Instructions to use hyeongjin99/CLIP_ViT_L_14_PCSP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hyeongjin99/CLIP_ViT_L_14_PCSP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hyeongjin99/CLIP_ViT_L_14_PCSP") 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("hyeongjin99/CLIP_ViT_L_14_PCSP") model = AutoModelForZeroShotImageClassification.from_pretrained("hyeongjin99/CLIP_ViT_L_14_PCSP") - Notebooks
- Google Colab
- Kaggle
CLIP_ViT_L_14_PCSP
This model is a fine-tuned version of openai/clip-vit-large-patch14 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0224
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: 16
- seed: 42
- optimizer: Use OptimizerNames.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: 100.0
Training results
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.21.0
- Downloads last month
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Model tree for hyeongjin99/CLIP_ViT_L_14_PCSP
Base model
openai/clip-vit-large-patch14