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ViTL-32-224-1e4-batch_16_epoch_4_classes_24

Image Classification
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vit
Generated from Trainer
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  1. README.md +161 -0
  2. config.json +76 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +36 -0
  5. runs/May30_02-46-36_26276f0c1205/events.out.tfevents.1717037227.26276f0c1205.34.0 +3 -0
  6. training_args.bin +3 -0
README.md ADDED
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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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+
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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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+ # ViTL-32-224-1e4-batch_16_epoch_4_classes_24
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+
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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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+
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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: 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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+
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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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+ | 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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+
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+
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+ ### Framework versions
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+
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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
config.json ADDED
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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)",
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+ "1": "Biriyani(\u09ac\u09bf\u09b0\u09bf\u09df\u09be\u09a8\u09bf)",
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+ "10": "Khichuri(\u0996\u09bf\u099a\u09c1\u09a1\u09bc\u09bf)",
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+ "11": "Malpua Pitha(\u09ae\u09be\u09b2\u09aa\u09c1\u09df\u09be \u09aa\u09bf\u09a0\u09be)",
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+ "12": "Mustard Hilsa(\u09b8\u09b0\u09b7\u09c7 \u0987\u09b2\u09bf\u09b6)",
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+ "13": "Nakshi Pitha(\u09a8\u0995\u09b6\u09bf \u09aa\u09bf\u09a0\u09be)",
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+ "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)",
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+ "16": "Prawn Malai Curry(\u099a\u09bf\u0982\u09dc\u09bf \u09ae\u09be\u09b2\u09be\u0987\u0995\u09be\u09b0\u09c0)",
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+ "17": "Rasgulla(\u09b0\u09b8\u0997\u09cb\u09b2\u09cd\u09b2\u09be)",
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+ "18": "Rose Cookies(\u09ab\u09c1\u09b2\u099d\u09c1\u09b0\u09bf \u09aa\u09bf\u09a0\u09be)",
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+ "19": "Roshmalai(\u09b0\u09b8\u09ae\u09be\u09b2\u09be\u0987)",
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+ "2": "Chicken Pulao(\u09ae\u09cb\u09b0\u0997 \u09aa\u09cb\u09b2\u09be\u0993)",
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+ "20": "Shahi Tukra(\u09b6\u09be\u09b9\u09bf \u099f\u09c1\u0995\u09b0\u09be)",
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+ "21": "Shingara(\u09b8\u09bf\u0999\u09cd\u0997\u09be\u09b0\u09be)",
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+ "22": "Sweet Yogurt(\u09ae\u09bf\u09b7\u09cd\u099f\u09bf \u09a6\u0987)",
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+ "23": "Tehari(\u09a4\u09c7\u09b9\u09be\u09b0\u09bf)",
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+ "3": "Chickpease Bhuna(\u099b\u09cb\u09b2\u09be\u09ad\u09c1\u09a8\u09be)",
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+ "4": "Egg Curry(\u09a1\u09bf\u09ae\u09ad\u09c1\u09a8\u09be)",
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+ "5": "Falooda(\u09ab\u09be\u09b2\u09c1\u09a6\u09be)",
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+ "6": "Fuchka(\u09ab\u09c1\u099a\u0995\u09be)",
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+ "7": "Haleem(\u09b9\u09be\u09b2\u09bf\u09ae)",
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+ "8": "Jalebi(\u099c\u09bf\u09b2\u09be\u09aa\u09c0)",
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+ "9": "Kala Bhuna(\u0995\u09be\u09b2\u09be \u09ad\u09c1\u09a8\u09be)"
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+ },
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+ "image_size": 224,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 4096,
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+ "label2id": {
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+ "Bhapa Pitha(\u09ad\u09be\u09aa\u09be \u09aa\u09bf\u09a0\u09be)": "0",
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+ "Biriyani(\u09ac\u09bf\u09b0\u09bf\u09df\u09be\u09a8\u09bf)": "1",
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+ "Chicken Pulao(\u09ae\u09cb\u09b0\u0997 \u09aa\u09cb\u09b2\u09be\u0993)": "2",
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+ "Chickpease Bhuna(\u099b\u09cb\u09b2\u09be\u09ad\u09c1\u09a8\u09be)": "3",
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+ "Egg Curry(\u09a1\u09bf\u09ae\u09ad\u09c1\u09a8\u09be)": "4",
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+ "Falooda(\u09ab\u09be\u09b2\u09c1\u09a6\u09be)": "5",
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+ "Fuchka(\u09ab\u09c1\u099a\u0995\u09be)": "6",
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+ "Haleem(\u09b9\u09be\u09b2\u09bf\u09ae)": "7",
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+ "Jalebi(\u099c\u09bf\u09b2\u09be\u09aa\u09c0)": "8",
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+ "Kala Bhuna(\u0995\u09be\u09b2\u09be \u09ad\u09c1\u09a8\u09be)": "9",
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+ "Khichuri(\u0996\u09bf\u099a\u09c1\u09a1\u09bc\u09bf)": "10",
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+ "Malpua Pitha(\u09ae\u09be\u09b2\u09aa\u09c1\u09df\u09be \u09aa\u09bf\u09a0\u09be)": "11",
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+ "Mustard Hilsa(\u09b8\u09b0\u09b7\u09c7 \u0987\u09b2\u09bf\u09b6)": "12",
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+ "Nakshi Pitha(\u09a8\u0995\u09b6\u09bf \u09aa\u09bf\u09a0\u09be)": "13",
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+ "Panta Ilish(\u09aa\u09be\u09a8\u09cd\u09a4\u09be \u0987\u09b2\u09bf\u09b6)": "14",
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+ "Patishapta Pitha(\u09aa\u09be\u099f\u09bf\u09b8\u09be\u09aa\u099f\u09be)": "15",
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+ "Prawn Malai Curry(\u099a\u09bf\u0982\u09dc\u09bf \u09ae\u09be\u09b2\u09be\u0987\u0995\u09be\u09b0\u09c0)": "16",
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+ "Rasgulla(\u09b0\u09b8\u0997\u09cb\u09b2\u09cd\u09b2\u09be)": "17",
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+ "Rose Cookies(\u09ab\u09c1\u09b2\u099d\u09c1\u09b0\u09bf \u09aa\u09bf\u09a0\u09be)": "18",
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+ "Roshmalai(\u09b0\u09b8\u09ae\u09be\u09b2\u09be\u0987)": "19",
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+ "Shahi Tukra(\u09b6\u09be\u09b9\u09bf \u099f\u09c1\u0995\u09b0\u09be)": "20",
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+ "Shingara(\u09b8\u09bf\u0999\u09cd\u0997\u09be\u09b0\u09be)": "21",
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+ "Sweet Yogurt(\u09ae\u09bf\u09b7\u09cd\u099f\u09bf \u09a6\u0987)": "22",
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+ "Tehari(\u09a4\u09c7\u09b9\u09be\u09b0\u09bf)": "23"
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ "num_attention_heads": 16,
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+ "num_channels": 3,
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+ "num_hidden_layers": 24,
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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": "4.39.3"
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+ }
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