metadata

library_name: transformers
license: apache-2.0
base_model: facebook/convnext-tiny-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: convnext-tiny-224-finetuned-papsmear
    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: 0.8897058823529411

convnext-tiny-224-finetuned-papsmear

This model is a fine-tuned version of facebook/convnext-tiny-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2836
Accuracy: 0.8897

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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.9231	9	1.7808	0.1691
1.8057	1.9487	19	1.6808	0.3309
1.7394	2.9744	29	1.5825	0.3382
1.6408	4.0	39	1.4576	0.375
1.5428	4.9231	48	1.3281	0.5221
1.3931	5.9487	58	1.2044	0.5588
1.2669	6.9744	68	1.0756	0.6103
1.1355	8.0	78	0.9845	0.6324
1.0379	8.9231	87	0.9260	0.6618
0.9571	9.9487	97	0.8539	0.6618
0.8376	10.9744	107	0.7998	0.7279
0.7942	12.0	117	0.7573	0.75
0.7095	12.9231	126	0.7005	0.7426
0.7022	13.9487	136	0.6834	0.7868
0.6504	14.9744	146	0.6552	0.7721
0.589	16.0	156	0.6192	0.8015
0.5679	16.9231	165	0.5738	0.8088
0.5236	17.9487	175	0.5617	0.8015
0.5244	18.9744	185	0.5073	0.8235
0.4781	20.0	195	0.5112	0.8162
0.453	20.9231	204	0.4650	0.8235
0.4544	21.9487	214	0.4591	0.8456
0.419	22.9744	224	0.4403	0.8309
0.4146	24.0	234	0.4292	0.8382
0.398	24.9231	243	0.4315	0.8382
0.3918	25.9487	253	0.3980	0.8676
0.361	26.9744	263	0.3758	0.8603
0.3355	28.0	273	0.3657	0.8603
0.3483	28.9231	282	0.3669	0.875
0.3171	29.9487	292	0.3492	0.8603
0.3249	30.9744	302	0.3400	0.875
0.3087	32.0	312	0.3251	0.875
0.3029	32.9231	321	0.3167	0.8824
0.3018	33.9487	331	0.3192	0.875
0.2823	34.9744	341	0.3066	0.875
0.2744	36.0	351	0.3003	0.875
0.258	36.9231	360	0.2964	0.875
0.2714	37.9487	370	0.3039	0.875
0.2486	38.9744	380	0.2937	0.875
0.2511	40.0	390	0.2739	0.8824
0.2511	40.9231	399	0.2836	0.8897
0.2659	41.9487	409	0.2804	0.875
0.2379	42.9744	419	0.2747	0.8824
0.2279	44.0	429	0.2726	0.8897
0.2153	44.9231	438	0.2732	0.8897
0.2461	45.9487	448	0.2738	0.8897
0.2482	46.1538	450	0.2738	0.8897

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1