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RobertoSonic
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swinv2-tiny-patch4-window8-256-dmae-humeda-DAV47

Image Classification
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swinv2
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  1. README.md +104 -0
  2. model.safetensors +1 -1
  3. runs/Feb06_23-21-47_ca55b56726ae/events.out.tfevents.1738884122.ca55b56726ae.7717.0 +2 -2
README.md ADDED
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+ ---
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+ library_name: transformers
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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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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: swinv2-tiny-patch4-window8-256-dmae-humeda-DAV47
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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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+ # swinv2-tiny-patch4-window8-256-dmae-humeda-DAV47
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9284
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+ - Accuracy: 0.75
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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: 3e-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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine_with_restarts
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 40
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+ - mixed_precision_training: Native AMP
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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 | 0.9412 | 8 | 1.5602 | 0.3409 |
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+ | 1.6237 | 1.9412 | 16 | 1.3767 | 0.4432 |
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+ | 1.4913 | 2.9412 | 24 | 1.3316 | 0.6136 |
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+ | 1.4913 | 3.9412 | 32 | 1.0605 | 0.6591 |
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+ | 1.2218 | 4.9412 | 40 | 0.9235 | 0.6932 |
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+ | 0.9148 | 5.9412 | 48 | 0.8240 | 0.75 |
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+ | 0.9148 | 6.9412 | 56 | 0.7359 | 0.6932 |
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+ | 0.7686 | 7.9412 | 64 | 0.7190 | 0.6932 |
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+ | 0.6291 | 8.9412 | 72 | 0.6824 | 0.7273 |
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+ | 0.6291 | 9.9412 | 80 | 0.7034 | 0.7614 |
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+ | 0.5546 | 10.9412 | 88 | 0.6911 | 0.7727 |
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+ | 0.4494 | 11.9412 | 96 | 0.6893 | 0.75 |
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+ | 0.4494 | 12.9412 | 104 | 0.6927 | 0.7727 |
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+ | 0.3719 | 13.9412 | 112 | 0.7180 | 0.7955 |
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+ | 0.3478 | 14.9412 | 120 | 0.7574 | 0.7159 |
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+ | 0.3478 | 15.9412 | 128 | 0.7665 | 0.7159 |
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+ | 0.3212 | 16.9412 | 136 | 0.8369 | 0.7386 |
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+ | 0.3184 | 17.9412 | 144 | 0.7906 | 0.7159 |
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+ | 0.3184 | 18.9412 | 152 | 0.8438 | 0.7273 |
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+ | 0.2873 | 19.9412 | 160 | 0.8233 | 0.7273 |
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+ | 0.2553 | 20.9412 | 168 | 0.8062 | 0.7386 |
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+ | 0.2553 | 21.9412 | 176 | 0.8711 | 0.7159 |
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+ | 0.2373 | 22.9412 | 184 | 0.8673 | 0.7386 |
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+ | 0.2208 | 23.9412 | 192 | 0.8600 | 0.7273 |
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+ | 0.2208 | 24.9412 | 200 | 0.8984 | 0.7159 |
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+ | 0.2353 | 25.9412 | 208 | 0.8848 | 0.7273 |
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+ | 0.2187 | 26.9412 | 216 | 0.8569 | 0.75 |
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+ | 0.2187 | 27.9412 | 224 | 0.8817 | 0.7386 |
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+ | 0.1943 | 28.9412 | 232 | 0.8949 | 0.75 |
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+ | 0.1926 | 29.9412 | 240 | 0.9077 | 0.7159 |
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+ | 0.1926 | 30.9412 | 248 | 0.9200 | 0.7159 |
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+ | 0.1816 | 31.9412 | 256 | 0.9233 | 0.7386 |
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+ | 0.1744 | 32.9412 | 264 | 0.9231 | 0.7386 |
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+ | 0.1744 | 33.9412 | 272 | 0.9329 | 0.7273 |
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+ | 0.1718 | 34.9412 | 280 | 0.9277 | 0.7386 |
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+ | 0.1701 | 35.9412 | 288 | 0.9258 | 0.75 |
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+ | 0.1701 | 36.9412 | 296 | 0.9262 | 0.75 |
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+ | 0.1921 | 37.9412 | 304 | 0.9274 | 0.75 |
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+ | 0.161 | 38.9412 | 312 | 0.9282 | 0.75 |
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+ | 0.161 | 39.9412 | 320 | 0.9284 | 0.75 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.48.2
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
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