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swinv2-tiny-patch4-window8-256-finetuned-PE

Image Classification
Transformers
PyTorch
swinv2
Generated from Trainer
Eval Results
Inference Endpoints
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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: microsoft/swinv2-tiny-patch4-window8-256
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: swinv2-tiny-patch4-window8-256-finetuned-PE
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.8720186154741129
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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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+ # swinv2-tiny-patch4-window8-256-finetuned-PE
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+
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+ This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.3083
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+ - Accuracy: 0.8720
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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: 0.00025
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+ - train_batch_size: 256
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+ - eval_batch_size: 256
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 1024
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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: 50
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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 |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.92 | 9 | 0.6391 | 0.6690 |
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+ | 0.6873 | 1.95 | 19 | 0.5293 | 0.7376 |
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+ | 0.6233 | 2.97 | 29 | 0.6385 | 0.6853 |
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+ | 0.5976 | 4.0 | 39 | 0.4447 | 0.7970 |
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+ | 0.5552 | 4.92 | 48 | 0.4029 | 0.8266 |
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+ | 0.552 | 5.95 | 58 | 0.3675 | 0.8429 |
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+ | 0.5055 | 6.97 | 68 | 0.3409 | 0.8581 |
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+ | 0.4816 | 8.0 | 78 | 0.3322 | 0.8615 |
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+ | 0.455 | 8.92 | 87 | 0.3166 | 0.8639 |
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+ | 0.4428 | 9.95 | 97 | 0.3100 | 0.8662 |
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+ | 0.4398 | 10.97 | 107 | 0.3713 | 0.8365 |
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+ | 0.4318 | 12.0 | 117 | 0.4019 | 0.8284 |
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+ | 0.4431 | 12.92 | 126 | 0.3074 | 0.8714 |
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+ | 0.4437 | 13.95 | 136 | 0.3156 | 0.8656 |
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+ | 0.4482 | 14.97 | 146 | 0.3516 | 0.8476 |
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+ | 0.4353 | 16.0 | 156 | 0.3162 | 0.8598 |
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+ | 0.4218 | 16.92 | 165 | 0.3018 | 0.8685 |
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+ | 0.4111 | 17.95 | 175 | 0.3143 | 0.8650 |
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+ | 0.4224 | 18.97 | 185 | 0.3146 | 0.8592 |
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+ | 0.4114 | 20.0 | 195 | 0.3097 | 0.8691 |
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+ | 0.4103 | 20.92 | 204 | 0.3038 | 0.8703 |
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+ | 0.3989 | 21.95 | 214 | 0.2893 | 0.8796 |
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+ | 0.3908 | 22.97 | 224 | 0.2956 | 0.8755 |
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+ | 0.3923 | 24.0 | 234 | 0.3041 | 0.8685 |
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+ | 0.3842 | 24.92 | 243 | 0.2876 | 0.8749 |
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+ | 0.3808 | 25.95 | 253 | 0.2907 | 0.8767 |
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+ | 0.382 | 26.97 | 263 | 0.3018 | 0.8738 |
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+ | 0.3816 | 28.0 | 273 | 0.2812 | 0.8825 |
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+ | 0.379 | 28.92 | 282 | 0.2960 | 0.8633 |
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+ | 0.3858 | 29.95 | 292 | 0.2960 | 0.8743 |
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+ | 0.3546 | 30.97 | 302 | 0.2850 | 0.8807 |
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+ | 0.3656 | 32.0 | 312 | 0.2905 | 0.8784 |
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+ | 0.3707 | 32.92 | 321 | 0.2926 | 0.8743 |
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+ | 0.3651 | 33.95 | 331 | 0.2941 | 0.8796 |
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+ | 0.3584 | 34.97 | 341 | 0.3133 | 0.8615 |
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+ | 0.36 | 36.0 | 351 | 0.3181 | 0.8679 |
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+ | 0.3496 | 36.92 | 360 | 0.3036 | 0.8685 |
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+ | 0.3458 | 37.95 | 370 | 0.2939 | 0.8732 |
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+ | 0.3431 | 38.97 | 380 | 0.3062 | 0.8703 |
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+ | 0.3512 | 40.0 | 390 | 0.2914 | 0.8755 |
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+ | 0.3512 | 40.92 | 399 | 0.3164 | 0.8674 |
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+ | 0.3403 | 41.95 | 409 | 0.3063 | 0.8679 |
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+ | 0.3423 | 42.97 | 419 | 0.3018 | 0.8720 |
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+ | 0.3312 | 44.0 | 429 | 0.3094 | 0.8697 |
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+ | 0.3365 | 44.92 | 438 | 0.3062 | 0.8755 |
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+ | 0.3319 | 45.95 | 448 | 0.3081 | 0.8720 |
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+ | 0.3409 | 46.15 | 450 | 0.3083 | 0.8720 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.3
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.14.5
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+ - Tokenizers 0.13.3
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