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vit-estadosfenologicos

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  1. README.md +110 -0
  2. best_model/config.json +36 -0
  3. best_model/model.safetensors +3 -0
  4. best_model/preprocessor_config.json +26 -0
  5. best_model/training_args.bin +3 -0
  6. model.safetensors +1 -1
  7. runs/Mar11_17-18-00_RAZGRIZ/events.out.tfevents.1741709881.RAZGRIZ.3772.0 +2 -2
  8. runs/Mar11_17-18-00_RAZGRIZ/events.out.tfevents.1741716851.RAZGRIZ.3772.1 +3 -0
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: google/vit-base-patch32-384
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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: vit-estadosfenologicos
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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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+ # vit-estadosfenologicos
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+
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+ This model is a fine-tuned version of [google/vit-base-patch32-384](https://huggingface.co/google/vit-base-patch32-384) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1298
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+ - Accuracy: 0.9581
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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: 0.0005
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+ - train_batch_size: 512
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+ - eval_batch_size: 8
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+ - seed: 42
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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: linear
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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 | 1.0 | 12 | 0.7243 | 0.8397 |
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+ | No log | 2.0 | 24 | 0.3981 | 0.8720 |
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+ | No log | 3.0 | 36 | 0.2737 | 0.9135 |
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+ | No log | 4.0 | 48 | 0.2173 | 0.9250 |
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+ | No log | 5.0 | 60 | 0.1910 | 0.9319 |
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+ | No log | 6.0 | 72 | 0.1757 | 0.9354 |
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+ | No log | 7.0 | 84 | 0.1638 | 0.9412 |
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+ | No log | 8.0 | 96 | 0.1572 | 0.9377 |
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+ | 0.4042 | 9.0 | 108 | 0.1504 | 0.9458 |
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+ | 0.4042 | 10.0 | 120 | 0.1456 | 0.9412 |
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+ | 0.4042 | 11.0 | 132 | 0.1415 | 0.9458 |
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+ | 0.4042 | 12.0 | 144 | 0.1375 | 0.9458 |
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+ | 0.4042 | 13.0 | 156 | 0.1351 | 0.9458 |
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+ | 0.4042 | 14.0 | 168 | 0.1327 | 0.9469 |
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+ | 0.4042 | 15.0 | 180 | 0.1294 | 0.9504 |
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+ | 0.4042 | 16.0 | 192 | 0.1270 | 0.9469 |
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+ | 0.1655 | 17.0 | 204 | 0.1258 | 0.9504 |
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+ | 0.1655 | 18.0 | 216 | 0.1247 | 0.9516 |
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+ | 0.1655 | 19.0 | 228 | 0.1221 | 0.9539 |
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+ | 0.1655 | 20.0 | 240 | 0.1210 | 0.9527 |
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+ | 0.1655 | 21.0 | 252 | 0.1208 | 0.9504 |
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+ | 0.1655 | 22.0 | 264 | 0.1185 | 0.9550 |
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+ | 0.1655 | 23.0 | 276 | 0.1176 | 0.9539 |
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+ | 0.1655 | 24.0 | 288 | 0.1158 | 0.9539 |
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+ | 0.1371 | 25.0 | 300 | 0.1162 | 0.9539 |
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+ | 0.1371 | 26.0 | 312 | 0.1142 | 0.9550 |
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+ | 0.1371 | 27.0 | 324 | 0.1148 | 0.9550 |
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+ | 0.1371 | 28.0 | 336 | 0.1131 | 0.9550 |
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+ | 0.1371 | 29.0 | 348 | 0.1122 | 0.9550 |
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+ | 0.1371 | 30.0 | 360 | 0.1118 | 0.9550 |
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+ | 0.1371 | 31.0 | 372 | 0.1116 | 0.9539 |
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+ | 0.1371 | 32.0 | 384 | 0.1103 | 0.9550 |
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+ | 0.1371 | 33.0 | 396 | 0.1102 | 0.9550 |
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+ | 0.124 | 34.0 | 408 | 0.1103 | 0.9550 |
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+ | 0.124 | 35.0 | 420 | 0.1089 | 0.9573 |
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+ | 0.124 | 36.0 | 432 | 0.1088 | 0.9562 |
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+ | 0.124 | 37.0 | 444 | 0.1092 | 0.9550 |
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+ | 0.124 | 38.0 | 456 | 0.1079 | 0.9562 |
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+ | 0.124 | 39.0 | 468 | 0.1082 | 0.9562 |
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+ | 0.124 | 40.0 | 480 | 0.1077 | 0.9562 |
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+ | 0.124 | 41.0 | 492 | 0.1071 | 0.9573 |
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+ | 0.1168 | 42.0 | 504 | 0.1068 | 0.9573 |
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+ | 0.1168 | 43.0 | 516 | 0.1073 | 0.9562 |
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+ | 0.1168 | 44.0 | 528 | 0.1067 | 0.9562 |
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+ | 0.1168 | 45.0 | 540 | 0.1066 | 0.9562 |
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+ | 0.1168 | 46.0 | 552 | 0.1062 | 0.9573 |
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+ | 0.1168 | 47.0 | 564 | 0.1063 | 0.9573 |
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+ | 0.1168 | 48.0 | 576 | 0.1063 | 0.9562 |
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+ | 0.1168 | 49.0 | 588 | 0.1063 | 0.9562 |
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+ | 0.1129 | 50.0 | 600 | 0.1063 | 0.9562 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.49.0
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+ - Pytorch 2.6.0+cu126
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+ - Datasets 3.3.2
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+ - Tokenizers 0.21.0
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ "num_attention_heads": 12,
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+ "num_channels": 3,
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+ "num_hidden_layers": 12,
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+ "patch_size": 32,
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+ "problem_type": "single_label_classification",
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+ "qkv_bias": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.49.0"
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+ }
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