Hand Gesture Yolo26n

Classes

0: like      1: palm      2: fist       3: peace
4: ok        5: one       6: three      7: four
8: call      9: point    10: grip      11: middle_finger

Usage

from ultralytics import YOLO

model = YOLO("yolo26n_hand-gesture_12c_v1.pt")
results = model("hand.jpg")
results[0].show()

Training Parameters

• imgsz: 800
• batch: 48
• optimizer: auto (AdamW)
• device: CUDA (NVIDIA RTX 5070 Ti, 16 GB)
• cache: disk
• workers: 12
• patience: 0

Dataset

Trained on a subset of HaGRID (Hand Gesture Recognition Image Dataset), containing ~36,000 images across 12 gesture classes.

Performance Notes

The model converged around epoch 84 (mAP50-95=0.862) and remained stable through epoch 100. Suitable for real-time inference on consumer GPUs.

--- language: en license: apache-2.0 tags: - hand-gesture - yolo26n - ultralytics - object-detection datasets: - hagrid library_name: ultralytics --- # Hand Gesture Recognition — YOLO26n Fine-tuned YOLO26n for 12-class hand gesture recognition, trained on the HaGRID dataset. ## Model Details | Attribute | Value | |-----------|-------| | **Architecture** | YOLO26n (Ultralytics) | | **Classes** | 12 | | **Input Size** | 800×800 | | **Batch** | 48 | | **Epochs** | 100 | | **mAP50** | 0.994 | | **mAP50-95** | 0.862 | | **Weight Size** | 5.1 MB (FP32) | ## Classes ``` 0: like 1: palm 2: fist 3: peace 4: ok 5: one 6: three 7: four 8: call 9: point 10: grip 11: middle_finger ``` ## Usage ```python from ultralytics import YOLO model = YOLO("yolo26n_hand-gesture_12c_v1.pt") results = model("hand.jpg") results[0].show() ``` ## Training Parameters - `imgsz`: 800 - `batch`: 48 - `optimizer`: auto (AdamW) - `device`: CUDA (NVIDIA RTX 5070 Ti, 16 GB) - `cache`: disk - `workers`: 12 - `patience`: 0 ## Dataset Trained on a subset of [HaGRID](https://hagrid.tilda.ws/) (Hand Gesture Recognition Image Dataset), containing ~36,000 images across 12 gesture classes. ## Performance Notes The model converged around epoch 84 (mAP50-95=0.862) and remained stable through epoch 100. Suitable for real-time inference on consumer GPUs.