--- license: other base_model: apple/mobilevit-xx-small tags: - generated_from_trainer metrics: - accuracy model-index: - name: mobilevit-xx-small-finetuned-eurosat results: [] --- # mobilevit-xx-small-finetuned-eurosat This model is a fine-tuned version of [apple/mobilevit-xx-small](https://huggingface.co/apple/mobilevit-xx-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.3961 - Accuracy: 0.09 ## 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: 0.003 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.2991 | 1.0 | 100 | 2.2896 | 0.16 | | 2.3041 | 2.0 | 200 | 2.4578 | 0.12 | | 2.2833 | 3.0 | 300 | 2.3022 | 0.12 | | 2.2755 | 4.0 | 400 | 2.4039 | 0.17 | | 2.3063 | 5.0 | 500 | 2.5689 | 0.1 | | 2.3247 | 6.0 | 600 | 2.5307 | 0.05 | | 2.2867 | 7.0 | 700 | 4.1296 | 0.08 | | 2.2696 | 8.0 | 800 | 3.0869 | 0.07 | | 2.2688 | 9.0 | 900 | 3.6086 | 0.08 | | 2.2616 | 10.0 | 1000 | 6.5422 | 0.13 | | 2.3896 | 11.0 | 1100 | 3.2715 | 0.11 | | 2.3264 | 12.0 | 1200 | 2.6975 | 0.08 | | 2.2603 | 13.0 | 1300 | 2.4012 | 0.17 | | 2.2845 | 14.0 | 1400 | 3.0856 | 0.19 | | 2.2813 | 15.0 | 1500 | 3.2556 | 0.17 | | 2.2232 | 16.0 | 1600 | 3.5357 | 0.18 | | 2.2332 | 17.0 | 1700 | 3.8758 | 0.11 | | 2.3568 | 18.0 | 1800 | 3.0675 | 0.13 | | 2.2627 | 19.0 | 1900 | 3.1308 | 0.16 | | 2.2528 | 20.0 | 2000 | 2.7741 | 0.1 | | 2.2039 | 21.0 | 2100 | 2.7257 | 0.14 | | 2.389 | 22.0 | 2200 | 2.6245 | 0.08 | | 2.31 | 23.0 | 2300 | 3.1870 | 0.1 | | 2.1471 | 24.0 | 2400 | 2.8313 | 0.02 | | 2.1658 | 25.0 | 2500 | 2.9323 | 0.11 | | 2.0946 | 26.0 | 2600 | 2.8372 | 0.14 | | 2.0924 | 27.0 | 2700 | 2.7403 | 0.16 | | 2.2634 | 28.0 | 2800 | 2.8991 | 0.14 | | 2.1897 | 29.0 | 2900 | 2.8778 | 0.13 | | 2.144 | 30.0 | 3000 | 2.6043 | 0.15 | | 2.108 | 31.0 | 3100 | 2.9231 | 0.1 | | 2.0792 | 32.0 | 3200 | 2.8421 | 0.12 | | 2.1552 | 33.0 | 3300 | 2.8106 | 0.12 | | 1.9701 | 34.0 | 3400 | 2.8279 | 0.11 | | 1.9291 | 35.0 | 3500 | 3.0954 | 0.2 | | 2.0341 | 36.0 | 3600 | 3.8294 | 0.14 | | 1.9165 | 37.0 | 3700 | 4.5289 | 0.11 | | 1.9736 | 38.0 | 3800 | 3.0090 | 0.14 | | 1.9811 | 39.0 | 3900 | 5.3900 | 0.14 | | 1.9522 | 40.0 | 4000 | 3.5710 | 0.08 | | 2.047 | 41.0 | 4100 | 3.4724 | 0.13 | | 1.9999 | 42.0 | 4200 | 7.2604 | 0.11 | | 1.9869 | 43.0 | 4300 | 7.9946 | 0.06 | | 1.9428 | 44.0 | 4400 | 6.1566 | 0.08 | | 1.7922 | 45.0 | 4500 | 4.9919 | 0.03 | | 1.9047 | 46.0 | 4600 | 7.1934 | 0.13 | | 1.9419 | 47.0 | 4700 | 4.3265 | 0.08 | | 1.7765 | 48.0 | 4800 | 4.6136 | 0.12 | | 1.7962 | 49.0 | 4900 | 13.4765 | 0.14 | | 2.0226 | 50.0 | 5000 | 8.1225 | 0.08 | | 2.1393 | 51.0 | 5100 | 7.7941 | 0.17 | | 1.8256 | 52.0 | 5200 | 5.4134 | 0.12 | | 1.9116 | 53.0 | 5300 | 6.1129 | 0.08 | | 2.1156 | 54.0 | 5400 | 4.1454 | 0.14 | | 1.7501 | 55.0 | 5500 | 6.2134 | 0.09 | | 1.8722 | 56.0 | 5600 | 6.4985 | 0.12 | | 1.9432 | 57.0 | 5700 | 5.2718 | 0.12 | | 1.7713 | 58.0 | 5800 | 12.3311 | 0.08 | | 1.6786 | 59.0 | 5900 | 7.1599 | 0.07 | | 1.5969 | 60.0 | 6000 | 6.0869 | 0.08 | | 1.8203 | 61.0 | 6100 | 8.8250 | 0.14 | | 1.7148 | 62.0 | 6200 | 19.0942 | 0.11 | | 1.6627 | 63.0 | 6300 | 12.4329 | 0.16 | | 1.7134 | 64.0 | 6400 | 5.5367 | 0.11 | | 1.8841 | 65.0 | 6500 | 9.1239 | 0.11 | | 1.6822 | 66.0 | 6600 | 9.4719 | 0.11 | | 1.8892 | 67.0 | 6700 | 5.6084 | 0.09 | | 1.72 | 68.0 | 6800 | 8.7854 | 0.12 | | 1.8751 | 69.0 | 6900 | 7.5571 | 0.11 | | 1.3783 | 70.0 | 7000 | 11.6321 | 0.12 | | 1.6403 | 71.0 | 7100 | 7.5354 | 0.15 | | 2.087 | 72.0 | 7200 | 13.7248 | 0.11 | | 1.6402 | 73.0 | 7300 | 5.4883 | 0.12 | | 1.8016 | 74.0 | 7400 | 7.8351 | 0.13 | | 1.4308 | 75.0 | 7500 | 4.6966 | 0.13 | | 1.6833 | 76.0 | 7600 | 5.9138 | 0.12 | | 1.5684 | 77.0 | 7700 | 11.9864 | 0.15 | | 1.6765 | 78.0 | 7800 | 12.2146 | 0.1 | | 1.7482 | 79.0 | 7900 | 4.6041 | 0.12 | | 1.7836 | 80.0 | 8000 | 9.7217 | 0.13 | | 1.5195 | 81.0 | 8100 | 7.5132 | 0.12 | | 1.4384 | 82.0 | 8200 | 6.6091 | 0.13 | | 1.5538 | 83.0 | 8300 | 7.0786 | 0.13 | | 1.5705 | 84.0 | 8400 | 12.5851 | 0.14 | | 1.7255 | 85.0 | 8500 | 9.9331 | 0.11 | | 1.6063 | 86.0 | 8600 | 11.3630 | 0.14 | | 1.5201 | 87.0 | 8700 | 20.8011 | 0.08 | | 1.3734 | 88.0 | 8800 | 5.2354 | 0.09 | | 1.5931 | 89.0 | 8900 | 6.5090 | 0.1 | | 1.5562 | 90.0 | 9000 | 11.8341 | 0.1 | | 1.576 | 91.0 | 9100 | 6.9521 | 0.11 | | 1.542 | 92.0 | 9200 | 5.4470 | 0.11 | | 1.4968 | 93.0 | 9300 | 11.3896 | 0.08 | | 1.5031 | 94.0 | 9400 | 11.9717 | 0.09 | | 1.797 | 95.0 | 9500 | 5.6596 | 0.15 | | 1.5389 | 96.0 | 9600 | 5.3947 | 0.15 | | 1.6494 | 97.0 | 9700 | 12.2707 | 0.09 | | 1.73 | 98.0 | 9800 | 7.7482 | 0.09 | | 1.6781 | 99.0 | 9900 | 8.2178 | 0.09 | | 1.6353 | 100.0 | 10000 | 7.3961 | 0.09 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.4.0+cu121 - Datasets 2.21.0 - Tokenizers 0.19.1