Detr Pose Coco50

--- license: apache-2.0 tags: - pytorch - keypoint-detection - human-pose-estimation - heatmap-regression - computer-vision - detr - coco model-index: - name: detr-pose-coco50 results: - task: type: pose-estimation name: Human Pose Estimation dataset: type: COCO name: COCO 2017 (50-person subset) metrics: - type: MSELoss value: ~0.02 name: Heatmap MSE --- # 📌 **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 heatmap...

Source context: 17 downloads · 0 likes · Pipeline keypoint-detection · Repo Koushim/detr-pose-coco50

Source context: 3 downloads · 0 likes · Pipeline keypoint-detection · Repo Koushim/detr-pose-coco50