hands_palms_classifier_resize

DownwardSpiral33/hands_palms_classifier_resize

hands_palms_classifier_resize is a image classification model from DownwardSpiral33. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Dec 5, 2023
Updated: Dec 5, 2023
SHA: d62408e5

Base models

google/vit-base-patch16-224-in21k

Model card

From the Hugging Face model README

DownwardSpiral33/hands_palms_classifier_resize

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

Train Loss: 0.0501
Validation Loss: 1.0093
Train Accuracy: 0.6774
Epoch: 49

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:

optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 17400, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Epoch
0.6821	0.6453	0.6452	0
0.6637	0.6372	0.6452	1
0.6512	0.6279	0.6452	2
0.6342	0.6190	0.7097	3
0.6092	0.6068	0.7097	4
0.5795	0.5944	0.7742	5
0.5557	0.5840	0.7742	6
0.5159	0.5725	0.7419	7
0.4782	0.5667	0.7419	8
0.4409	0.5991	0.6774	9
0.4026	0.6284	0.6129	10
0.3717	0.6061	0.6774	11
0.3223	0.6154	0.6452	12
0.2490	0.6326	0.7419	13
0.2393	0.6448	0.6452	14
0.1971	0.6517	0.6774	15
0.1856	0.6966	0.5806	16
0.1828	0.7499	0.6774	17
0.1416	0.6842	0.7097	18
0.1379	0.6103	0.7742	19
0.1395	0.7928	0.6774	20
0.1494	0.6574	0.7097	21
0.1075	0.6905	0.7097	22
0.0969	0.6674	0.7097	23
0.1016	0.8444	0.6452	24
0.0974	0.9260	0.6129	25
0.1400	0.8985	0.6774	26
0.0977	0.9014	0.5806	27
0.0759	0.8310	0.6452	28
0.0683	0.8327	0.6774	29
0.0670	0.8247	0.7097	30
0.0692	0.8734	0.6452	31
0.0701	0.8369	0.7097	32
0.0605	1.0525	0.6129	33
0.0847	0.9439	0.5806	34
0.0515	0.8494	0.6774	35
0.0584	0.9270	0.5806	36
0.0623	1.0442	0.5806	37
0.0532	1.0707	0.6129	38
0.0596	0.9569	0.6452	39
0.0529	0.9502	0.7097	40
0.0451	0.9873	0.6452	41
0.0465	1.0143	0.6774	42
0.0426	1.0763	0.6452	43
0.0445	1.0282	0.6452	44
0.0644	1.0912	0.6129	45
0.1811	0.9771	0.6774	46
0.1052	1.0571	0.6452	47
0.0741	1.1330	0.5484	48
0.0501	1.0093	0.6774	49

Framework versions

Transformers 4.35.2
TensorFlow 2.14.0
Datasets 2.15.0
Tokenizers 0.15.0

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Dec 5, 2023
Updated: Dec 5, 2023