End of training

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: google/vit-base-patch16-224
+tags:
+- generated_from_trainer
+datasets:
+- medmnist-v2
+metrics:
+- accuracy
+- f1
+model-index:
+- name: ViT_bloodmnist_std_30
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: medmnist-v2
+      type: medmnist-v2
+      config: bloodmnist
+      split: validation
+      args: bloodmnist
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.9429991230634317
+    - name: F1
+      type: f1
+      value: 0.9339022055894328
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# ViT_bloodmnist_std_30
+
+This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1697
+- Accuracy: 0.9430
+- F1: 0.9339
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     |
+|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
+| 0.5658        | 0.0595 | 200   | 1.2306          | 0.5076   | 0.4526 |
+| 0.2887        | 0.1189 | 400   | 0.6368          | 0.7751   | 0.7410 |
+| 0.2406        | 0.1784 | 600   | 0.6641          | 0.7827   | 0.7050 |
+| 0.2229        | 0.2378 | 800   | 0.4808          | 0.8072   | 0.7832 |
+| 0.1955        | 0.2973 | 1000  | 0.4868          | 0.8002   | 0.7827 |
+| 0.1654        | 0.3567 | 1200  | 0.3306          | 0.8657   | 0.8466 |
+| 0.1627        | 0.4162 | 1400  | 0.3754          | 0.8732   | 0.8367 |
+| 0.1479        | 0.4756 | 1600  | 0.2421          | 0.9118   | 0.8949 |
+| 0.1501        | 0.5351 | 1800  | 0.2125          | 0.9235   | 0.9076 |
+| 0.1372        | 0.5945 | 2000  | 0.3706          | 0.8616   | 0.8337 |
+| 0.1194        | 0.6540 | 2200  | 0.1552          | 0.9451   | 0.9370 |
+| 0.1194        | 0.7134 | 2400  | 0.2345          | 0.9194   | 0.8992 |
+| 0.1135        | 0.7729 | 2600  | 0.2121          | 0.9287   | 0.9113 |
+| 0.1032        | 0.8323 | 2800  | 0.2023          | 0.9299   | 0.9152 |
+| 0.1006        | 0.8918 | 3000  | 0.1784          | 0.9451   | 0.9376 |
+| 0.0814        | 0.9512 | 3200  | 0.1273          | 0.9533   | 0.9484 |
+| 0.0842        | 1.0107 | 3400  | 0.2012          | 0.9363   | 0.9240 |
+| 0.0426        | 1.0702 | 3600  | 0.2221          | 0.9340   | 0.9280 |
+| 0.06          | 1.1296 | 3800  | 0.2641          | 0.9100   | 0.9037 |
+| 0.0632        | 1.1891 | 4000  | 0.1796          | 0.9433   | 0.9339 |
+| 0.0506        | 1.2485 | 4200  | 0.2771          | 0.8989   | 0.8838 |
+| 0.0467        | 1.3080 | 4400  | 0.1939          | 0.9393   | 0.9265 |
+| 0.0469        | 1.3674 | 4600  | 0.1896          | 0.9410   | 0.9322 |
+| 0.0457        | 1.4269 | 4800  | 0.1477          | 0.9509   | 0.9479 |
+| 0.0416        | 1.4863 | 5000  | 0.2789          | 0.9206   | 0.9086 |
+| 0.043         | 1.5458 | 5200  | 0.1832          | 0.9463   | 0.9389 |
+| 0.0412        | 1.6052 | 5400  | 0.2100          | 0.9404   | 0.9337 |
+| 0.0358        | 1.6647 | 5600  | 0.2368          | 0.9287   | 0.9135 |
+| 0.0376        | 1.7241 | 5800  | 0.2668          | 0.9252   | 0.9096 |
+| 0.0385        | 1.7836 | 6000  | 0.2145          | 0.9398   | 0.9291 |
+| 0.0273        | 1.8430 | 6200  | 0.1995          | 0.9433   | 0.9302 |
+| 0.0251        | 1.9025 | 6400  | 0.1900          | 0.9486   | 0.9395 |
+| 0.0298        | 1.9620 | 6600  | 0.1617          | 0.9597   | 0.9526 |
+| 0.02          | 2.0214 | 6800  | 0.1984          | 0.9463   | 0.9343 |
+| 0.0083        | 2.0809 | 7000  | 0.1899          | 0.9498   | 0.9377 |
+| 0.0068        | 2.1403 | 7200  | 0.2592          | 0.9340   | 0.9199 |
+| 0.0059        | 2.1998 | 7400  | 0.2101          | 0.9428   | 0.9335 |
+| 0.0066        | 2.2592 | 7600  | 0.2247          | 0.9422   | 0.9259 |
+| 0.0062        | 2.3187 | 7800  | 0.2370          | 0.9439   | 0.9348 |
+| 0.0084        | 2.3781 | 8000  | 0.2266          | 0.9474   | 0.9390 |
+| 0.0049        | 2.4376 | 8200  | 0.2343          | 0.9480   | 0.9354 |
+| 0.0075        | 2.4970 | 8400  | 0.2032          | 0.9486   | 0.9378 |
+| 0.0025        | 2.5565 | 8600  | 0.1916          | 0.9515   | 0.9436 |
+| 0.0064        | 2.6159 | 8800  | 0.2066          | 0.9533   | 0.9436 |
+| 0.004         | 2.6754 | 9000  | 0.2404          | 0.9445   | 0.9321 |
+| 0.0029        | 2.7348 | 9200  | 0.2402          | 0.9439   | 0.9322 |
+| 0.0008        | 2.7943 | 9400  | 0.2256          | 0.9468   | 0.9365 |
+| 0.003         | 2.8537 | 9600  | 0.2265          | 0.9492   | 0.9408 |
+| 0.002         | 2.9132 | 9800  | 0.2278          | 0.9515   | 0.9419 |
+| 0.0013        | 2.9727 | 10000 | 0.2175          | 0.9504   | 0.9422 |
+
+
+### Framework versions
+
+- Transformers 4.45.1
+- Pytorch 2.4.0
+- Datasets 3.0.1
+- Tokenizers 0.20.0

config.json

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+{
+  "_name_or_path": "google/vit-base-patch16-224",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "basophil",
+    "1": "eosinophil",
+    "2": "erythroblast",
+    "3": "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)",
+    "4": "lymphocyte",
+    "5": "monocyte",
+    "6": "neutrophil",
+    "7": "platelet"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "basophil": "0",
+    "eosinophil": "1",
+    "erythroblast": "2",
+    "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)": "3",
+    "lymphocyte": "4",
+    "monocyte": "5",
+    "neutrophil": "6",
+    "platelet": "7"
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1"
+}

model.safetensors

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+size 343242432

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

runs/Nov09_10-50-55_533218bcf843/events.out.tfevents.1731149458.533218bcf843.24.0

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training_args.bin

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