End of training

README.md

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+---
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: msi-resnet-18
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: validation
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.6217151244059268
+    - name: F1
+      type: f1
+      value: 0.5152478617168957
+    - name: Precision
+      type: precision
+      value: 0.5801734570391287
+    - name: Recall
+      type: recall
+      value: 0.4633910592025775
+---
+
+<!-- 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. -->
+
+# msi-resnet-18
+
+This model was trained from scratch on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6730
+- Accuracy: 0.6217
+- F1: 0.5152
+- Precision: 0.5802
+- Recall: 0.4634
+
+## 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: 1e-06
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 4
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| 0.5705        | 1.0   | 2015 | 0.6879          | 0.5897   | 0.4460 | 0.5384    | 0.3807 |
+| 0.5309        | 2.0   | 4031 | 0.6788          | 0.6091   | 0.4859 | 0.5657    | 0.4258 |
+| 0.5263        | 3.0   | 6047 | 0.7020          | 0.6036   | 0.4322 | 0.5709    | 0.3477 |
+| 0.496         | 4.0   | 8060 | 0.6730          | 0.6217   | 0.5152 | 0.5802    | 0.4634 |
+
+
+### Framework versions
+
+- Transformers 4.35.2
+- Pytorch 2.0.1+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0