Update README.md

README.md

@@ -1,35 +1,24 @@
----
-license: apache-2.0
-metrics:
-- accuracy
-pipeline_tag: object-detection
-datasets:
-- Ultralytics/COCO8
-language:
-- en
-base_model:
-- PekingU/rtdetr_r101vd
----
-
-## Usage:
-```python
-from transformers import AutoModelForObjectDetection, AutoImageProcessor
-from PIL import Image
-import requests
-
-# Load model and processor
-model = AutoModelForObjectDetection.from_pretrained("your-username/rtdetr-coco8")
-processor = AutoImageProcessor.from_pretrained("your-username/rtdetr-coco8")
-
-# Load an image for object detection
-def fetch_image(url):
-    return Image.open(requests.get(url, stream=True).raw)
-
-image = fetch_image("https://ultralytics.com/images/zidane.jpg")
-
-# Perform inference
-inputs = processor(images=image, return_tensors="pt")
-outputs = model(**inputs)
-
-# Print detected objects
-print(outputs)
+# RTDETR Model on COCO8 Dataset
+
+This model is a **Vision Transformer** (ViT) based object detection and tracking model, trained on the **COCO8** dataset, which contains images of people wearing coats (`1`) and people without coats (`0`).
+
+## Model Details
+
+- **Model Type**: RTDETR (a Vision Transformer based object detection and tracking model)
+- **Trained On**: COCO8 dataset (people with and without coats)
+- **Training Epochs**: 100 epochs
+- **Input Size**: 640x640 pixels
+- **Output**: Classifies images into two categories:
+  - `1`: People wearing coats
+  - `0`: People not wearing coats
+
+## How to Use
+
+You can use this model directly from the Hugging Face Hub. Below is an example of how to use it for inference on your images.
+
+### Install Dependencies
+
+First, ensure you have the `transformers` and `torch` libraries installed:
+
+```bash
+pip install transformers torch