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Detr Pose Coco50

🤖 facebook/detr-resnet-50 (object detector) 🧱 Custom PyTorch keypoint head 📊 Trained on 500-person subset of COCO 2017 Keypoints

Sourcehuggingface.co/Koushim/detr-pose-coco50/tree/c03c19c7e2c99b58e99eee86b309638e00c114a4

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Kind: Other
File size: 307 KB
Version: vRevision c03c19c
Updated: Nov 1, 2026
Downloads: 17
Source: Hugging Face

Source description

📌 DETR + Keypoint Estimation (COCO Subset)

Author: @Koushik

🧠 Model Overview

This project combines:

🤖 facebook/detr-resnet-50 (object detector)
🧱 Custom PyTorch keypoint head
📊 Trained on 500-person subset of COCO 2017 Keypoints

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

🏗️ Architecture

[ Input Image ]
      │
      ▼
[ DETR (Person BBox) ]
      │
      ▼
[ Crop + Resize (256×256) ]
      │
      ▼
[ CNN Keypoint Head ]
      │
      ▼
[ 17 Heatmaps (Keypoints) ]

🚀 Quick Start

import torch
from model import KeypointHead

model = KeypointHead()
model.load_state_dict(torch.load('pytorch_model.bin'))
model.eval()

🧪 Inference Demo

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

Sources

1 source

Hugging FacePrimaryVersion c03c19c7e2c99b58e99eee86b309638e00c114a4

huggingface.co/Koushim/detr-pose-coco50/tree/c03c19c7e2c99b58e99eee86b309638e00c114a4

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pytorch_model.bin

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Source excerpts

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ReadmeAug 18huggingface.co/Koushim/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...

NoteNov 1huggingface.co/Koushim/detr-pose-coco50

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

NoteAug 18huggingface.co/Koushim/detr-pose-coco50

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