ZaneHorrible
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Model save
Browse files- README.md +161 -0
- config.json +76 -0
- model.safetensors +3 -0
- preprocessor_config.json +36 -0
- runs/May30_02-46-36_26276f0c1205/events.out.tfevents.1717037227.26276f0c1205.34.0 +3 -0
- training_args.bin +3 -0
README.md
ADDED
@@ -0,0 +1,161 @@
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---
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license: apache-2.0
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base_model: google/vit-large-patch32-224-in21k
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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: ViTL-32-224-1e4-batch_16_epoch_4_classes_24
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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: train
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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: 0.9410919540229885
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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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# ViTL-32-224-1e4-batch_16_epoch_4_classes_24
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This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co/google/vit-large-patch32-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3192
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- Accuracy: 0.9411
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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: 8
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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: 3
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.3387 | 0.03 | 100 | 1.3149 | 0.7328 |
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| 0.7705 | 0.07 | 200 | 0.7867 | 0.8003 |
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| 0.5818 | 0.1 | 300 | 0.6799 | 0.8204 |
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| 0.537 | 0.14 | 400 | 0.4596 | 0.8836 |
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| 0.4053 | 0.17 | 500 | 0.5233 | 0.8592 |
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| 0.3401 | 0.21 | 600 | 0.6987 | 0.8032 |
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| 0.5161 | 0.24 | 700 | 0.5360 | 0.8405 |
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| 0.3592 | 0.28 | 800 | 0.4567 | 0.8664 |
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| 0.284 | 0.31 | 900 | 0.3531 | 0.8966 |
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| 0.2266 | 0.35 | 1000 | 0.4766 | 0.8678 |
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| 0.2876 | 0.38 | 1100 | 0.6849 | 0.8233 |
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| 0.3459 | 0.42 | 1200 | 0.4300 | 0.8851 |
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| 0.2598 | 0.45 | 1300 | 0.3651 | 0.9052 |
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| 0.5085 | 0.49 | 1400 | 0.4353 | 0.8736 |
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| 0.4432 | 0.52 | 1500 | 0.4327 | 0.8678 |
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| 0.2403 | 0.56 | 1600 | 0.4481 | 0.8736 |
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| 0.4616 | 0.59 | 1700 | 0.5625 | 0.8549 |
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| 0.244 | 0.63 | 1800 | 0.4537 | 0.8664 |
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| 0.4304 | 0.66 | 1900 | 0.4377 | 0.8879 |
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| 0.1581 | 0.7 | 2000 | 0.4487 | 0.8851 |
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| 0.1273 | 0.73 | 2100 | 0.5803 | 0.8649 |
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| 0.1073 | 0.77 | 2200 | 0.4146 | 0.8865 |
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| 0.2694 | 0.8 | 2300 | 0.3707 | 0.9080 |
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| 0.1699 | 0.84 | 2400 | 0.3477 | 0.9152 |
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| 0.2632 | 0.87 | 2500 | 0.4382 | 0.8951 |
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| 0.1191 | 0.91 | 2600 | 0.3614 | 0.9095 |
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| 0.1634 | 0.94 | 2700 | 0.3786 | 0.9167 |
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| 0.1704 | 0.98 | 2800 | 0.4049 | 0.8865 |
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| 0.0117 | 1.01 | 2900 | 0.3248 | 0.9080 |
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| 0.0522 | 1.04 | 3000 | 0.3518 | 0.9066 |
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| 0.179 | 1.08 | 3100 | 0.4117 | 0.9080 |
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| 0.0079 | 1.11 | 3200 | 0.4204 | 0.9023 |
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| 0.1191 | 1.15 | 3300 | 0.4253 | 0.9066 |
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| 0.0444 | 1.18 | 3400 | 0.4485 | 0.9080 |
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| 0.2814 | 1.22 | 3500 | 0.4029 | 0.9167 |
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| 0.1599 | 1.25 | 3600 | 0.4882 | 0.8937 |
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| 0.0156 | 1.29 | 3700 | 0.4070 | 0.9152 |
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| 0.2496 | 1.32 | 3800 | 0.3230 | 0.9282 |
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| 0.0407 | 1.36 | 3900 | 0.3894 | 0.9167 |
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| 0.1122 | 1.39 | 4000 | 0.4924 | 0.8980 |
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| 0.0803 | 1.43 | 4100 | 0.4620 | 0.8937 |
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| 0.1398 | 1.46 | 4200 | 0.3461 | 0.9109 |
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| 0.1072 | 1.5 | 4300 | 0.4346 | 0.9080 |
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| 0.0855 | 1.53 | 4400 | 0.3444 | 0.9267 |
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| 0.0065 | 1.57 | 4500 | 0.4178 | 0.9023 |
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| 0.0143 | 1.6 | 4600 | 0.3257 | 0.9224 |
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| 0.041 | 1.64 | 4700 | 0.3396 | 0.9195 |
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| 0.0042 | 1.67 | 4800 | 0.3481 | 0.9253 |
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| 0.0117 | 1.71 | 4900 | 0.4299 | 0.9037 |
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| 0.132 | 1.74 | 5000 | 0.3819 | 0.9195 |
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| 0.0223 | 1.78 | 5100 | 0.4280 | 0.9152 |
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| 0.0009 | 1.81 | 5200 | 0.4115 | 0.9239 |
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| 0.0578 | 1.85 | 5300 | 0.3844 | 0.9267 |
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| 0.0014 | 1.88 | 5400 | 0.4024 | 0.9296 |
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| 0.002 | 1.92 | 5500 | 0.4511 | 0.9095 |
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| 0.0186 | 1.95 | 5600 | 0.3562 | 0.9353 |
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| 0.1249 | 1.99 | 5700 | 0.3672 | 0.9253 |
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| 0.0615 | 2.02 | 5800 | 0.3567 | 0.9310 |
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| 0.0031 | 2.06 | 5900 | 0.3148 | 0.9325 |
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| 0.0212 | 2.09 | 6000 | 0.3752 | 0.9267 |
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| 0.0008 | 2.12 | 6100 | 0.3394 | 0.9339 |
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| 0.0007 | 2.16 | 6200 | 0.3566 | 0.9339 |
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| 0.0771 | 2.19 | 6300 | 0.3514 | 0.9310 |
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| 0.0007 | 2.23 | 6400 | 0.4172 | 0.9253 |
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| 0.0018 | 2.26 | 6500 | 0.4019 | 0.9267 |
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| 0.0058 | 2.3 | 6600 | 0.3383 | 0.9368 |
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| 0.0032 | 2.33 | 6700 | 0.3362 | 0.9339 |
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| 0.0006 | 2.37 | 6800 | 0.3186 | 0.9382 |
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| 0.0005 | 2.4 | 6900 | 0.3366 | 0.9382 |
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| 0.0006 | 2.44 | 7000 | 0.3802 | 0.9296 |
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| 0.0919 | 2.47 | 7100 | 0.4116 | 0.9296 |
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| 0.0005 | 2.51 | 7200 | 0.3063 | 0.9425 |
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| 0.0004 | 2.54 | 7300 | 0.3466 | 0.9339 |
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| 0.0005 | 2.58 | 7400 | 0.3435 | 0.9368 |
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| 0.0004 | 2.61 | 7500 | 0.3080 | 0.9411 |
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| 0.0016 | 2.65 | 7600 | 0.3310 | 0.9425 |
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| 0.0004 | 2.68 | 7700 | 0.3398 | 0.9368 |
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| 0.0004 | 2.72 | 7800 | 0.3446 | 0.9353 |
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| 0.0004 | 2.75 | 7900 | 0.3294 | 0.9382 |
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| 0.1075 | 2.79 | 8000 | 0.3090 | 0.9425 |
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| 0.0004 | 2.82 | 8100 | 0.3218 | 0.9382 |
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| 0.0004 | 2.86 | 8200 | 0.3160 | 0.9425 |
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| 0.0004 | 2.89 | 8300 | 0.3270 | 0.9397 |
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| 0.0004 | 2.93 | 8400 | 0.3273 | 0.9397 |
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| 0.0003 | 2.96 | 8500 | 0.3184 | 0.9440 |
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| 0.0004 | 3.0 | 8600 | 0.3192 | 0.9411 |
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### Framework versions
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- Transformers 4.39.3
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- Pytorch 2.1.2
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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config.json
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{
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"_name_or_path": "google/vit-large-patch32-224-in21k",
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"architectures": [
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"ViTForImageClassification"
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],
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"attention_probs_dropout_prob": 0.0,
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"encoder_stride": 16,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 1024,
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"id2label": {
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"0": "Bhapa Pitha(\u09ad\u09be\u09aa\u09be \u09aa\u09bf\u09a0\u09be)",
|
13 |
+
"1": "Biriyani(\u09ac\u09bf\u09b0\u09bf\u09df\u09be\u09a8\u09bf)",
|
14 |
+
"10": "Khichuri(\u0996\u09bf\u099a\u09c1\u09a1\u09bc\u09bf)",
|
15 |
+
"11": "Malpua Pitha(\u09ae\u09be\u09b2\u09aa\u09c1\u09df\u09be \u09aa\u09bf\u09a0\u09be)",
|
16 |
+
"12": "Mustard Hilsa(\u09b8\u09b0\u09b7\u09c7 \u0987\u09b2\u09bf\u09b6)",
|
17 |
+
"13": "Nakshi Pitha(\u09a8\u0995\u09b6\u09bf \u09aa\u09bf\u09a0\u09be)",
|
18 |
+
"14": "Panta Ilish(\u09aa\u09be\u09a8\u09cd\u09a4\u09be \u0987\u09b2\u09bf\u09b6)",
|
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+
"15": "Patishapta Pitha(\u09aa\u09be\u099f\u09bf\u09b8\u09be\u09aa\u099f\u09be)",
|
20 |
+
"16": "Prawn Malai Curry(\u099a\u09bf\u0982\u09dc\u09bf \u09ae\u09be\u09b2\u09be\u0987\u0995\u09be\u09b0\u09c0)",
|
21 |
+
"17": "Rasgulla(\u09b0\u09b8\u0997\u09cb\u09b2\u09cd\u09b2\u09be)",
|
22 |
+
"18": "Rose Cookies(\u09ab\u09c1\u09b2\u099d\u09c1\u09b0\u09bf \u09aa\u09bf\u09a0\u09be)",
|
23 |
+
"19": "Roshmalai(\u09b0\u09b8\u09ae\u09be\u09b2\u09be\u0987)",
|
24 |
+
"2": "Chicken Pulao(\u09ae\u09cb\u09b0\u0997 \u09aa\u09cb\u09b2\u09be\u0993)",
|
25 |
+
"20": "Shahi Tukra(\u09b6\u09be\u09b9\u09bf \u099f\u09c1\u0995\u09b0\u09be)",
|
26 |
+
"21": "Shingara(\u09b8\u09bf\u0999\u09cd\u0997\u09be\u09b0\u09be)",
|
27 |
+
"22": "Sweet Yogurt(\u09ae\u09bf\u09b7\u09cd\u099f\u09bf \u09a6\u0987)",
|
28 |
+
"23": "Tehari(\u09a4\u09c7\u09b9\u09be\u09b0\u09bf)",
|
29 |
+
"3": "Chickpease Bhuna(\u099b\u09cb\u09b2\u09be\u09ad\u09c1\u09a8\u09be)",
|
30 |
+
"4": "Egg Curry(\u09a1\u09bf\u09ae\u09ad\u09c1\u09a8\u09be)",
|
31 |
+
"5": "Falooda(\u09ab\u09be\u09b2\u09c1\u09a6\u09be)",
|
32 |
+
"6": "Fuchka(\u09ab\u09c1\u099a\u0995\u09be)",
|
33 |
+
"7": "Haleem(\u09b9\u09be\u09b2\u09bf\u09ae)",
|
34 |
+
"8": "Jalebi(\u099c\u09bf\u09b2\u09be\u09aa\u09c0)",
|
35 |
+
"9": "Kala Bhuna(\u0995\u09be\u09b2\u09be \u09ad\u09c1\u09a8\u09be)"
|
36 |
+
},
|
37 |
+
"image_size": 224,
|
38 |
+
"initializer_range": 0.02,
|
39 |
+
"intermediate_size": 4096,
|
40 |
+
"label2id": {
|
41 |
+
"Bhapa Pitha(\u09ad\u09be\u09aa\u09be \u09aa\u09bf\u09a0\u09be)": "0",
|
42 |
+
"Biriyani(\u09ac\u09bf\u09b0\u09bf\u09df\u09be\u09a8\u09bf)": "1",
|
43 |
+
"Chicken Pulao(\u09ae\u09cb\u09b0\u0997 \u09aa\u09cb\u09b2\u09be\u0993)": "2",
|
44 |
+
"Chickpease Bhuna(\u099b\u09cb\u09b2\u09be\u09ad\u09c1\u09a8\u09be)": "3",
|
45 |
+
"Egg Curry(\u09a1\u09bf\u09ae\u09ad\u09c1\u09a8\u09be)": "4",
|
46 |
+
"Falooda(\u09ab\u09be\u09b2\u09c1\u09a6\u09be)": "5",
|
47 |
+
"Fuchka(\u09ab\u09c1\u099a\u0995\u09be)": "6",
|
48 |
+
"Haleem(\u09b9\u09be\u09b2\u09bf\u09ae)": "7",
|
49 |
+
"Jalebi(\u099c\u09bf\u09b2\u09be\u09aa\u09c0)": "8",
|
50 |
+
"Kala Bhuna(\u0995\u09be\u09b2\u09be \u09ad\u09c1\u09a8\u09be)": "9",
|
51 |
+
"Khichuri(\u0996\u09bf\u099a\u09c1\u09a1\u09bc\u09bf)": "10",
|
52 |
+
"Malpua Pitha(\u09ae\u09be\u09b2\u09aa\u09c1\u09df\u09be \u09aa\u09bf\u09a0\u09be)": "11",
|
53 |
+
"Mustard Hilsa(\u09b8\u09b0\u09b7\u09c7 \u0987\u09b2\u09bf\u09b6)": "12",
|
54 |
+
"Nakshi Pitha(\u09a8\u0995\u09b6\u09bf \u09aa\u09bf\u09a0\u09be)": "13",
|
55 |
+
"Panta Ilish(\u09aa\u09be\u09a8\u09cd\u09a4\u09be \u0987\u09b2\u09bf\u09b6)": "14",
|
56 |
+
"Patishapta Pitha(\u09aa\u09be\u099f\u09bf\u09b8\u09be\u09aa\u099f\u09be)": "15",
|
57 |
+
"Prawn Malai Curry(\u099a\u09bf\u0982\u09dc\u09bf \u09ae\u09be\u09b2\u09be\u0987\u0995\u09be\u09b0\u09c0)": "16",
|
58 |
+
"Rasgulla(\u09b0\u09b8\u0997\u09cb\u09b2\u09cd\u09b2\u09be)": "17",
|
59 |
+
"Rose Cookies(\u09ab\u09c1\u09b2\u099d\u09c1\u09b0\u09bf \u09aa\u09bf\u09a0\u09be)": "18",
|
60 |
+
"Roshmalai(\u09b0\u09b8\u09ae\u09be\u09b2\u09be\u0987)": "19",
|
61 |
+
"Shahi Tukra(\u09b6\u09be\u09b9\u09bf \u099f\u09c1\u0995\u09b0\u09be)": "20",
|
62 |
+
"Shingara(\u09b8\u09bf\u0999\u09cd\u0997\u09be\u09b0\u09be)": "21",
|
63 |
+
"Sweet Yogurt(\u09ae\u09bf\u09b7\u09cd\u099f\u09bf \u09a6\u0987)": "22",
|
64 |
+
"Tehari(\u09a4\u09c7\u09b9\u09be\u09b0\u09bf)": "23"
|
65 |
+
},
|
66 |
+
"layer_norm_eps": 1e-12,
|
67 |
+
"model_type": "vit",
|
68 |
+
"num_attention_heads": 16,
|
69 |
+
"num_channels": 3,
|
70 |
+
"num_hidden_layers": 24,
|
71 |
+
"patch_size": 32,
|
72 |
+
"problem_type": "single_label_classification",
|
73 |
+
"qkv_bias": true,
|
74 |
+
"torch_dtype": "float32",
|
75 |
+
"transformers_version": "4.39.3"
|
76 |
+
}
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f69a611ed08fa88b612f6d9acdb15c8286b6e762f36e53c6ed438aa982e49713
|
3 |
+
size 1222186568
|
preprocessor_config.json
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_valid_processor_keys": [
|
3 |
+
"images",
|
4 |
+
"do_resize",
|
5 |
+
"size",
|
6 |
+
"resample",
|
7 |
+
"do_rescale",
|
8 |
+
"rescale_factor",
|
9 |
+
"do_normalize",
|
10 |
+
"image_mean",
|
11 |
+
"image_std",
|
12 |
+
"return_tensors",
|
13 |
+
"data_format",
|
14 |
+
"input_data_format"
|
15 |
+
],
|
16 |
+
"do_normalize": true,
|
17 |
+
"do_rescale": true,
|
18 |
+
"do_resize": true,
|
19 |
+
"image_mean": [
|
20 |
+
0.5,
|
21 |
+
0.5,
|
22 |
+
0.5
|
23 |
+
],
|
24 |
+
"image_processor_type": "ViTFeatureExtractor",
|
25 |
+
"image_std": [
|
26 |
+
0.5,
|
27 |
+
0.5,
|
28 |
+
0.5
|
29 |
+
],
|
30 |
+
"resample": 2,
|
31 |
+
"rescale_factor": 0.00392156862745098,
|
32 |
+
"size": {
|
33 |
+
"height": 224,
|
34 |
+
"width": 224
|
35 |
+
}
|
36 |
+
}
|
runs/May30_02-46-36_26276f0c1205/events.out.tfevents.1717037227.26276f0c1205.34.0
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
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2 |
+
oid sha256:4197a0a1d71df7bbd0c3b8e789cacaa90a7738c3366fb5956d05f3ed14c52268
|
3 |
+
size 217990
|
training_args.bin
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6c9c714cbe99de709408308003b8e11685c08b8c9f21dbb15aa2f9436d9f449e
|
3 |
+
size 4984
|