+- Not clinically validated
+- Should not replace professional medical diagnosis
+- May have biases based on training data
+- Performance may vary on different populations or imaging conditions
+
+## Model Details
+
+- **Model Type**: Image Classification
+- **Architecture**: google/vit-base-patch16-224-in21k
+- **Classes**: 2
+- **Input**: RGB images (224x224 pixels)
+- **Accuracy**: 95.83%
+
+### Classes
+- NORMAL
+- PNEUMONIA
+
+## Usage
+
+```python
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+from PIL import Image
+import torch
+
+# Load model and processor
+model = AutoModelForImageClassification.from_pretrained("your-username/chest-x-ray-pneumonia-detection")
+processor = AutoImageProcessor.from_pretrained("your-username/chest-x-ray-pneumonia-detection")
+
+# Load and process image
+image = Image.open("path_to_image.jpg")
+inputs = processor(image, return_tensors="pt")
+
+# Make prediction
+with torch.no_grad():
+    outputs = model(**inputs)
+    predicted_class_id = outputs.logits.argmax().item()
+    predicted_class = model.config.id2label[predicted_class_id]
+
+print(f"Predicted class: {predicted_class}")
+```
+
+## Training Details
+
+This model was fine-tuned from pre-trained vision transformers on medical image datasets. For detailed training information, please refer to the original model documentation.
+
+## Evaluation
+
+The model has been tested on held-out validation sets with the reported accuracy metrics. However, clinical evaluation and validation are required before any medical application.
+
+## Ethical Considerations
+
+- Medical AI models can have significant impact on human health
+- Proper validation and regulatory approval required for clinical use  
+- Potential for bias in training data and model predictions
+- Should be used responsibly with appropriate medical oversight
+
+## Contact
+
+For questions about this model, please create an issue in the repository.
+
+## Citation
+
+If you use this model in your research, please cite appropriately and acknowledge that it's for research purposes only.

config.json

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+{
+  "_name_or_path": "google/vit-base-patch16-224-in21k",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "NORMAL",
+    "1": "PNEUMONIA"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "NORMAL": "0",
+    "PNEUMONIA": "1"
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.17.0"
+}

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "ViTFeatureExtractor",
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "size": 224
+}

pytorch_model.bin

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