clip-fine-tuned-satellite

This model is a fine-tuned version of openai/clip-vit-base-patch32 on the blanchon/UC_Merced dataset.
It achieves the following results on the test set:
-Accuracy: 96.9%
The original CLIP model achieves 58.8% of accuracy.

Model description

The model is a fine-tuned version of CLIP.
30% of the parameters were retrained to achieve a significant increase in accuracy after only 2 epochs.

Intended uses & limitations

The model is to be used to classify satellite images.
It was trained on the UC_Merced dataset that comprises 21 classes: agricultural, airplane, baseballdiamond, beach, buildings, chaparral, denseresidential, forest, freeway, golfcourse, harbor, intersection, mediumresidential, mobilehomepark, overpass, parkinglot, river, runway, sparseresidential, storagetanks, tenniscourt

Training and evaluation data

30% of the parameters trained.
Evaluated against a test set of 420 images.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
1.4974 1.0 20 3.0190
1.3733 2.0 40 2.9588

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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