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jaffe_crop_200_iter

Image Classification
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  1. README.md +278 -0
  2. model.safetensors +1 -1
  3. runs/Mar13_17-40-51_f630d3cef1eb/events.out.tfevents.1741887654.f630d3cef1eb.31.0 +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: WinKawaks/vit-tiny-patch16-224
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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: jaffe_crop_200_iter
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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: validation
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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: 0.8333333333333334
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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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+ # jaffe_crop_200_iter
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+
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+ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6110
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+ - Accuracy: 0.8333
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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: Use 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: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 200
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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 | 1.0 | 1 | 2.7256 | 0.1667 |
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+ | No log | 2.0 | 2 | 2.5775 | 0.1667 |
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+ | No log | 3.0 | 3 | 2.5633 | 0.1333 |
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+ | No log | 4.0 | 4 | 2.3386 | 0.1333 |
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+ | No log | 5.0 | 5 | 2.3028 | 0.1 |
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+ | No log | 6.0 | 6 | 2.1672 | 0.2333 |
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+ | No log | 7.0 | 7 | 2.1696 | 0.0333 |
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+ | No log | 8.0 | 8 | 2.0133 | 0.0667 |
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+ | No log | 9.0 | 9 | 2.0010 | 0.1 |
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+ | 2.3074 | 10.0 | 10 | 1.9153 | 0.3333 |
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+ | 2.3074 | 11.0 | 11 | 2.0177 | 0.3 |
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+ | 2.3074 | 12.0 | 12 | 1.8241 | 0.3333 |
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+ | 2.3074 | 13.0 | 13 | 1.7967 | 0.3 |
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+ | 2.3074 | 14.0 | 14 | 1.7210 | 0.3333 |
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+ | 2.3074 | 15.0 | 15 | 1.6551 | 0.3333 |
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+ | 2.3074 | 16.0 | 16 | 1.5307 | 0.5333 |
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+ | 2.3074 | 17.0 | 17 | 1.4263 | 0.4333 |
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+ | 2.3074 | 18.0 | 18 | 1.4219 | 0.4333 |
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+ | 2.3074 | 19.0 | 19 | 1.4402 | 0.4333 |
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+ | 1.5864 | 20.0 | 20 | 1.3050 | 0.5 |
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+ | 1.5864 | 21.0 | 21 | 1.4508 | 0.4 |
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+ | 1.5864 | 22.0 | 22 | 1.3384 | 0.4667 |
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+ | 1.5864 | 23.0 | 23 | 1.1632 | 0.6 |
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+ | 1.5864 | 24.0 | 24 | 1.0711 | 0.6333 |
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+ | 1.5864 | 25.0 | 25 | 1.1361 | 0.4667 |
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+ | 1.5864 | 26.0 | 26 | 1.2230 | 0.5667 |
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+ | 1.5864 | 27.0 | 27 | 1.0167 | 0.7 |
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+ | 1.5864 | 28.0 | 28 | 0.8815 | 0.6667 |
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+ | 1.5864 | 29.0 | 29 | 1.0191 | 0.6667 |
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+ | 0.7909 | 30.0 | 30 | 0.9474 | 0.6667 |
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+ | 0.7909 | 31.0 | 31 | 0.9328 | 0.5333 |
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+ | 0.7909 | 32.0 | 32 | 0.9476 | 0.6 |
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+ | 0.7909 | 33.0 | 33 | 0.7911 | 0.7 |
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+ | 0.7909 | 34.0 | 34 | 0.7609 | 0.7333 |
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+ | 0.7909 | 35.0 | 35 | 0.9489 | 0.6333 |
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+ | 0.7909 | 36.0 | 36 | 0.7199 | 0.6667 |
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+ | 0.7909 | 37.0 | 37 | 0.6195 | 0.7667 |
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+ | 0.7909 | 38.0 | 38 | 0.7611 | 0.7333 |
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+ | 0.7909 | 39.0 | 39 | 0.8063 | 0.7333 |
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+ | 0.3677 | 40.0 | 40 | 0.8360 | 0.6667 |
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+ | 0.3677 | 41.0 | 41 | 0.7250 | 0.7333 |
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+ | 0.3677 | 42.0 | 42 | 0.6228 | 0.8333 |
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+ | 0.3677 | 43.0 | 43 | 0.7757 | 0.7667 |
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+ | 0.3677 | 44.0 | 44 | 0.6767 | 0.7667 |
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+ | 0.3677 | 45.0 | 45 | 0.8128 | 0.6667 |
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+ | 0.3677 | 46.0 | 46 | 0.8584 | 0.7333 |
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+ | 0.3677 | 47.0 | 47 | 0.8866 | 0.6333 |
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+ | 0.3677 | 48.0 | 48 | 0.5143 | 0.8 |
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+ | 0.3677 | 49.0 | 49 | 0.7074 | 0.7 |
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+ | 0.192 | 50.0 | 50 | 0.7536 | 0.7333 |
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+ | 0.192 | 51.0 | 51 | 0.7113 | 0.7667 |
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+ | 0.192 | 52.0 | 52 | 0.8321 | 0.7 |
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+ | 0.192 | 53.0 | 53 | 0.7229 | 0.7333 |
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+ | 0.192 | 54.0 | 54 | 0.8839 | 0.7 |
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+ | 0.192 | 55.0 | 55 | 0.5496 | 0.8 |
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+ | 0.192 | 56.0 | 56 | 0.6769 | 0.8 |
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+ | 0.192 | 57.0 | 57 | 0.5740 | 0.8333 |
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+ | 0.192 | 58.0 | 58 | 0.6321 | 0.7667 |
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+ | 0.192 | 59.0 | 59 | 0.7635 | 0.7667 |
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+ | 0.1225 | 60.0 | 60 | 0.6869 | 0.8 |
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+ | 0.1225 | 61.0 | 61 | 0.6595 | 0.8333 |
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+ | 0.1225 | 62.0 | 62 | 0.5097 | 0.8 |
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+ | 0.1225 | 63.0 | 63 | 0.5642 | 0.8333 |
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+ | 0.1225 | 64.0 | 64 | 0.6503 | 0.8 |
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+ | 0.088 | 70.0 | 70 | 0.6571 | 0.8 |
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+ | 0.088 | 73.0 | 73 | 0.4259 | 0.9 |
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+ | 0.0359 | 92.0 | 92 | 0.3954 | 0.9 |
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+ | 0.023 | 110.0 | 110 | 0.5494 | 0.8 |
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+ | 0.0155 | 131.0 | 131 | 0.3391 | 0.9 |
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+ | 0.0048 | 179.0 | 179 | 0.4292 | 0.9 |
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+ | 0.003 | 185.0 | 185 | 0.7223 | 0.8 |
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+ | 0.003 | 187.0 | 187 | 0.5756 | 0.8333 |
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+ | 0.003 | 188.0 | 188 | 0.6505 | 0.8667 |
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+ | 0.003 | 189.0 | 189 | 0.5517 | 0.8667 |
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+ | 0.0025 | 190.0 | 190 | 0.5176 | 0.8667 |
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+ | 0.0025 | 191.0 | 191 | 0.4003 | 0.9 |
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+ | 0.0025 | 192.0 | 192 | 0.5594 | 0.8667 |
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+ | 0.0025 | 193.0 | 193 | 0.4229 | 0.9 |
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+ | 0.0025 | 194.0 | 194 | 0.5938 | 0.8667 |
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+ | 0.0025 | 195.0 | 195 | 0.6415 | 0.8667 |
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+ | 0.0025 | 196.0 | 196 | 0.5056 | 0.8667 |
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+ | 0.0025 | 197.0 | 197 | 0.5146 | 0.9 |
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+ | 0.0025 | 198.0 | 198 | 0.5822 | 0.8667 |
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+ | 0.0025 | 199.0 | 199 | 0.6066 | 0.8667 |
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+ | 0.0021 | 200.0 | 200 | 0.6110 | 0.8333 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.0
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+ - Pytorch 2.5.1+cu121
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+ - Datasets 3.3.1
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+ - Tokenizers 0.21.0
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