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resnet-50-FV2-finetuned-memes
This model is a fine-tuned version of 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
