README.md

metadata

license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: finetuned-ConvNext-Indian-food
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: indian_food_images
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9107332624867163

finetuned-ConvNext-Indian-food

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

• Loss: 0.2977
• Accuracy: 0.9107

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: 0.0002
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.3145	0.3	100	1.0460	0.8151
0.6694	0.6	200	0.5439	0.8757
0.5057	0.9	300	0.4398	0.8831
0.4381	1.2	400	0.4286	0.8820
0.4376	1.5	500	0.3400	0.9044
0.2499	1.8	600	0.3312	0.9065
0.2802	2.1	700	0.3338	0.9033
0.3014	2.4	800	0.3572	0.8948
0.2508	2.7	900	0.3432	0.9022
0.2012	3.0	1000	0.3060	0.9086
0.2634	3.3	1100	0.3451	0.9086
0.2483	3.6	1200	0.3550	0.9044
0.2273	3.9	1300	0.2977	0.9107
0.1214	4.2	1400	0.3265	0.9160
0.2048	4.5	1500	0.3126	0.9214
0.0997	4.8	1600	0.3164	0.9160
0.1145	5.11	1700	0.3055	0.9139
0.1578	5.41	1800	0.3195	0.9171
0.0615	5.71	1900	0.3401	0.9107
0.1537	6.01	2000	0.3428	0.9097
0.1278	6.31	2100	0.3058	0.9192
0.1274	6.61	2200	0.3189	0.9192
0.0877	6.91	2300	0.3370	0.9182
0.1058	7.21	2400	0.3225	0.9192
0.1742	7.51	2500	0.3341	0.9214
0.0949	7.81	2600	0.3126	0.9256
0.1732	8.11	2700	0.3078	0.9235
0.0894	8.41	2800	0.3098	0.9267
0.1257	8.71	2900	0.3030	0.9320
0.1747	9.01	3000	0.3106	0.9256
0.2119	9.31	3100	0.3037	0.9299
0.1074	9.61	3200	0.3049	0.9277
0.1275	9.91	3300	0.3046	0.9309

Framework versions

• Transformers 4.22.2
• Pytorch 1.12.1+cu113
• Datasets 2.5.1
• Tokenizers 0.12.1