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adam_ViTB-32-224-in21k-1e-4-batch_16_epoch_4_classes_24

Image Classification
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vit
Generated from Trainer
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  1. README.md +132 -0
  2. config.json +76 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +36 -0
  5. runs/May15_19-04-15_72e3d1b1c3ed/events.out.tfevents.1715800483.72e3d1b1c3ed.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-base-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: adam_ViTB-32-224-in21k-1e-4-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: 0.9568965517241379
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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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+ # adam_ViTB-32-224-in21k-1e-4-batch_16_epoch_4_classes_24
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+
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+ This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-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: 0.2034
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+ - Accuracy: 0.9569
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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: 0.0001
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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: 4
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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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+ | 1.5461 | 0.07 | 100 | 1.4329 | 0.9066 |
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+ | 0.7007 | 0.14 | 200 | 0.7957 | 0.8793 |
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+ | 0.429 | 0.21 | 300 | 0.5000 | 0.9224 |
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+ | 0.3771 | 0.28 | 400 | 0.3894 | 0.9224 |
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+ | 0.1591 | 0.35 | 500 | 0.3144 | 0.9382 |
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+ | 0.1708 | 0.42 | 600 | 0.2762 | 0.9440 |
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+ | 0.1994 | 0.49 | 700 | 0.3094 | 0.9224 |
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+ | 0.0824 | 0.56 | 800 | 0.2418 | 0.9339 |
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+ | 0.2089 | 0.63 | 900 | 0.2544 | 0.9282 |
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+ | 0.154 | 0.7 | 1000 | 0.2186 | 0.9440 |
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+ | 0.21 | 0.77 | 1100 | 0.1751 | 0.9540 |
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+ | 0.0946 | 0.84 | 1200 | 0.2017 | 0.9483 |
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+ | 0.1034 | 0.91 | 1300 | 0.2426 | 0.9353 |
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+ | 0.0421 | 0.97 | 1400 | 0.2267 | 0.9411 |
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+ | 0.0164 | 1.04 | 1500 | 0.2640 | 0.9411 |
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+ | 0.0126 | 1.11 | 1600 | 0.2163 | 0.9483 |
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+ | 0.0143 | 1.18 | 1700 | 0.2065 | 0.9483 |
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+ | 0.1191 | 1.25 | 1800 | 0.2615 | 0.9382 |
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+ | 0.01 | 1.32 | 1900 | 0.2328 | 0.9397 |
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+ | 0.072 | 1.39 | 2000 | 0.2196 | 0.9497 |
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+ | 0.0227 | 1.46 | 2100 | 0.2373 | 0.9440 |
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+ | 0.0267 | 1.53 | 2200 | 0.2118 | 0.9468 |
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+ | 0.035 | 1.6 | 2300 | 0.2156 | 0.9468 |
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+ | 0.0127 | 1.67 | 2400 | 0.1456 | 0.9641 |
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+ | 0.011 | 1.74 | 2500 | 0.2419 | 0.9382 |
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+ | 0.0328 | 1.81 | 2600 | 0.1889 | 0.9526 |
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+ | 0.0234 | 1.88 | 2700 | 0.1991 | 0.9483 |
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+ | 0.0055 | 1.95 | 2800 | 0.2120 | 0.9526 |
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+ | 0.0042 | 2.02 | 2900 | 0.2639 | 0.9368 |
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+ | 0.0031 | 2.09 | 3000 | 0.2094 | 0.9454 |
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+ | 0.0392 | 2.16 | 3100 | 0.2004 | 0.9526 |
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+ | 0.0142 | 2.23 | 3200 | 0.2160 | 0.9483 |
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+ | 0.0026 | 2.3 | 3300 | 0.2103 | 0.9569 |
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+ | 0.0024 | 2.37 | 3400 | 0.2394 | 0.9440 |
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+ | 0.0036 | 2.44 | 3500 | 0.2459 | 0.9454 |
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+ | 0.0427 | 2.51 | 3600 | 0.2159 | 0.9497 |
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+ | 0.002 | 2.58 | 3700 | 0.2357 | 0.9483 |
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+ | 0.0034 | 2.65 | 3800 | 0.3332 | 0.9325 |
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+ | 0.0282 | 2.72 | 3900 | 0.2469 | 0.9497 |
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+ | 0.0077 | 2.79 | 4000 | 0.2483 | 0.9540 |
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+ | 0.0016 | 2.86 | 4100 | 0.2169 | 0.9526 |
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+ | 0.0015 | 2.92 | 4200 | 0.2104 | 0.9526 |
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+ | 0.0015 | 2.99 | 4300 | 0.2196 | 0.9526 |
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+ | 0.0014 | 3.06 | 4400 | 0.2343 | 0.9440 |
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+ | 0.0013 | 3.13 | 4500 | 0.2106 | 0.9483 |
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+ | 0.0013 | 3.2 | 4600 | 0.1880 | 0.9526 |
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+ | 0.0014 | 3.27 | 4700 | 0.1891 | 0.9483 |
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+ | 0.0013 | 3.34 | 4800 | 0.1896 | 0.9526 |
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+ | 0.0012 | 3.41 | 4900 | 0.1958 | 0.9468 |
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+ | 0.0012 | 3.48 | 5000 | 0.1978 | 0.9526 |
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+ | 0.0011 | 3.55 | 5100 | 0.1978 | 0.9526 |
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+ | 0.0011 | 3.62 | 5200 | 0.2108 | 0.9555 |
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+ | 0.0011 | 3.69 | 5300 | 0.2044 | 0.9583 |
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+ | 0.0011 | 3.76 | 5400 | 0.2062 | 0.9598 |
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+ | 0.001 | 3.83 | 5500 | 0.2058 | 0.9598 |
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+ | 0.0011 | 3.9 | 5600 | 0.2046 | 0.9569 |
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+ | 0.001 | 3.97 | 5700 | 0.2034 | 0.9569 |
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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-base-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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+ "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": 3072,
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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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+ "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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+ "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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+ "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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+ "torch_dtype": "float32",
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+ "transformers_version": "4.39.3"
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