Object Detection
ultralytics
ONNX
PyTorch
TensorRT
English
yolo26
yolo11
int8
fp16
construction-safety
hazard-detection
Instructions to use yihong1120/Construction-Hazard-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use yihong1120/Construction-Hazard-Detection with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("yihong1120/Construction-Hazard-Detection") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - TensorRT
How to use yihong1120/Construction-Hazard-Detection with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| variant,precision_mode,backend,description,parameters,model_size_mb,model_sha256,images,imgsz,batch,device,metric_precision,recall,map50,map50_95,preprocess_ms,inference_ms,postprocess_ms,evaluated_at_utc | |
| l,f16,PyTorch,PyTorch FP16 inference.,26193306,50.52,398c424c1edbc0d288f88ecb9b38ac30d941707142f74db8b622b1b82f8d10d3,6664,640,16,0,0.930062,0.798927,0.86675,0.647971,0.2265,1.1701,0.0387,2026-08-01T18:40:53.456887+00:00 | |
| l,f32,PyTorch,PyTorch FP32 inference.,26193306,50.52,398c424c1edbc0d288f88ecb9b38ac30d941707142f74db8b622b1b82f8d10d3,6664,640,16,0,0.929777,0.799209,0.866875,0.648242,0.2215,2.1692,0.0383,2026-08-01T18:40:39.083112+00:00 | |
| l,int8,TensorRT,TensorRT INT8 engine with the full graph quantized.,26193306,29.35,c8a4d33b261a01ed5bc683de83fdd2b4515b9f5bfa08e15d93b699fcad4990f1,6664,640,16,0,0.971049,0.687998,0.835762,0.601201,0.2598,0.4257,0.0473,2026-08-01T18:41:13.548934+00:00 | |
| l,mixed,TensorRT,TensorRT INT8 engine with the YOLO Detect head retained at FP16.,26193306,31.55,88f205a388274ccf02f08cb0ef26b7ec94a5551369636239414c9ad1b00055ec,6664,640,16,0,0.925349,0.791439,0.860163,0.636147,0.2598,0.4608,0.0465,2026-08-01T18:41:03.704285+00:00 | |
| m,f16,PyTorch,PyTorch FP16 inference.,21789850,41.98,87e801a92275b30fe9527b8fd66a49d74324ee4f7850e74ffbdb5ed922f921d5,6664,640,16,0,0.903707,0.752999,0.828072,0.588866,0.2335,2.5976,0.0383,2026-08-01T18:39:59.549344+00:00 | |
| m,f32,PyTorch,PyTorch FP32 inference.,21789850,41.98,87e801a92275b30fe9527b8fd66a49d74324ee4f7850e74ffbdb5ed922f921d5,6664,640,16,0,0.904254,0.75303,0.828134,0.589149,0.2155,1.7714,0.0381,2026-08-01T18:39:35.297332+00:00 | |
| m,int8,TensorRT,TensorRT INT8 engine with the full graph quantized.,21789850,23.39,654e8deabf5068a75dcbc3c71899edd26194feda599f94b98eebe01ec8c830e7,6664,640,16,0,0.971324,0.584819,0.787064,0.497938,0.2521,0.3211,0.0487,2026-08-01T18:40:18.064000+00:00 | |
| m,mixed,TensorRT,TensorRT INT8 engine with the YOLO Detect head retained at FP16.,21789850,25.68,44d866eb006c754ed255826c71d0c25ae8dae74d103f23610e2d571e754c4a07,6664,640,16,0,0.904236,0.750863,0.827783,0.58822,0.2678,0.3603,0.0456,2026-08-01T18:40:08.918461+00:00 | |
| n,f16,PyTorch,PyTorch FP16 inference.,2508090,5.12,80c463daf7cf151f20e92231276162607bc4611c013cfb6e052b89442b764b1d,6664,640,16,0,0.850856,0.645846,0.723156,0.476369,0.2493,5.0454,0.0401,2026-08-01T18:37:22.927615+00:00 | |
| n,f32,PyTorch,PyTorch FP32 inference.,2508090,5.12,80c463daf7cf151f20e92231276162607bc4611c013cfb6e052b89442b764b1d,6664,640,16,0,0.851368,0.645664,0.723274,0.476493,0.2637,0.4431,0.04,2026-08-01T18:36:42.104549+00:00 | |
| n,int8,TensorRT,TensorRT INT8 engine with the full graph quantized.,2508090,6.37,a744c767c70a7d40b1664a0a132c9bc334f9fea2637c95216743acbb4cf0e65c,6664,640,16,0,0.845011,0.613659,0.694177,0.436827,0.2724,0.1478,0.05,2026-08-01T18:37:39.626028+00:00 | |
| n,mixed,TensorRT,TensorRT INT8 engine with the YOLO Detect head retained at FP16.,2508090,7.07,3a35a19c2e69f4d2b06d9de96cb77ba49ec6c4813dd678dd92123abc615811c3,6664,640,16,0,0.847046,0.638825,0.71671,0.468853,0.2599,0.152,0.0472,2026-08-01T18:37:31.302237+00:00 | |
| s,f16,PyTorch,PyTorch FP16 inference.,9956378,19.36,96296d1cb455e44e806e3fb0d7b7778389deb79fbe2958f0c07fc7dc22c6b7c9,6664,640,16,0,0.910438,0.753681,0.826335,0.599376,0.2411,5.1964,0.0399,2026-08-01T18:38:59.974164+00:00 | |
| s,f32,PyTorch,PyTorch FP32 inference.,9956378,19.36,96296d1cb455e44e806e3fb0d7b7778389deb79fbe2958f0c07fc7dc22c6b7c9,6664,640,16,0,0.912761,0.752537,0.82618,0.599616,0.2358,0.8075,0.0374,2026-08-01T18:38:17.669750+00:00 | |
| s,int8,TensorRT,TensorRT INT8 engine with the full graph quantized.,9956378,12.74,07b3c43932f8119b446eca5de8a457b0f233547fe3fab5d525ef04e72a604541,6664,640,16,0,0.930892,0.713524,0.807916,0.574217,0.2719,0.1906,0.0482,2026-08-01T18:39:17.060334+00:00 | |
| s,mixed,TensorRT,TensorRT INT8 engine with the YOLO Detect head retained at FP16.,9956378,14.18,fae06b126e894a3f4ac3b69546834c5e1729edf49939b3b0cae450637d82af0b,6664,640,16,0,0.90998,0.750698,0.823458,0.595634,0.2694,0.2073,0.0479,2026-08-01T18:39:08.571675+00:00 | |
| x,f16,PyTorch,PyTorch FP16 inference.,58833978,112.84,e3d8ee5ca7ff00bbb888ee28f997952fa371a9f2904702c678e0a809fb43ce2c,6664,640,16,0,0.931826,0.814494,0.878787,0.667003,0.2205,6.058,0.0383,2026-08-01T18:42:36.012057+00:00 | |
| x,f32,PyTorch,PyTorch FP32 inference.,58833978,112.84,e3d8ee5ca7ff00bbb888ee28f997952fa371a9f2904702c678e0a809fb43ce2c,6664,640,16,0,0.931725,0.814436,0.878796,0.667321,0.2136,4.1727,0.0388,2026-08-01T18:41:48.173372+00:00 | |
| x,int8,TensorRT,TensorRT INT8 engine with the full graph quantized.,58833978,59.17,9bd8c2bde11a33ae3e908cbe789393ed2d22b92de113d1bc6f8a96e8dfe3fbde,6664,640,16,0,0.96064,0.698458,0.798278,0.555985,0.2487,0.6643,0.0426,2026-08-01T18:44:24.793878+00:00 | |
| x,mixed,TensorRT,TensorRT INT8 engine with the YOLO Detect head retained at FP16.,58833978,63.39,48f98c4326480de2f73e58066ecc37adce29ccc361d9979df575b22be0f6a3cb,6664,640,16,0,0.93026,0.813044,0.877572,0.664249,0.2464,0.7598,0.0405,2026-08-01T18:44:02.153593+00:00 | |