Instructions to use mrisdi/asl_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrisdi/asl_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mrisdi/asl_classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("mrisdi/asl_classification") model = AutoModelForImageClassification.from_pretrained("mrisdi/asl_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from mrisdi/asl_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.43 kB
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https://huggingface.co/mrisdi/asl_classification/resolve/main/README.md
- Command line
-
hf download hf://mrisdi/asl_classification/README.md
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curl -L -o README.md https://huggingface.co/mrisdi/asl_classification/resolve/main/README.md
1.43 kB
metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
model-index:
- name: asl_classification
results: []
asl_classification
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 3.0043
- eval_accuracy: 0.2019
- eval_runtime: 1.4504
- eval_samples_per_second: 71.703
- eval_steps_per_second: 2.758
- epoch: 21.5385
- step: 35
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1