README.md

metadata

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-eurosat-albumentations
    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: 0.5

convnext-tiny-224-finetuned-eurosat-albumentations

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: 1.3086
• Accuracy: 0.5

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	1.0	5	2.0663	0.1562
2.0637	2.0	10	2.0571	0.1812
2.0637	3.0	15	2.0389	0.1938
2.0295	4.0	20	2.0109	0.2437
2.0295	5.0	25	1.9754	0.2875
1.9452	6.0	30	1.9335	0.2938
1.9452	7.0	35	1.8869	0.275
1.8289	8.0	40	1.8378	0.2812
1.8289	9.0	45	1.7800	0.3563
1.7075	10.0	50	1.7231	0.3563
1.7075	11.0	55	1.6730	0.3625
1.5909	12.0	60	1.6253	0.3688
1.5909	13.0	65	1.5897	0.3875
1.4997	14.0	70	1.5604	0.4
1.4997	15.0	75	1.5336	0.425
1.4066	16.0	80	1.5147	0.425
1.4066	17.0	85	1.4923	0.425
1.3344	18.0	90	1.4744	0.4375
1.3344	19.0	95	1.4615	0.4437
1.2545	20.0	100	1.4479	0.4437
1.2545	21.0	105	1.4311	0.45
1.1789	22.0	110	1.4222	0.475
1.1789	23.0	115	1.4099	0.4813
1.1186	24.0	120	1.3926	0.4688
1.1186	25.0	125	1.3835	0.4625
1.0685	26.0	130	1.3747	0.4625
1.0685	27.0	135	1.3622	0.4625
0.9935	28.0	140	1.3523	0.4688
0.9935	29.0	145	1.3514	0.45
0.9453	30.0	150	1.3413	0.4688
0.9453	31.0	155	1.3334	0.45
0.9162	32.0	160	1.3239	0.45
0.9162	33.0	165	1.3177	0.475
0.8637	34.0	170	1.3090	0.475
0.8637	35.0	175	1.3078	0.4938
0.8298	36.0	180	1.3086	0.5
0.8298	37.0	185	1.2990	0.5
0.7801	38.0	190	1.2975	0.4938
0.7801	39.0	195	1.2946	0.4938
0.7691	40.0	200	1.2921	0.4875
0.7691	41.0	205	1.2913	0.4938
0.7409	42.0	210	1.2902	0.4875
0.7409	43.0	215	1.2886	0.4875
0.7223	44.0	220	1.2860	0.4938
0.7223	45.0	225	1.2849	0.4875
0.7091	46.0	230	1.2849	0.4875
0.7091	47.0	235	1.2854	0.4875
0.6915	48.0	240	1.2845	0.4875
0.6915	49.0	245	1.2842	0.4875
0.6917	50.0	250	1.2840	0.4875

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1