metadata

license: apache-2.0
base_model: microsoft/resnet-18
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: font-identifier
    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: 0.9102040816326531

font-identifier

This model is a fine-tuned version of microsoft/resnet-18 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.3980
Accuracy: 0.9102

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
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
3.9105	0.98	30	3.7931	0.0551
3.2821	1.98	61	2.9878	0.2755
2.4752	2.99	92	2.1760	0.4408
1.9958	4.0	123	1.6964	0.5327
1.6609	4.98	153	1.4001	0.6265
1.4328	5.98	184	1.1766	0.6796
1.2677	6.99	215	1.0262	0.7163
1.1174	8.0	246	0.8758	0.7653
1.0564	8.98	276	0.7675	0.8184
0.9003	9.98	307	0.7161	0.8286
0.8711	10.99	338	0.6461	0.8224
0.7954	12.0	369	0.5683	0.8653
0.743	12.98	399	0.5438	0.8510
0.6914	13.98	430	0.5129	0.8878
0.6714	14.99	461	0.4418	0.8857
0.663	16.0	492	0.4555	0.8694
0.6326	16.98	522	0.4746	0.8755
0.5831	17.98	553	0.4263	0.8776
0.571	18.99	584	0.4305	0.8857
0.6543	19.51	600	0.3980	0.9102

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.0.0
Datasets 2.12.0
Tokenizers 0.14.1