metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: resnet-152-fv-finetuned-memess
    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: 0.767387944358578
          - name: Precision
            type: precision
            value: 0.7651125602674349
          - name: Recall
            type: recall
            value: 0.767387944358578
          - name: F1
            type: f1
            value: 0.7646848616766787

resnet-152-fv-finetuned-memess

This model is a fine-tuned version of microsoft/resnet-152 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6281
Accuracy: 0.7674
Precision: 0.7651
Recall: 0.7674
F1: 0.7647

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
1.5902	0.99	20	1.5519	0.4938	0.3491	0.4938	0.3529
1.4694	1.99	40	1.3730	0.4892	0.4095	0.4892	0.3222
1.3129	2.99	60	1.2052	0.5301	0.3504	0.5301	0.4005
1.1831	3.99	80	1.1142	0.5587	0.4077	0.5587	0.4444
1.0581	4.99	100	0.9930	0.6012	0.5680	0.6012	0.5108
0.9464	5.99	120	0.9263	0.6507	0.6200	0.6507	0.6029
0.8581	6.99	140	0.8400	0.6917	0.6645	0.6917	0.6638
0.7739	7.99	160	0.7829	0.7087	0.6918	0.7087	0.6845
0.6762	8.99	180	0.7512	0.7318	0.7206	0.7318	0.7189
0.6162	9.99	200	0.7409	0.7264	0.7244	0.7264	0.7241
0.5546	10.99	220	0.6936	0.7465	0.7429	0.7465	0.7395
0.4633	11.99	240	0.6779	0.7473	0.7393	0.7473	0.7412
0.4373	12.99	260	0.6736	0.7573	0.7492	0.7573	0.7523
0.4074	13.99	280	0.6534	0.7566	0.7516	0.7566	0.7528
0.39	14.99	300	0.6521	0.7651	0.7603	0.7651	0.7608
0.3766	15.99	320	0.6499	0.7682	0.7607	0.7682	0.7630
0.3507	16.99	340	0.6497	0.7697	0.7686	0.7697	0.7686
0.3589	17.99	360	0.6519	0.7535	0.7485	0.7535	0.7502
0.3261	18.99	380	0.6449	0.7589	0.7597	0.7589	0.7585
0.3234	19.99	400	0.6281	0.7674	0.7651	0.7674	0.7647

Framework versions

Transformers 4.24.0.dev0
Pytorch 1.11.0+cu102
Datasets 2.6.1.dev0
Tokenizers 0.13.1