ModelListadosExterno

Efficientnet B7 Coco 256x192

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

Fontehuggingface.co/DeKUT-DSAIL/efficientnet_b7_coco_256x192/tree/c9891ed1ae242c406596ec6af0ea83b09c8239b2

TipoOtherVersãoModelBnota

Fonte do modelo

Tipo: Other
Tamanho do arquivo: 293 MB
Versão: vModel
Atualizado: 10 de out. de 2026
Fonte: Hugging Face

Descrição da fonte

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.

Property	Value
Backbone	EfficientNet-B7
Attention Neck	None
Parameters	~78 M
Input Size	192 × 256
Output	Heatmaps (17, 64, 48)

Evaluation Results

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

Metric	Score
COCO AP	0.726
COCO AR	0.763

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

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")

Input / Output Specification

Property	Value
Input size	`(1, 3, 256, 192)` — RGB, channel-first
Normalisation	Mean `[0.485, 0.456, 0.406]` / Std `[0.229, 0.224, 0.225]`
Output	Heatmaps `(N, 17, 64, 48)`
Keypoints	COCO 17-joint format

COCO 17 Keypoints

Index	Name	Index	Name
0	nose	9	left_wrist
1	left_eye	10	right_wrist
2	right_eye	11	left_hip
3	left_ear	12	right_hip
4	right_ear	13	left_knee
5	left_shoulder	14	right_knee
6	right_shoulder	15	left_ankle
7	left_elbow	16	right_ankle
8	right_elbow

Training Details

Trained using MMPose on the following datasets:

Dataset	Link
COCO 2017	cocodataset.org
MPII Human Pose	mpii.is.tue.mpg.de
CrowdPose	GitHub
OCHuman	GitHub

Parameter	Value
Optimizer	AdamW
Learning rate	1 × 10−3
LR schedule	Multi-step decay
Batch size	64
Epochs	210
Input size	192 × 256
Loss	MSE on heatmaps + knowledge distillation loss

Architecture

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

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

Fontes

1 fonte

Hugging FacePrimárioVersão c9891ed1ae242c406596ec6af0ea83b09c8239b2

huggingface.co/DeKUT-DSAIL/efficientnet_b7_coco_256x192/tree/c9891ed1ae242c406596ec6af0ea83b09c8239b2

Verificado 10 de out.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 293 MB · SHA-256 546d888f3739…f5fb · Hugging Face

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Trechos de fonte

2 trechos

Readme1 de ago.huggingface.co/DeKUT-DSAIL/efficientnet_b7_coco_256x192

--- license: apache-2.0 language: - en tags: - pose-estimation - human-pose-estimation - safetensors - keypoint-detection - computer-vision - pytorch - efficientnet - coco - mmpose datasets: - coco - mpii - crowdpose - ochuman metrics: - ap - ar library_name: pytorch pipeline_tag: keypoint-detection --- # EfficientNet-B7 Pose Estimation A 2D human pose estimation model trained at **DeKUT-DSAIL** using the [MMPose](https://github.com/open-mmlab/mmpose) framework. Predicts **17 COCO keypoints** from a single cropped person image. | Property | Value | |---|---| | Backbone | EfficientNet-B7 | | Attention Neck | None | | Parameters | ~78 M | | Input Size | 192 × 256 | | Output | Heatmaps (17, 64, 48) | --- ## Evaluation Results Evaluated on **COCO 2017 val** using OKS-based metrics (top-down, GT bounding boxes). | Metric | Score | |---|---| | **COCO AP** | **0.726** | | **COCO AR** | **0.763** | --- ## 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 ``` --- ## Quick Start ### Step 1 — Clone the repository ```bash git clone https://huggingface.co/DeKUT-DSAIL/efficientnet_b7_coco_256x192 cd efficientnet_b7_coco_256x192 ``` ### Step 2 — Create a virtual environment **Linux / macOS** ```bash python -m venv venv source venv/bin/activate ``` **Windows (Command Prompt)** ```cmd python -m venv venv venv\Scripts\activate.bat ``` **Windows (PowerShell)** ```powershell python -m venv venv venv\Scripts\Activate.ps1 ``` ### Step 3 — Install dependencies ```bash 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](https://pytorch.org/get-started/locally/). ### Step 4 — Run inference ```python 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)...

Note1 de ago.huggingface.co/DeKUT-DSAIL/efficientnet_b7_coco_256x192

Source context: 0 downloads · 1 likes · Pipeline keypoint-detection · Library pytorch · Repo DeKUT-DSAIL/efficientnet_b7_coco_256x192