Instructions to use AdityasArsenal/finetuned-for-YogaPoses-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdityasArsenal/finetuned-for-YogaPoses-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="AdityasArsenal/finetuned-for-YogaPoses-v2") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("AdityasArsenal/finetuned-for-YogaPoses-v2") model = AutoModelForImageClassification.from_pretrained("AdityasArsenal/finetuned-for-YogaPoses-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
finetuned-for-YogaPoses-v2
This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for AdityasArsenal/finetuned-for-YogaPoses-v2
Base model
google/mobilenet_v2_1.0_224