metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - image-classification
  - bird species identification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: image_classification_obipix_birdID
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: private crawled images
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9719696025912545

image_classification_obipix_birdID

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the private crawled images dataset. It achieves the following results on the evaluation set:

Loss: 0.1150
Accuracy: 0.9720

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: 64
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
6.9257	0.18	1000	5.3830	0.1638
3.9727	0.35	2000	2.7695	0.4797
2.057	0.53	3000	1.5070	0.6936
1.2103	0.7	4000	0.9727	0.7842
0.8513	0.88	5000	0.7101	0.8318
0.5836	1.06	6000	0.5797	0.8561
0.3545	1.23	7000	0.5066	0.8730
0.314	1.41	8000	0.4521	0.8818
0.2858	1.58	9000	0.3915	0.8960
0.2482	1.76	10000	0.3564	0.9056
0.2192	1.93	11000	0.3131	0.9148
0.1271	2.11	12000	0.2916	0.9207
0.0779	2.29	13000	0.2727	0.9260
0.0749	2.46	14000	0.2597	0.9309
0.0682	2.64	15000	0.2415	0.9355
0.0615	2.81	16000	0.2268	0.9385
0.0566	2.99	17000	0.2084	0.9440
0.0197	3.17	18000	0.1951	0.9475
0.0158	3.34	19000	0.1843	0.9513
0.0145	3.52	20000	0.1746	0.9541
0.0118	3.69	21000	0.1649	0.9573
0.0103	3.87	22000	0.1531	0.9599
0.006	4.05	23000	0.1379	0.9644
0.0016	4.22	24000	0.1316	0.9668
0.0013	4.4	25000	0.1265	0.9686
0.0014	4.57	26000	0.1232	0.9697
0.0009	4.75	27000	0.1189	0.9712
0.001	4.92	28000	0.1150	0.9720

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.0