Model save

README.md

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+---
+license: apache-2.0
+base_model: facebook/dinov2-base
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- f1
+model-index:
+- name: dinov2-base-ODIR-5K
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: test
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.7563587684069611
+    - name: F1
+      type: f1
+      value: 0.6330645161290323
+---
+
+<!-- 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. -->
+
+# dinov2-base-ODIR-5K
+
+This model is a fine-tuned version of [facebook/dinov2-base](https://huggingface.co/facebook/dinov2-base) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6111
+- Accuracy: 0.7564
+- F1: 0.6331
+
+## 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: 32
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 128
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 6
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
+| 0.6374        | 0.9858 | 52   | 0.6186          | 0.6778   | 0.2031 |
+| 0.5789        | 1.9905 | 105  | 0.5661          | 0.7153   | 0.3794 |
+| 0.5368        | 2.9953 | 158  | 0.5334          | 0.7407   | 0.5756 |
+| 0.4162        | 4.0    | 211  | 0.5747          | 0.6983   | 0.6198 |
+| 0.3679        | 4.9858 | 263  | 0.5700          | 0.7189   | 0.6333 |
+| 0.2431        | 5.9147 | 312  | 0.6111          | 0.7564   | 0.6331 |
+
+
+### Framework versions
+
+- Transformers 4.40.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.19.1
+- Tokenizers 0.19.1

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