luisarizmendi/hardhat-or-hat
By luisarizmendi · ↓ 0 · ♥ 0 · Updated 2025-04-24
What's in this repo
This page lists the files in luisarizmendi/hardhat-or-hat, including model.onnx and more.
- 2 ONNX models (.onnx files)
- 86 files, including 40 .jpg, 28 .png, 2 .ipynb, 2 .py, 2 .onnx, 2 .pt
- Last updated 2025-04-24
ONNX files
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
Model for detecting Hardhats and Hats
Model binary
You can download:
Base Model
Ultralytics/YOLO11m
Huggingface page
https://huggingface.co/luisarizmendi/hardhat-or-hat
Model Dataset
https://universe.roboflow.com/luisarizmendi/hardhat-or-hat
Labels
- hat
- helmet
- no_helmet
Model metrics
Model training
You can review the Jupyter notebook here
Hyperparameters
base model: yolov11x.pt
epochs: 150
batch: 16
imgsz: 640
patience: 15
optimizer: 'SGD'
lr0: 0.001
lrf: 0.01
momentum: 0.9
weight_decay: 0.0005
warmup_epochs: 3
warmup_bias_lr: 0.01
warmup_momentum: 0.8
Model Usage
Usage with Huggingface spaces
If you don't want to run it locally, you can use this huggingface space that I've created with this code but be aware that this will be slow since I'm using a free instance, so it's better to run it locally with the python script below.
Remember to check that the Model URL is pointing to the model that you want to test.
Usage with Python script
Install the following PIP requirements
gradio
ultralytics
Pillow
opencv-python
torch
Then run the python code below and open http://localhost:8800 in a browser to upload and scan the images.
import gradio as gr
from ultralytics import YOLO
from PIL import Image
import os
import cv2
import torch
DEFAULT_MODEL_URL = "https://huggingface.co/luisarizmendi/hardhat-or-hat/tree/main/v2/model/pytorch/best.pt"
def detect_objects_in_files(model_input, files):
"""
Processes uploaded images for object detection.
"""
if not files:
return "No files uploaded.", []
model = YOLO(str(model_input))
if torch.cuda.is_available():
model.to('cuda')
print("Using GPU for inference")
else:
print("Using CPU for inference")
results_images = []
for file in files:
try:
image = Image.open(file).convert("RGB")
results = model(image)
result_img_bgr = results[0].plot()
result_img_rgb = cv2.cvtColor(result_img_bgr, cv2.COLOR_BGR2RGB)
results_images.append(result_img_rgb)
# If you want that images appear one by one (slower)
#yield "Processing image...", results_images
except Exception as e:
return f"Error processing file: {file}. Exception: {str(e)}", []
del model
torch.cuda.empty_cache()
return "Processing completed.", results_images
interface = gr.Interface(
fn=detect_objects_in_files,
inputs=[
gr.Textbox(value=DEFAULT_MODEL_URL, label="Model URL", placeholder="Enter the model URL"),
gr.Files(file_types=["image"], label="Select Images"),
],
outputs=[
gr.Textbox(label="Status"),
gr.Gallery(label="Results")
],
title="Object Detection on Images",
description="Upload images to perform object detection. The model will process each image and display the results."
)
if __name__ == "__main__":
interface.launch()
Model info
tensorboard
onnx
yolo
yolo11
hardhat
hat
object-detection
base_model:Ultralytics/YOLO11
base_model:quantized:Ultralytics/YOLO11
model-index
region:us
↓ 0
♥ 0
Created: 2024-11-28
Updated: 2025-04-24
View on Hugging FaceFiles (86)
.gitattributes
.ipynb_checkpoints/train-checkpoint.ipynb
README.md
dev/object-detection-model-file/pytorch/Containerfile
dev/object-detection-model-file/pytorch/object-detection-pytorch.py
dev/object-detection-model-file/pytorch/requirements.txt
dev/prototyping.ipynb
images/example.png
images/spaces-example.png
tools/pytorch-to-onnx.py
v1/model/onnx/1/model.onnx
ONNX
v1/model/pytorch/best.pt
v1/test/F1_curve.png
v1/test/PR_curve.png
v1/test/P_curve.png
v1/test/R_curve.png
v1/test/confusion_matrix.png
v1/test/confusion_matrix_normalized.png
v1/test/val_batch0_labels.jpg
v1/test/val_batch0_pred.jpg
v1/test/val_batch1_labels.jpg
v1/test/val_batch1_pred.jpg
v1/test/val_batch2_labels.jpg
v1/test/val_batch2_pred.jpg
v1/train-val/F1_curve.png
v1/train-val/PR_curve.png
v1/train-val/P_curve.png
v1/train-val/R_curve.png
v1/train-val/args.yaml
v1/train-val/confusion_matrix.png
v1/train-val/confusion_matrix_normalized.png
v1/train-val/events.out.tfevents.1738747289.yolo-training-pipeline-7vd94-system-container-impl-3115668440.58.0
v1/train-val/labels.jpg
v1/train-val/labels_correlogram.jpg
v1/train-val/results.csv
v1/train-val/results.png
v1/train-val/train_batch0.jpg
v1/train-val/train_batch1.jpg
v1/train-val/train_batch2.jpg
v1/train-val/train_batch27555.jpg
v1/train-val/train_batch27556.jpg
v1/train-val/train_batch27557.jpg
v1/train-val/val_batch0_labels.jpg
v1/train-val/val_batch0_pred.jpg
v1/train-val/val_batch1_labels.jpg
v1/train-val/val_batch1_pred.jpg
v1/train-val/val_batch2_labels.jpg
v1/train-val/val_batch2_pred.jpg
v2/model/onnx/1/model.onnx
ONNX
v2/model/pytorch/best.pt
v2/test/F1_curve.png
v2/test/PR_curve.png
v2/test/P_curve.png
v2/test/R_curve.png
v2/test/confusion_matrix.png
v2/test/confusion_matrix_normalized.png
v2/test/val_batch0_labels.jpg
v2/test/val_batch0_pred.jpg
v2/test/val_batch1_labels.jpg
v2/test/val_batch1_pred.jpg
v2/test/val_batch2_labels.jpg
v2/test/val_batch2_pred.jpg
v2/train-val/F1_curve.png
v2/train-val/PR_curve.png
v2/train-val/P_curve.png
v2/train-val/R_curve.png
v2/train-val/args.yaml
v2/train-val/confusion_matrix.png
v2/train-val/confusion_matrix_normalized.png
v2/train-val/events.out.tfevents.1738747287.yolo-training-pipeline-b82kg-system-container-impl-2306871192.63.0
v2/train-val/labels.jpg
v2/train-val/labels_correlogram.jpg
v2/train-val/results.csv
v2/train-val/results.png
v2/train-val/train_batch0.jpg
v2/train-val/train_batch1.jpg
v2/train-val/train_batch2.jpg
v2/train-val/train_batch27555.jpg
v2/train-val/train_batch27556.jpg
v2/train-val/train_batch27557.jpg
v2/train-val/val_batch0_labels.jpg
v2/train-val/val_batch0_pred.jpg
v2/train-val/val_batch1_labels.jpg
v2/train-val/val_batch1_pred.jpg
v2/train-val/val_batch2_labels.jpg
v2/train-val/val_batch2_pred.jpg
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