Create README.md

README.md

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+---
+base_model: microsoft/resnet-18
+license: mit
+tags:
+- image-classification
+- pytorch
+- computer-vision
+- fall-detection
+---
+# Fall Detection Model (ResNet-18 Fine-tuned)
+
+This model is a fine-tuned ResNet-18 for image classification, specifically trained to detect falls in images.
+
+## Model Details
+- **Base Model:** `microsoft/resnet-18`
+- **Dataset:** `hiennguyen9874/fall-detection-dataset`
+- **Task:** Binary image classification (fall/no_fall)
+- **Classes:**
+  - `0`: `no_fall`
+  - `1`: `fall`
+
+## How to Use
+
+### 1. Load the Model and Image Processor
+
+```python
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+from PIL import Image
+import torch
+
+# Assuming 'device' is already defined (e.g., torch.device("cuda" if torch.cuda.is_available() else "cpu"))
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+repo_id = "popkek00/fall_detection_model" # Your model's repository ID
+
+model = AutoModelForImageClassification.from_pretrained(repo_id).to(device)
+image_processor = AutoImageProcessor.from_pretrained(repo_id)
+
+model.eval() # Set model to evaluation mode
+```
+
+### 2. Prepare an Image for Inference
+
+```python
+# Example: Load an image (replace with your image path or PIL Image object)
+# You can load an image from a URL, local file, or a BytesIO object
+# For demonstration, let's assume you have a PIL Image object called `example_image`
+
+# Create a dummy image for demonstration
+example_image = Image.new('RGB', (224, 224), color = 'red')
+
+# Process the image
+inputs = image_processor(images=example_image, return_tensors="pt")
+pixel_values = inputs["pixel_values"].to(device)
+```
+
+### 3. Get Predictions
+
+```python
+with torch.no_grad():
+    outputs = model(pixel_values)
+
+logits = outputs.logits
+probabilities = torch.softmax(logits, dim=1)
+predicted_class_id = probabilities.argmax().item()
+
+# Get the human-readable label from the model's config
+predicted_label = model.config.id2label[predicted_class_id]
+confidence = probabilities[0, predicted_class_id].item() * 100
+
+print(f"Predicted label: {predicted_label} (Confidence: {confidence:.2f}%)")
+```
+
+---