You can deploy this model and start earning money today!

sudo-s/
exper_batch_16_e4

Name: exper_batch_16_e4
Rating: 2.5 (5 reviews)

generated_from_trainer

apache-2.0

autotrain_compatible

endpoints_compatible

Views

No views yet

exper_batch_16_e4

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the sudo-s/herbier_mesuem1 dataset. It achieves the following results on the evaluation set:

Loss: 0.3598
Accuracy: 0.9059

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: 4
mixed_precision_training: Apex, opt level O1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.7606	0.16	100	3.7839	0.1989
3.1072	0.31	200	3.0251	0.3285
2.4068	0.47	300	2.4380	0.4719
2.0881	0.63	400	2.0489	0.5412
1.6817	0.78	500	1.7968	0.6025
1.342	0.94	600	1.5044	0.6249
0.9343	1.1	700	1.1881	0.7132
0.9552	1.25	800	1.1064	0.7224
0.7265	1.41	900	0.9189	0.7768
0.6732	1.56	1000	0.9227	0.7606
0.5587	1.72	1100	0.7912	0.7903
0.6332	1.88	1200	0.7606	0.7945
0.3188	2.03	1300	0.6535	0.8288
0.3079	2.19	1400	0.5686	0.8577
0.2518	2.35	1500	0.5517	0.8577
0.2	2.5	1600	0.5277	0.8631
0.2032	2.66	1700	0.4841	0.8701
0.1555	2.82	1800	0.4578	0.8793
0.145	2.97	1900	0.4466	0.8755
0.0985	3.13	2000	0.4249	0.8867
0.0955	3.29	2100	0.3977	0.8932
0.0438	3.44	2200	0.3785	0.9036
0.0589	3.6	2300	0.3717	0.9017
0.0709	3.76	2400	0.3609	0.9052
0.0706	3.91	2500	0.3598	0.9059

Framework versions

Transformers 4.19.4
Pytorch 1.5.1
Datasets 2.3.2
Tokenizers 0.12.1

sudo-s/exper_batch_16_e4

exper_batch_16_e4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sudo-s/
exper_batch_16_e4