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vit-base-lcdoctypev1_session3

Image Classification
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  2. model.safetensors +1 -1
  3. runs/Mar29_00-56-56_5b2ae90c8915/events.out.tfevents.1711673820.5b2ae90c8915.192.2 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: nsugianto/vit-base-lcdoctypev1_session2
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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: vit-base-lcdoctypev1_session3
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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: 0.9669421487603306
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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-lcdoctypev1_session3
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+
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+ This model is a fine-tuned version of [nsugianto/vit-base-lcdoctypev1_session2](https://huggingface.co/nsugianto/vit-base-lcdoctypev1_session2) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1338
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+ - Accuracy: 0.9669
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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: 0.0002
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+ - train_batch_size: 16
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+ - eval_batch_size: 8
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+ - seed: 42
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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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+ - num_epochs: 10
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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 | 0.08 | 5 | 0.3159 | 0.9091 |
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+ | 0.1798 | 0.17 | 10 | 0.2262 | 0.9339 |
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+ | 0.1798 | 0.25 | 15 | 0.9910 | 0.7769 |
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+ | 0.3815 | 0.33 | 20 | 0.3035 | 0.9008 |
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+ | 0.3815 | 0.42 | 25 | 0.2177 | 0.9339 |
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+ | 0.1429 | 0.5 | 30 | 0.4909 | 0.8843 |
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+ | 0.1429 | 0.58 | 35 | 0.3096 | 0.9256 |
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+ | 0.2424 | 0.67 | 40 | 0.3270 | 0.9174 |
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+ | 0.2424 | 0.75 | 45 | 0.2555 | 0.9174 |
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+ | 0.1172 | 0.83 | 50 | 0.2309 | 0.9174 |
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+ | 0.1172 | 0.92 | 55 | 0.2952 | 0.9174 |
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+ | 0.1185 | 1.0 | 60 | 0.2957 | 0.9174 |
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+ | 0.1185 | 1.08 | 65 | 0.3724 | 0.8926 |
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+ | 0.1594 | 1.17 | 70 | 0.4216 | 0.8843 |
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+ | 0.1594 | 1.25 | 75 | 0.3475 | 0.9174 |
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+ | 0.1231 | 1.33 | 80 | 0.3234 | 0.8926 |
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+ | 0.1231 | 1.42 | 85 | 0.4310 | 0.8843 |
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+ | 0.0875 | 1.5 | 90 | 0.3598 | 0.9256 |
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+ | 0.0875 | 1.58 | 95 | 0.3038 | 0.9256 |
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+ | 0.0897 | 1.67 | 100 | 0.2599 | 0.9339 |
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+ | 0.0897 | 1.75 | 105 | 0.1684 | 0.9587 |
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+ | 0.1797 | 1.83 | 110 | 0.1412 | 0.9504 |
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+ | 0.1797 | 1.92 | 115 | 0.1453 | 0.9587 |
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+ | 0.1178 | 2.0 | 120 | 0.3831 | 0.8926 |
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+ | 0.1178 | 2.08 | 125 | 0.3321 | 0.9091 |
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+ | 0.1969 | 2.17 | 130 | 0.2546 | 0.9091 |
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+ | 0.1969 | 2.25 | 135 | 0.1839 | 0.9504 |
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+ | 0.0362 | 2.33 | 140 | 0.2027 | 0.9587 |
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+ | 0.0362 | 2.42 | 145 | 0.2877 | 0.9091 |
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+ | 0.1047 | 2.5 | 150 | 0.4504 | 0.8926 |
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+ | 0.1047 | 2.58 | 155 | 0.1811 | 0.9504 |
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+ | 0.1232 | 2.67 | 160 | 0.2107 | 0.9421 |
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+ | 0.1232 | 2.75 | 165 | 0.2086 | 0.9504 |
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+ | 0.0611 | 2.83 | 170 | 0.2971 | 0.9339 |
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+ | 0.0611 | 2.92 | 175 | 0.2732 | 0.9339 |
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+ | 0.0815 | 3.0 | 180 | 0.1679 | 0.9587 |
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+ | 0.0815 | 3.08 | 185 | 0.2416 | 0.9339 |
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+ | 0.0469 | 3.17 | 190 | 0.2927 | 0.9256 |
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+ | 0.0469 | 3.25 | 195 | 0.2831 | 0.9339 |
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+ | 0.0443 | 3.33 | 200 | 0.2745 | 0.9421 |
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+ | 0.0443 | 3.42 | 205 | 0.4193 | 0.8926 |
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+ | 0.0823 | 3.5 | 210 | 0.3746 | 0.9174 |
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+ | 0.0823 | 3.58 | 215 | 0.3030 | 0.9421 |
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+ | 0.0101 | 3.67 | 220 | 0.2146 | 0.9504 |
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+ | 0.0101 | 3.75 | 225 | 0.2514 | 0.9421 |
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+ | 0.16 | 3.83 | 230 | 0.2552 | 0.9421 |
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+ | 0.16 | 3.92 | 235 | 0.2239 | 0.9421 |
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+ | 0.1687 | 4.0 | 240 | 0.2571 | 0.9256 |
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+ | 0.1687 | 4.08 | 245 | 0.1357 | 0.9752 |
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+ | 0.0758 | 4.17 | 250 | 0.1734 | 0.9504 |
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+ | 0.0758 | 4.25 | 255 | 0.1197 | 0.9752 |
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+ | 0.042 | 4.33 | 260 | 0.2339 | 0.9421 |
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+ | 0.042 | 4.42 | 265 | 0.2924 | 0.9174 |
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+ | 0.0114 | 4.5 | 270 | 0.2318 | 0.9504 |
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+ | 0.0114 | 4.58 | 275 | 0.1765 | 0.9587 |
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+ | 0.0197 | 4.67 | 280 | 0.1263 | 0.9669 |
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+ | 0.0197 | 4.75 | 285 | 0.1253 | 0.9669 |
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+ | 0.0283 | 4.83 | 290 | 0.1239 | 0.9669 |
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+ | 0.0283 | 4.92 | 295 | 0.1278 | 0.9669 |
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+ | 0.1115 | 5.0 | 300 | 0.2528 | 0.9339 |
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+ | 0.1115 | 5.08 | 305 | 0.3164 | 0.9339 |
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+ | 0.0404 | 5.17 | 310 | 0.2842 | 0.9339 |
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+ | 0.0404 | 5.25 | 315 | 0.1713 | 0.9504 |
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+ | 0.0719 | 5.33 | 320 | 0.1896 | 0.9339 |
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+ | 0.0719 | 5.42 | 325 | 0.1855 | 0.9256 |
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+ | 0.0435 | 5.5 | 330 | 0.1541 | 0.9669 |
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+ | 0.0435 | 5.58 | 335 | 0.1050 | 0.9669 |
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+ | 0.0129 | 5.67 | 340 | 0.1063 | 0.9587 |
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+ | 0.0129 | 5.75 | 345 | 0.1138 | 0.9587 |
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+ | 0.0222 | 5.83 | 350 | 0.1144 | 0.9587 |
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+ | 0.0222 | 5.92 | 355 | 0.1238 | 0.9669 |
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+ | 0.0431 | 6.0 | 360 | 0.1343 | 0.9752 |
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+ | 0.0431 | 6.08 | 365 | 0.1441 | 0.9669 |
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+ | 0.0064 | 6.17 | 370 | 0.1471 | 0.9669 |
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+ | 0.0064 | 6.25 | 375 | 0.1361 | 0.9752 |
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+ | 0.0576 | 6.33 | 380 | 0.1316 | 0.9752 |
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+ | 0.0576 | 6.42 | 385 | 0.1232 | 0.9669 |
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+ | 0.0298 | 6.5 | 390 | 0.1255 | 0.9669 |
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+ | 0.0298 | 6.58 | 395 | 0.1359 | 0.9669 |
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+ | 0.0097 | 6.67 | 400 | 0.1435 | 0.9669 |
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+ | 0.0097 | 6.75 | 405 | 0.1451 | 0.9669 |
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+ | 0.0153 | 6.83 | 410 | 0.1439 | 0.9669 |
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+ | 0.0153 | 6.92 | 415 | 0.1353 | 0.9752 |
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+ | 0.0406 | 7.0 | 420 | 0.1316 | 0.9752 |
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+ | 0.0406 | 7.08 | 425 | 0.1309 | 0.9752 |
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+ | 0.0154 | 7.17 | 430 | 0.1305 | 0.9752 |
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+ | 0.0154 | 7.25 | 435 | 0.1310 | 0.9752 |
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+ | 0.0209 | 7.33 | 440 | 0.1301 | 0.9752 |
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+ | 0.0209 | 7.42 | 445 | 0.1459 | 0.9587 |
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+ | 0.0298 | 7.5 | 450 | 0.1663 | 0.9587 |
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+ | 0.0298 | 7.58 | 455 | 0.1559 | 0.9587 |
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+ | 0.0052 | 7.67 | 460 | 0.1516 | 0.9587 |
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+ | 0.0052 | 7.75 | 465 | 0.1396 | 0.9587 |
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+ | 0.0172 | 7.83 | 470 | 0.1330 | 0.9587 |
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+ | 0.0172 | 7.92 | 475 | 0.1236 | 0.9752 |
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+ | 0.0348 | 8.0 | 480 | 0.1210 | 0.9752 |
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+ | 0.0348 | 8.08 | 485 | 0.1175 | 0.9752 |
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+ | 0.0068 | 8.17 | 490 | 0.1185 | 0.9752 |
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+ | 0.0068 | 8.25 | 495 | 0.1229 | 0.9752 |
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+ | 0.0305 | 8.33 | 500 | 0.1230 | 0.9752 |
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+ | 0.0305 | 8.42 | 505 | 0.1205 | 0.9752 |
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+ | 0.0154 | 8.5 | 510 | 0.1197 | 0.9752 |
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+ | 0.0154 | 8.58 | 515 | 0.1217 | 0.9752 |
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+ | 0.0177 | 8.67 | 520 | 0.1239 | 0.9752 |
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+ | 0.0177 | 8.75 | 525 | 0.1244 | 0.9752 |
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+ | 0.0123 | 8.83 | 530 | 0.1271 | 0.9669 |
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+ | 0.0123 | 8.92 | 535 | 0.1300 | 0.9669 |
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+ | 0.0154 | 9.0 | 540 | 0.1314 | 0.9669 |
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+ | 0.0154 | 9.08 | 545 | 0.1296 | 0.9669 |
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+ | 0.0331 | 9.17 | 550 | 0.1251 | 0.9752 |
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+ | 0.0331 | 9.25 | 555 | 0.1269 | 0.9752 |
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+ | 0.0196 | 9.33 | 560 | 0.1284 | 0.9752 |
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+ | 0.0196 | 9.42 | 565 | 0.1298 | 0.9669 |
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+ | 0.0058 | 9.5 | 570 | 0.1313 | 0.9669 |
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+ | 0.0058 | 9.58 | 575 | 0.1321 | 0.9669 |
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+ | 0.012 | 9.67 | 580 | 0.1327 | 0.9669 |
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+ | 0.012 | 9.75 | 585 | 0.1326 | 0.9669 |
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+ | 0.0081 | 9.83 | 590 | 0.1329 | 0.9669 |
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+ | 0.0081 | 9.92 | 595 | 0.1336 | 0.9669 |
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+ | 0.0083 | 10.0 | 600 | 0.1338 | 0.9669 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.38.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.18.0
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+ - Tokenizers 0.15.2
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