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ansilmbabl
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surface_grade-swin-tiny-patch4-window7-224-finetuned-v1

Image Classification
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  2. model.safetensors +1 -1
  3. runs/Feb16_18-00-39_e2e-83-148/events.out.tfevents.1708086668.e2e-83-148.1556269.0 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/swin-tiny-patch4-window7-224
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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: surface_grade-swin-tiny-patch4-window7-224-finetuned-v1
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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: test
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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: 0.4094082588335462
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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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+ # surface_grade-swin-tiny-patch4-window7-224-finetuned-v1
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+
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+ This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.4768
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+ - Accuracy: 0.4094
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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: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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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 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 1.6612 | 1.0 | 734 | 1.6258 | 0.2908 |
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+ | 1.6119 | 2.0 | 1468 | 1.5665 | 0.3149 |
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+ | 1.5692 | 3.0 | 2202 | 1.5426 | 0.3301 |
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+ | 1.5691 | 4.0 | 2936 | 1.4929 | 0.3599 |
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+ | 1.5401 | 5.0 | 3670 | 1.4630 | 0.3702 |
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+ | 1.5245 | 6.0 | 4404 | 1.4586 | 0.3728 |
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+ | 1.5342 | 7.0 | 5138 | 1.4018 | 0.4008 |
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+ | 1.5268 | 8.0 | 5872 | 1.3966 | 0.4040 |
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+ | 1.4918 | 9.0 | 6606 | 1.4097 | 0.3960 |
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+ | 1.4447 | 10.0 | 7340 | 1.3942 | 0.3997 |
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+ | 1.468 | 11.0 | 8074 | 1.3802 | 0.4164 |
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+ | 1.4379 | 12.0 | 8808 | 1.3927 | 0.4091 |
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+ | 1.4152 | 13.0 | 9542 | 1.3916 | 0.4091 |
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+ | 1.3845 | 14.0 | 10276 | 1.3901 | 0.4063 |
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+ | 1.3659 | 15.0 | 11010 | 1.3846 | 0.4121 |
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+ | 1.3429 | 16.0 | 11744 | 1.4010 | 0.4099 |
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+ | 1.3534 | 17.0 | 12478 | 1.3968 | 0.4115 |
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+ | 1.3026 | 18.0 | 13212 | 1.4060 | 0.4106 |
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+ | 1.2955 | 19.0 | 13946 | 1.4469 | 0.4008 |
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+ | 1.2849 | 20.0 | 14680 | 1.4081 | 0.4136 |
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+ | 1.2331 | 21.0 | 15414 | 1.4188 | 0.4089 |
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+ | 1.2313 | 22.0 | 16148 | 1.4256 | 0.4101 |
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+ | 1.2 | 23.0 | 16882 | 1.4414 | 0.4100 |
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+ | 1.2271 | 24.0 | 17616 | 1.4540 | 0.4088 |
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+ | 1.2142 | 25.0 | 18350 | 1.4528 | 0.4064 |
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+ | 1.1986 | 26.0 | 19084 | 1.4566 | 0.4090 |
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+ | 1.134 | 27.0 | 19818 | 1.4648 | 0.4104 |
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+ | 1.1756 | 28.0 | 20552 | 1.4700 | 0.4089 |
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+ | 1.1415 | 29.0 | 21286 | 1.4791 | 0.4082 |
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+ | 1.1411 | 30.0 | 22020 | 1.4768 | 0.4094 |
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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 2.0.1+cu117
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+ - Datasets 2.17.0
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+ - Tokenizers 0.15.2
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