Chapter 50: Yolov8 for custom dataset ASL Sign Language WhatsApp [phone] for Research Project

This video will explain how to deploy Yolov8 algorithm for custom dataset ASL Sign Language (Home and Love).
WhatsApp [phone] for Research Project
YOLOv8 for Custom ASL Sign Language Dataset
Implementing YOLOv8 for recognizing American Sign Language (ASL) signs enables real-time, high-accuracy gesture detection and interpretation. This application leverages YOLOv8's advanced object detection capabilities to create a robust model tailored for ASL, facilitating communication and accessibility.
#### Key Features:
- **High Precision**: Achieves excellent accuracy in detecting and classifying ASL signs.
- **Real-Time Processing**: Enables instant recognition of gestures, making it suitable for live applications.
- **Custom Dataset**: Trained specifically on a diverse ASL dataset to ensure comprehensive coverage of signs.
- **Scalability**: Easily adaptable to include additional signs and gestures.
#### Benefits:
- **Improved Accessibility**: Assists individuals with hearing impairments in communication.
- **Educational Tool**: Enhances learning experiences for ASL students and educators.
- **Integration Potential**: Can be incorporated into various platforms and devices, such as mobile apps and wearables.
#### Implementation Steps:
1. **Dataset Collection**: Gather and annotate a comprehensive dataset of ASL signs.
2. **Model Training**: Train the YOLOv8 model using the custom ASL dataset.
3. **Evaluation**: Test the model's performance and accuracy in recognizing ASL signs.
4. **Deployment**: Implement the model in real-time applications for end-users.
#### Hashtags:
#YOLOv8 #SignLanguage #ASL #ObjectDetection #MachineLearning #Accessibility #RealTimeRecognition #CustomDataset #AI #DeepLearning
With YOLOv8, bridging the communication gap through sign language recognition becomes more efficient and accessible than ever before.
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All Python codes have been successfully executed on Python 3.5 (32 bits) and Anaconda Navigator (anaconda3)
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[@TestKappa-vz4dz]

Can you share all the jupyter notebook codes at the end that show the detection?

[@awhbxrry6309]

Best Video!! Thanks a lot for making it.

[@codewithkristi]

Thanks dear

[@awhbxrry6309]

@@codewithkristi previously I ran this program the model ran fine. but now it shows error for the same code.
/content/sign-language--1
Ultralytics YOLOv8.0.230 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)
engine/trainer: task=detect, mode=train, model=/content/yolov8s.pt, data=/content/sign-language--1/data.yaml, epochs=25, time=None, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py", line 116, in __init__
self.data = check_det_dataset(self.args.data)
File "/usr/local/lib/python3.10/dist-packages/ultralytics/data/utils.py", line 312, in check_det_dataset
raise FileNotFoundError(m)
FileNotFoundError:
Dataset '/content/sign-language--1/data.yaml' images not found ⚠, missing path '/content/sign-language--1/sign-language--1/valid/images'
Note dataset download directory is '/content/datasets'. You can update this in '/root/.config/Ultralytics/settings.yaml'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/bin/yolo", line 8, in
sys.exit(entrypoint())
File "/usr/local/lib/python3.10/dist-packages/ultralytics/cfg/__init__.py", line 448, in entrypoint
getattr(model, mode)(**overrides) # default args from model
File "/usr/local/lib/python3.10/dist-packages/ultralytics/engine/model.py", line 351, in train
self.trainer = (trainer or self._smart_load('trainer'))(overrides=args, _callbacks=self.callbacks)
File "/usr/local/lib/python3.10/dist-packages/ultralytics/engine/trainer.py", line 120, in __init__
raise RuntimeError(emojis(f"Dataset '{clean_url(self.args.data)}' error ❌ {e}")) from e
RuntimeError: Dataset '/content/sign-language--1/data.yaml' error ❌
Dataset '/content/sign-language--1/data.yaml' images not found ⚠, missing path '/content/sign-language--1/sign-language--1/valid/images'
Note dataset download directory is '/content/datasets'. You can update this in '/root/.config/Ultralytics/settings.yaml'
this is the error i am getting. what to do?

[@kirthivershaM]

@@awhbxrry6309 yes I'm also getting this error how to rectify this?

[@NLP__]

@@kirthivershaM copy training and valid path and paste it correctly in data.yaml file try this

[@kirthivershaM]

in this video 28:59 you changed to another colab page that has the file run how to import that file and how you rectified that error before

[@codewithkristi]

20/573

[@codewithkristi]

50/574

[@codewithkristi]

36/573

[@sachinpriya88]

46/473

[@codewithkristi]

54/577

[@ayseyavas5060]

Can you share all the jupyter notebook codes at the end that show the detection?

[@codewithkristi]

sure will do