dinov2-imagenet-class-weighting-on-tanzania

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

• Loss: 0.5166
• Accuracy: 0.8773

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: 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
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7421	1.0	15	0.5973	0.8773
0.8054	2.0	30	0.7593	0.1227
0.6982	3.0	45	0.5166	0.8773

Framework versions

• Transformers 4.53.1
• Pytorch 2.7.1+cu126
• Datasets 3.6.0
• Tokenizers 0.21.2