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

hoanbklucky/dinov2-imagenet-class-weighting-on-tanzania

dinov2-imagenet-class-weighting-on-tanzania

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hoanbklucky/
dinov2-imagenet-class-weighting-on-tanzania