metadata

license: other
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: mobilenet_v2_1.0_224-cxr-view
    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: 0.929384965831435

mobilenet_v2_1.0_224-cxr-view

This model is a fine-tuned version of google/mobilenet_v2_1.0_224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2278
Accuracy: 0.9294

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-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.7049	1.0	109	0.6746	0.7449
0.6565	2.0	219	0.6498	0.6743
0.5699	3.0	328	0.5730	0.7995
0.5702	4.0	438	0.5119	0.8087
0.4849	5.0	547	0.4356	0.8679
0.356	6.0	657	0.4641	0.8087
0.3713	7.0	766	0.3407	0.8679
0.4571	8.0	876	0.4896	0.7813
0.3896	9.0	985	0.3124	0.8884
0.3422	10.0	1095	0.2791	0.9271
0.3358	11.0	1204	0.3998	0.8246
0.3658	12.0	1314	0.2716	0.9066
0.4547	13.0	1423	0.5828	0.7973
0.2615	14.0	1533	0.3446	0.8542
0.377	15.0	1642	0.6322	0.7312
0.2846	16.0	1752	0.2621	0.9248
0.3433	17.0	1861	0.3709	0.8383
0.2851	18.0	1971	0.8134	0.7312
0.2298	19.0	2080	0.4324	0.8314
0.3916	20.0	2190	0.3631	0.8360
0.3049	21.0	2299	0.3405	0.8633
0.3068	22.0	2409	0.2585	0.9021
0.3091	23.0	2518	0.2278	0.9294
0.2749	24.0	2628	0.2963	0.9043
0.3543	25.0	2737	0.2637	0.8975
0.3024	26.0	2847	0.2966	0.8998
0.2593	27.0	2956	0.3842	0.8542
0.1979	28.0	3066	0.2711	0.8884
0.2549	29.0	3175	0.3145	0.8633
0.3216	29.86	3270	0.4565	0.8155

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3