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: resnet152-FV-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: 0.7557959814528593
+    - name: Precision
+      type: precision
+      value: 0.7556690736625777
+    - name: Recall
+      type: recall
+      value: 0.7557959814528593
+    - name: F1
+      type: f1
+      value: 0.7545674798253312
+---
+
+<!-- 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. -->
+
+# resnet152-FV-finetuned-memes
+
+This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.6772
+- Accuracy: 0.7558
+- Precision: 0.7557
+- Recall: 0.7558
+- F1: 0.7546
+
+## 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     |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
+| 1.5739        | 0.99  | 20   | 1.5427          | 0.4521   | 0.3131    | 0.4521 | 0.2880 |
+| 1.4353        | 1.99  | 40   | 1.3786          | 0.4490   | 0.3850    | 0.4490 | 0.2791 |
+| 1.3026        | 2.99  | 60   | 1.2734          | 0.4799   | 0.3073    | 0.4799 | 0.3393 |
+| 1.1579        | 3.99  | 80   | 1.1378          | 0.5278   | 0.4300    | 0.5278 | 0.4143 |
+| 1.0276        | 4.99  | 100  | 1.0231          | 0.5734   | 0.4497    | 0.5734 | 0.4865 |
+| 0.8826        | 5.99  | 120  | 0.9228          | 0.6252   | 0.5983    | 0.6252 | 0.5637 |
+| 0.766         | 6.99  | 140  | 0.8441          | 0.6662   | 0.6474    | 0.6662 | 0.6320 |
+| 0.6732        | 7.99  | 160  | 0.8009          | 0.6901   | 0.6759    | 0.6901 | 0.6704 |
+| 0.5653        | 8.99  | 180  | 0.7535          | 0.7218   | 0.7141    | 0.7218 | 0.7129 |
+| 0.4957        | 9.99  | 200  | 0.7317          | 0.7257   | 0.7248    | 0.7257 | 0.7200 |
+| 0.4534        | 10.99 | 220  | 0.6808          | 0.7434   | 0.7405    | 0.7434 | 0.7390 |
+| 0.3792        | 11.99 | 240  | 0.6949          | 0.7450   | 0.7454    | 0.7450 | 0.7399 |
+| 0.3489        | 12.99 | 260  | 0.6746          | 0.7496   | 0.7511    | 0.7496 | 0.7474 |
+| 0.3113        | 13.99 | 280  | 0.6637          | 0.7573   | 0.7638    | 0.7573 | 0.7579 |
+| 0.2947        | 14.99 | 300  | 0.6451          | 0.7589   | 0.7667    | 0.7589 | 0.7610 |
+| 0.2776        | 15.99 | 320  | 0.6754          | 0.7543   | 0.7565    | 0.7543 | 0.7525 |
+| 0.2611        | 16.99 | 340  | 0.6808          | 0.7550   | 0.7607    | 0.7550 | 0.7529 |
+| 0.2428        | 17.99 | 360  | 0.7005          | 0.7457   | 0.7497    | 0.7457 | 0.7404 |
+| 0.2346        | 18.99 | 380  | 0.6597          | 0.7573   | 0.7642    | 0.7573 | 0.7590 |
+| 0.2367        | 19.99 | 400  | 0.6772          | 0.7558   | 0.7557    | 0.7558 | 0.7546 |
+
+
+### Framework versions
+
+- Transformers 4.24.0.dev0
+- Pytorch 1.11.0+cu102
+- Datasets 2.6.1.dev0
+- Tokenizers 0.13.1