README.md

metadata

license: apache-2.0
base_model: facebook/dinov2-small-imagenet1k-1-layer
tags:
  - generated_from_trainer
datasets:
  - food101
metrics:
  - accuracy
model-index:
  - name: my_awesome_food_model
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: food101
          type: food101
          config: default
          split: train[:5000]
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.933

my_awesome_food_model

This model is a fine-tuned version of facebook/dinov2-small-imagenet1k-1-layer on the food101 dataset. It achieves the following results on the evaluation set:

• Loss: 0.2296
• Accuracy: 0.933

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: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4769	0.99	62	0.5877	0.805
0.3517	2.0	125	0.3854	0.872
0.2431	2.98	186	0.2296	0.933

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0