efficientnet_b7_coco_256x192

EfficientNet-B7 Pose Estimation

A 2D human pose estimation model trained at DeKUT-DSAIL using the MMPose framework. Predicts 17 COCO keypoints from a single cropped person image.

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Evaluation Results

Evaluated on COCO 2017 val using OKS-based metrics (top-down, GT bounding boxes).

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Repository Files

model.safetensors      # Model weights (safetensors format)
model.py               # Self-contained PoseEstimator inference helper
requirements.txt       # Python dependencies
pose.jpg               # Example test image
README.md              # This model card

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Quick Start

Step 1 — Clone the repository

git clone https://huggingface.co/DeKUT-DSAIL/efficientnet_b7_coco_256x192
cd efficientnet_b7_coco_256x192

Step 2 — Create a virtual environment

Linux / macOS

python -m venv venv
source venv/bin/activate

Windows (Command Prompt)

python -m venv venv
venv\Scripts\activate.bat

Windows (PowerShell)

python -m venv venv
venv\Scripts\Activate.ps1

Step 3 — Install dependencies

pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt

GPU users: Replace the PyTorch URL with your CUDA version. See pytorch.org/get-started.

Step 4 — Run inference

import cv2
from model import PoseEstimator

estimator = PoseEstimator("DeKUT-DSAIL/efficientnet_b7_coco_256x192")

image = cv2.imread("pose.jpg")
keypoints, scores = estimator.predict(image)

print("Keypoints shape:", keypoints.shape)  # (N, 17, 2)
print("Scores shape:   ", scores.shape)     # (N, 17, 1)

annotated = estimator.visualize(image, keypoints, scores, score_threshold=0.3)
cv2.imwrite("output.jpg", annotated)
print("Saved output.jpg")

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Input / Output Specification

COCO 17 Keypoints

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Training Details

Trained using MMPose on the following datasets:

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Architecture

Input Image (3, 256, 192)
        │
        ▼
  EfficientNet-B7 Backbone
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        │
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  HeatmapHead (3× deconv + 1×1 conv)
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  Output Heatmaps (17, 64, 48)

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Developed by

DeKUT-DSAIL — Dedan Kimathi University of Technology

• Framework: PyTorch / MMPose
• Model type: 2D Human Pose Estimation
• Task: Keypoint Detection
• License: Apache 2.0