metadata

license: apache-2.0
base_model: facebook/convnextv2-large-1k-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: convnextv2-large-1k-224-finetuned-cassava-leaf-disease
    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: 0.8691588785046729

convnextv2-large-1k-224-finetuned-cassava-leaf-disease

This model is a fine-tuned version of facebook/convnextv2-large-1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.4210
Accuracy: 0.8692

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: 240
eval_batch_size: 240
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 960
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
8.2962	0.49	10	5.4110	0.0033
3.1666	0.99	20	2.0615	0.5883
1.4693	1.48	30	1.0935	0.6084
0.8718	1.98	40	0.7291	0.7463
0.6252	2.47	50	0.5894	0.7916
0.5198	2.96	60	0.5204	0.8299
0.4517	3.46	70	0.4658	0.8393
0.4266	3.95	80	0.4664	0.8407
0.4049	4.44	90	0.4337	0.8579
0.3817	4.94	100	0.4247	0.8523
0.3696	5.43	110	0.4146	0.8621
0.3577	5.93	120	0.4058	0.8607
0.3577	6.42	130	0.4047	0.8636
0.3354	6.91	140	0.3985	0.8617
0.3356	7.41	150	0.4025	0.8645
0.3286	7.9	160	0.4054	0.8673
0.3225	8.4	170	0.4062	0.8631
0.317	8.89	180	0.4007	0.8692
0.3101	9.38	190	0.3931	0.8701
0.293	9.88	200	0.3928	0.8682
0.2992	10.37	210	0.3942	0.8668
0.2968	10.86	220	0.3892	0.8692
0.2794	11.36	230	0.3988	0.8701
0.2707	11.85	240	0.3865	0.8762
0.2883	12.35	250	0.4040	0.8640
0.2784	12.84	260	0.3930	0.8692
0.2667	13.33	270	0.3985	0.8701
0.2642	13.83	280	0.4160	0.8668
0.2612	14.32	290	0.4086	0.8687
0.2586	14.81	300	0.3990	0.8668
0.2483	15.31	310	0.4111	0.8720
0.254	15.8	320	0.4082	0.8748
0.2283	16.3	330	0.4165	0.8668
0.246	16.79	340	0.4264	0.8692
0.2365	17.28	350	0.4185	0.8692
0.2388	17.78	360	0.4152	0.8650
0.2401	18.27	370	0.4169	0.8659
0.2334	18.77	380	0.4187	0.8696
0.2245	19.26	390	0.4192	0.8692
0.2291	19.75	400	0.4210	0.8692

Framework versions

Transformers 4.37.2
Pytorch 2.2.1
Datasets 2.18.0
Tokenizers 0.15.1