hbenitez/AV_classifier_resnet50

This model is a fine-tuned version of microsoft/resnet-50 on an unknown dataset. It achieves the following results on the evaluation set:

• Train Loss: 0.8261
• Validation Loss: 2.4425
• Train Accuracy: 0.6
• Epoch: 99

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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 8000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
• training_precision: float32

Training results

Train Loss	Validation Loss	Train Accuracy	Epoch
8.2326	8.1060	0.0	0
8.3420	7.6394	0.05	1
7.9643	7.5706	0.05	2
7.9337	7.6265	0.05	3
7.8018	7.7736	0.05	4
7.8009	7.7905	0.05	5
7.6369	7.6354	0.05	6
7.4782	7.5608	0.05	7
7.3655	7.6271	0.05	8
7.2886	7.6028	0.0	9
7.1145	7.5211	0.0	10
7.1232	7.2993	0.0	11
6.8393	7.2079	0.0	12
6.8202	7.2143	0.0	13
6.7180	7.1236	0.05	14
6.7318	7.1061	0.0	15
6.4563	6.9758	0.05	16
6.3765	6.9413	0.05	17
6.1791	6.8315	0.05	18
6.1946	6.7703	0.05	19
5.8448	6.7431	0.1	20
5.8514	6.6876	0.1	21
5.8200	6.6353	0.05	22
5.8323	6.5814	0.05	23
5.5553	6.4306	0.05	24
5.4999	6.4455	0.05	25
5.4370	6.3026	0.05	26
5.2288	6.0093	0.1	27
5.2173	6.0593	0.05	28
5.2280	6.0598	0.05	29
5.0484	5.9769	0.05	30
4.8703	5.8336	0.05	31
4.9881	5.7711	0.1	32
4.5905	5.6685	0.1	33
4.7240	5.6156	0.15	34
4.5095	5.4680	0.15	35
4.2225	5.3962	0.15	36
4.3615	5.3290	0.2	37
4.1862	5.3602	0.15	38
3.9455	5.2635	0.15	39
3.9737	5.2337	0.15	40
4.0922	5.1268	0.15	41
3.6042	4.9972	0.2	42
3.7219	4.8787	0.15	43
3.5563	4.9075	0.2	44
3.5897	4.9157	0.25	45
3.5769	4.7936	0.25	46
3.6225	4.8689	0.2	47
3.4568	4.8767	0.2	48
3.1431	4.7520	0.25	49
3.0607	4.5815	0.3	50
2.8904	4.5007	0.2	51
2.8308	4.5054	0.25	52
2.8136	4.2745	0.25	53
2.6192	4.3300	0.2	54
2.5308	4.3180	0.2	55
2.5192	4.2706	0.2	56
2.5761	4.1395	0.25	57
2.3516	3.9031	0.3	58
2.3231	3.8172	0.35	59
2.2735	3.7651	0.35	60
2.1215	3.8034	0.35	61
2.3229	3.8096	0.35	62
2.2230	3.7000	0.35	63
1.9059	3.6666	0.25	64
2.0289	3.6743	0.25	65
1.9178	3.5819	0.3	66
2.0295	3.5087	0.35	67
1.6499	3.4962	0.4	68
1.6261	3.4146	0.3	69
1.7059	3.4097	0.35	70
1.4837	3.2702	0.35	71
1.3766	3.2214	0.4	72
1.5898	3.2674	0.4	73
1.5002	3.1907	0.4	74
1.2641	3.1176	0.4	75
1.3456	3.1562	0.4	76
1.2655	2.9548	0.5	77
1.5449	2.8738	0.5	78
1.2519	2.8336	0.45	79
1.0682	2.8478	0.35	80
1.1891	2.8408	0.5	81
1.2920	2.6254	0.5	82
1.1239	2.7507	0.5	83
1.0857	2.7772	0.4	84
0.9821	2.8372	0.45	85
1.0457	2.8636	0.45	86
1.1419	2.8426	0.45	87
1.0782	2.7856	0.5	88
0.9906	2.6826	0.55	89
1.0766	2.6707	0.5	90
1.1115	2.6457	0.5	91
1.2201	2.6838	0.55	92
0.8706	2.5262	0.55	93
0.7441	2.5422	0.55	94
0.9710	2.4211	0.6	95
0.9731	2.4090	0.6	96
0.8942	2.3773	0.6	97
1.0461	2.4159	0.55	98
0.8261	2.4425	0.6	99

Framework versions

• Transformers 4.30.2
• TensorFlow 2.13.0-rc2
• Datasets 2.13.1
• Tokenizers 0.13.3