NemesisAlm
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clip-fine-tuned-satellite

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 ---
+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
 ---
+# clip-fine-tuned-satellite
+This model is a fine-tuned version of [openai/clip-vit-base-patch32](https://huggingface.co/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