metadata

license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-colon-cancer-classification
    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: 0.8210439105219552
pipeline_tag: image-classification

vit-colon-cancer-classification

This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6794
Accuracy: 0.8210

Model description

Fine tuned vision transformer for classification of colon cancer.
Four classes: Normal Tissue, Serrated Lesion, Adenoma, Adenocarcinoma

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: 10
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8993	0.35	100	0.6462	0.7341
0.6042	0.71	200	0.6380	0.7432
0.6284	1.06	300	0.5628	0.7821
0.5494	1.42	400	0.5643	0.7788
0.5218	1.77	500	0.5478	0.7970
0.5053	2.13	600	0.5356	0.7846
0.4441	2.48	700	0.6928	0.7133
0.4492	2.84	800	0.4898	0.8078
0.429	3.19	900	0.5166	0.8020
0.3474	3.55	1000	0.5373	0.8061
0.337	3.9	1100	0.5442	0.7904
0.3243	4.26	1200	0.5171	0.8111
0.3003	4.61	1300	0.5463	0.8070
0.3127	4.96	1400	0.5122	0.8202
0.2587	5.32	1500	0.5807	0.8152
0.2434	5.67	1600	0.5392	0.8219
0.1996	6.03	1700	0.6343	0.8045
0.2033	6.38	1800	0.5855	0.8128
0.2056	6.74	1900	0.6516	0.8144
0.1927	7.09	2000	0.5770	0.8227
0.1688	7.45	2100	0.6153	0.8293
0.1566	7.8	2200	0.5994	0.8268
0.1406	8.16	2300	0.6192	0.8277
0.1381	8.51	2400	0.6334	0.8202
0.12	8.87	2500	0.6444	0.8136
0.104	9.22	2600	0.6709	0.8202
0.1049	9.57	2700	0.6752	0.8227
0.1349	9.93	2800	0.6980	0.8186
0.0846	10.28	2900	0.6794	0.8210

Framework versions

Transformers 4.35.2
Pytorch 2.0.1
Datasets 2.15.0
Tokenizers 0.15.0