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README.md

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+# 📌 **DETR + Keypoint Estimation (COCO Subset)**
+Author: [@Koushik](https://huggingface.co/Koushim)
+
+---
+
+### 🧠 Model Overview
+
+This project combines:
+
+* 🤖 [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) (object detector)
+* 🧱 Custom PyTorch keypoint head
+* 📊 Trained on 500-person subset of [COCO 2017 Keypoints](https://cocodataset.org/#keypoints-2020)
+
+The system detects people using DETR, then predicts 17 COCO-style keypoints (top-down) using heatmap regression.
+
+---
+
+### 📂 Files Included
+
+| File                            | Description                                |
+| ------------------------------- | ------------------------------------------ |
+| `pytorch_model.bin`             | Trained PyTorch model weights              |
+| `05_detr_pose_coco_colab.ipynb` | Full Colab notebook (training + inference) |
+| `config.json`                   | Basic model metadata                       |
+| `README.md`                     | Project description                        |
+
+---
+
+### 📚 Dataset
+
+* **Subset**: 500 images from COCO val2017 with visible persons
+* **Annotations**: 17 keypoints per person
+* **Source**: [COCO Keypoints](https://cocodataset.org/#keypoints-2020)
+
+---
+
+### 🏗️ Architecture
+
+```text
+[ Input Image ]
+      │
+      ▼
+[ DETR (Person BBox) ]
+      │
+      ▼
+[ Crop + Resize (256×256) ]
+      │
+      ▼
+[ CNN Keypoint Head ]
+      │
+      ▼
+[ 17 Heatmaps (Keypoints) ]
+```
+
+---
+
+### 🚀 Quick Start
+
+```python
+import torch
+from model import KeypointHead
+
+model = KeypointHead()
+model.load_state_dict(torch.load('pytorch_model.bin'))
+model.eval()
+```
+
+---
+
+### 🧪 Inference Demo
+
+```python
+from PIL import Image
+import cv2, numpy as np
+from transformers import DetrImageProcessor, DetrForObjectDetection
+
+img = Image.open('sample.jpg')
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
+detector = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+inputs = processor(images=img, return_tensors="pt")
+outputs = detector(**inputs)
+results = processor.post_process_object_detection(outputs, target_sizes=[img.size[::-1]], threshold=0.8)[0]
+
+# Use results['boxes'][0] to crop person
+# Feed crop into model(img) to get 17 heatmaps
+```
+
+---
+
+### 🧠 Training (optional)
+
+To fine-tune on your own dataset:
+
+* Convert your data to COCO format
+* Use the notebook provided (`05_detr_pose_coco_colab.ipynb`)
+* Change paths and re-train
+
+---
+
+### ✨ Credit
+
+* [Hugging Face Transformers](https://github.com/huggingface/transformers)
+* [COCO Dataset](https://cocodataset.org/)
+* [facebook/detr](https://huggingface.co/facebook/detr-resnet-50)
+