update model card README.md

README.md

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+---
+license: other
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: mit-b2-VF2-finetuned-memes
+  results:
+  - task:
+      name: Image Classification
+      type: image-classification
+    dataset:
+      name: imagefolder
+      type: imagefolder
+      config: default
+      split: train
+      args: default
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.8307573415765069
+    - name: Precision
+      type: precision
+      value: 0.8272186656187493
+    - name: Recall
+      type: recall
+      value: 0.8307573415765069
+    - name: F1
+      type: f1
+      value: 0.8286939083150942
+---
+
+<!-- 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. -->
+
+# mit-b2-VF2-finetuned-memes
+
+This model is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6547
+- Accuracy: 0.8308
+- Precision: 0.8272
+- Recall: 0.8308
+- F1: 0.8287
+
+## 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: 0.00012
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 256
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 20
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.3077        | 0.99  | 20   | 1.1683          | 0.5549   | 0.5621    | 0.5549 | 0.5286 |
+| 0.9359        | 1.99  | 40   | 0.8573          | 0.6731   | 0.6807    | 0.6731 | 0.6535 |
+| 0.7219        | 2.99  | 60   | 0.7106          | 0.7272   | 0.7359    | 0.7272 | 0.7246 |
+| 0.6013        | 3.99  | 80   | 0.6445          | 0.7550   | 0.7686    | 0.7550 | 0.7558 |
+| 0.5243        | 4.99  | 100  | 0.6717          | 0.7573   | 0.8077    | 0.7573 | 0.7584 |
+| 0.4409        | 5.99  | 120  | 0.5315          | 0.8068   | 0.8027    | 0.8068 | 0.7989 |
+| 0.3325        | 6.99  | 140  | 0.5159          | 0.8230   | 0.8236    | 0.8230 | 0.8158 |
+| 0.2719        | 7.99  | 160  | 0.5250          | 0.8215   | 0.8227    | 0.8215 | 0.8202 |
+| 0.242         | 8.99  | 180  | 0.5087          | 0.8277   | 0.8260    | 0.8277 | 0.8268 |
+| 0.2247        | 9.99  | 200  | 0.5313          | 0.8215   | 0.8275    | 0.8215 | 0.8218 |
+| 0.1955        | 10.99 | 220  | 0.6167          | 0.8130   | 0.8062    | 0.8130 | 0.8073 |
+| 0.1567        | 11.99 | 240  | 0.5859          | 0.8168   | 0.8185    | 0.8168 | 0.8173 |
+| 0.1479        | 12.99 | 260  | 0.5938          | 0.8215   | 0.8169    | 0.8215 | 0.8178 |
+| 0.1241        | 13.99 | 280  | 0.6187          | 0.8261   | 0.8234    | 0.8261 | 0.8239 |
+| 0.1114        | 14.99 | 300  | 0.6419          | 0.8261   | 0.8351    | 0.8261 | 0.8293 |
+| 0.1022        | 15.99 | 320  | 0.6322          | 0.8323   | 0.8284    | 0.8323 | 0.8294 |
+| 0.0941        | 16.99 | 340  | 0.6595          | 0.8269   | 0.8266    | 0.8269 | 0.8263 |
+| 0.0935        | 17.99 | 360  | 0.6674          | 0.8269   | 0.8218    | 0.8269 | 0.8237 |
+| 0.089         | 18.99 | 380  | 0.6533          | 0.8253   | 0.8222    | 0.8253 | 0.8235 |
+| 0.0794        | 19.99 | 400  | 0.6547          | 0.8308   | 0.8272    | 0.8308 | 0.8287 |
+
+
+### Framework versions
+
+- Transformers 4.24.0.dev0
+- Pytorch 1.11.0+cu102
+- Datasets 2.6.1.dev0
+- Tokenizers 0.13.1