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vit-base-patch16-384-finetuned-galaxy10-decals

Image Classification
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vit
vision
Generated from Trainer
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-384
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ - precision
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+ - recall
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+ - f1
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+ model-index:
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+ - name: vit-base-patch16-384-finetuned-galaxy10-decals
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # vit-base-patch16-384-finetuned-galaxy10-decals
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.5571
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+ - Accuracy: 0.8591
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+ - Precision: 0.8571
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+ - Recall: 0.8591
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+ - F1: 0.8564
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 512
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 30
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 1.5894 | 0.99 | 31 | 1.2732 | 0.5744 | 0.5409 | 0.5744 | 0.5481 |
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+ | 0.8001 | 1.98 | 62 | 0.6184 | 0.7976 | 0.7934 | 0.7976 | 0.7880 |
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+ | 0.6895 | 2.98 | 93 | 0.5823 | 0.8067 | 0.7991 | 0.8067 | 0.7955 |
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+ | 0.6259 | 4.0 | 125 | 0.4910 | 0.8433 | 0.8427 | 0.8433 | 0.8368 |
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+ | 0.556 | 4.99 | 156 | 0.4874 | 0.8467 | 0.8465 | 0.8467 | 0.8465 |
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+ | 0.5116 | 5.98 | 187 | 0.4734 | 0.8546 | 0.8569 | 0.8546 | 0.8518 |
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+ | 0.4877 | 6.98 | 218 | 0.4539 | 0.8461 | 0.8429 | 0.8461 | 0.8428 |
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+ | 0.4383 | 8.0 | 250 | 0.4716 | 0.8377 | 0.8399 | 0.8377 | 0.8345 |
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+ | 0.4267 | 8.99 | 281 | 0.4355 | 0.8602 | 0.8576 | 0.8602 | 0.8559 |
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+ | 0.4022 | 9.98 | 312 | 0.4758 | 0.8377 | 0.8377 | 0.8377 | 0.8356 |
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+ | 0.3811 | 10.98 | 343 | 0.4538 | 0.8495 | 0.8471 | 0.8495 | 0.8474 |
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+ | 0.3612 | 12.0 | 375 | 0.4808 | 0.8439 | 0.8412 | 0.8439 | 0.8399 |
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+ | 0.363 | 12.99 | 406 | 0.4751 | 0.8467 | 0.8502 | 0.8467 | 0.8458 |
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+ | 0.3198 | 13.98 | 437 | 0.4800 | 0.8489 | 0.8497 | 0.8489 | 0.8450 |
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+ | 0.3192 | 14.98 | 468 | 0.4834 | 0.8574 | 0.8580 | 0.8574 | 0.8570 |
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+ | 0.3041 | 16.0 | 500 | 0.4879 | 0.8495 | 0.8500 | 0.8495 | 0.8443 |
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+ | 0.2607 | 16.99 | 531 | 0.4958 | 0.8540 | 0.8529 | 0.8540 | 0.8523 |
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+ | 0.2649 | 17.98 | 562 | 0.4927 | 0.8579 | 0.8570 | 0.8579 | 0.8562 |
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+ | 0.2553 | 18.98 | 593 | 0.5095 | 0.8495 | 0.8473 | 0.8495 | 0.8474 |
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+ | 0.2453 | 20.0 | 625 | 0.5162 | 0.8495 | 0.8467 | 0.8495 | 0.8467 |
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+ | 0.2417 | 20.99 | 656 | 0.5375 | 0.8579 | 0.8573 | 0.8579 | 0.8543 |
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+ | 0.241 | 21.98 | 687 | 0.5129 | 0.8568 | 0.8546 | 0.8568 | 0.8547 |
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+ | 0.2257 | 22.98 | 718 | 0.5316 | 0.8596 | 0.8584 | 0.8596 | 0.8571 |
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+ | 0.2087 | 24.0 | 750 | 0.5530 | 0.8512 | 0.8497 | 0.8512 | 0.8489 |
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+ | 0.2196 | 24.99 | 781 | 0.5422 | 0.8613 | 0.8600 | 0.8613 | 0.8596 |
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+ | 0.1975 | 25.98 | 812 | 0.5672 | 0.8529 | 0.8534 | 0.8529 | 0.8508 |
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+ | 0.2135 | 26.98 | 843 | 0.5697 | 0.8523 | 0.8513 | 0.8523 | 0.8509 |
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+ | 0.1946 | 28.0 | 875 | 0.5598 | 0.8557 | 0.8542 | 0.8557 | 0.8536 |
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+ | 0.2006 | 28.99 | 906 | 0.5582 | 0.8591 | 0.8566 | 0.8591 | 0.8560 |
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+ | 0.1968 | 29.76 | 930 | 0.5571 | 0.8591 | 0.8571 | 0.8591 | 0.8564 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.3.0
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+ - Datasets 2.19.1
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+ - Tokenizers 0.15.1
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