base_model: openai/clip-vit-base-patch32
tags:
- generated_from_trainer
model-index:
- name: clip-fine-tuned-satellite-20240821
results: []
license: mit
datasets:
- blanchon/UC_Merced
metrics:
- accuracy
library_name: transformers
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
To see how to use it, refer to the CLIP documentation or check the app using this model:
https://huggingface.co/spaces/NemesisAlm/clip-satellite-demo
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