Instructions to use AvaneeshKGarg/clip-food101-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AvaneeshKGarg/clip-food101-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="AvaneeshKGarg/clip-food101-ft") 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("AvaneeshKGarg/clip-food101-ft") model = AutoModelForZeroShotImageClassification.from_pretrained("AvaneeshKGarg/clip-food101-ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
clip-food101-ft
This model is a fine-tuned version of openai/clip-vit-base-patch32 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: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1
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Model tree for AvaneeshKGarg/clip-food101-ft
Base model
openai/clip-vit-base-patch32