metadata

license: apache-2.0
base_model: microsoft/resnet-101
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: resnet101_rvl-cdip
    results: []

resnet101_rvl-cdip

This model is a fine-tuned version of microsoft/resnet-101 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6158
Accuracy: 0.8210
Brier Loss: 0.2556
Nll: 1.7696
F1 Micro: 0.8210
F1 Macro: 0.8209
Ece: 0.0176
Aurc: 0.0418

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: 2e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
1.3521	1.0	5000	1.2626	0.6133	0.5108	2.7262	0.6133	0.6042	0.0455	0.1644
0.942	2.0	10000	0.9005	0.7318	0.3723	2.2139	0.7318	0.7293	0.0174	0.0862
0.7983	3.0	15000	0.7691	0.7723	0.3198	2.0444	0.7723	0.7714	0.0139	0.0641
0.7167	4.0	20000	0.7048	0.7924	0.2931	1.9414	0.7924	0.7931	0.0135	0.0541
0.6656	5.0	25000	0.6658	0.8052	0.2770	1.8581	0.8052	0.8056	0.0108	0.0486
0.6252	6.0	30000	0.6415	0.8117	0.2670	1.8157	0.8117	0.8112	0.0128	0.0455
0.6038	7.0	35000	0.6269	0.8176	0.2607	1.7833	0.8176	0.8180	0.0144	0.0432
0.5784	8.0	40000	0.6217	0.8195	0.2583	1.7723	0.8195	0.8195	0.0151	0.0425
0.5583	9.0	45000	0.6150	0.8214	0.2553	1.7719	0.8214	0.8214	0.0164	0.0415
0.5519	10.0	50000	0.6158	0.8210	0.2556	1.7696	0.8210	0.8209	0.0176	0.0418

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3