update model card README.md

README.md

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+---
+license: apache-2.0
+tags:
+- generated_from_trainer
+datasets:
+- imagefolder
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+model-index:
+- name: resnet-50-FV2-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: 0.6452859350850078
+    - name: Precision
+      type: precision
+      value: 0.5727919568038408
+    - name: Recall
+      type: recall
+      value: 0.6452859350850078
+    - name: F1
+      type: f1
+      value: 0.5963647629954705
+---
+
+<!-- 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. -->
+
+# resnet-50-FV2-finetuned-memes
+
+This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9263
+- Accuracy: 0.6453
+- Precision: 0.5728
+- Recall: 0.6453
+- F1: 0.5964
+
+## 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.5763        | 0.99  | 20   | 1.5575          | 0.4281   | 0.2966    | 0.4281 | 0.2669 |
+| 1.4761        | 1.99  | 40   | 1.4424          | 0.4343   | 0.1886    | 0.4343 | 0.2630 |
+| 1.3563        | 2.99  | 60   | 1.3240          | 0.4343   | 0.1886    | 0.4343 | 0.2630 |
+| 1.2824        | 3.99  | 80   | 1.2636          | 0.4389   | 0.3097    | 0.4389 | 0.2734 |
+| 1.2315        | 4.99  | 100  | 1.2119          | 0.4529   | 0.3236    | 0.4529 | 0.3042 |
+| 1.1956        | 5.99  | 120  | 1.1764          | 0.4900   | 0.3731    | 0.4900 | 0.3692 |
+| 1.1452        | 6.99  | 140  | 1.1424          | 0.5147   | 0.3963    | 0.5147 | 0.4090 |
+| 1.1076        | 7.99  | 160  | 1.1190          | 0.5371   | 0.4121    | 0.5371 | 0.4392 |
+| 1.0679        | 8.99  | 180  | 1.0825          | 0.5719   | 0.4465    | 0.5719 | 0.4831 |
+| 1.0432        | 9.99  | 200  | 1.0482          | 0.5750   | 0.5404    | 0.5750 | 0.4930 |
+| 0.9903        | 10.99 | 220  | 1.0275          | 0.5958   | 0.5459    | 0.5958 | 0.5241 |
+| 0.9675        | 11.99 | 240  | 1.0145          | 0.6051   | 0.5350    | 0.6051 | 0.5379 |
+| 0.9335        | 12.99 | 260  | 0.9860          | 0.6175   | 0.5537    | 0.6175 | 0.5527 |
+| 0.9157        | 13.99 | 280  | 0.9683          | 0.6105   | 0.5386    | 0.6105 | 0.5504 |
+| 0.8901        | 14.99 | 300  | 0.9558          | 0.6352   | 0.5686    | 0.6352 | 0.5833 |
+| 0.8722        | 15.99 | 320  | 0.9382          | 0.6345   | 0.5657    | 0.6345 | 0.5807 |
+| 0.854         | 16.99 | 340  | 0.9322          | 0.6376   | 0.5623    | 0.6376 | 0.5856 |
+| 0.8494        | 17.99 | 360  | 0.9287          | 0.6422   | 0.6675    | 0.6422 | 0.5918 |
+| 0.8652        | 18.99 | 380  | 0.9212          | 0.6399   | 0.5640    | 0.6399 | 0.5863 |
+| 0.846         | 19.99 | 400  | 0.9263          | 0.6453   | 0.5728    | 0.6453 | 0.5964 |
+
+
+### Framework versions
+
+- Transformers 4.24.0.dev0
+- Pytorch 1.11.0+cu102
+- Datasets 2.6.1.dev0
+- Tokenizers 0.13.1