Instructions to use vinluvie/clip-vit-large-patch14-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vinluvie/clip-vit-large-patch14-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="vinluvie/clip-vit-large-patch14-finetuned") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("vinluvie/clip-vit-large-patch14-finetuned") model = AutoModelForZeroShotImageClassification.from_pretrained("vinluvie/clip-vit-large-patch14-finetuned") - Notebooks
- Google Colab
- Kaggle
clip-vit-large-patch14-finetuned
This model is a fine-tuned version of openai/clip-vit-large-patch14 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.7755
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
Training results
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
- Downloads last month
- 12
Model tree for vinluvie/clip-vit-large-patch14-finetuned
Base model
openai/clip-vit-large-patch14