metadata

library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-50
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: Resneteau-50-2024_09_23-batch-size32_freeze
    results: []

Resneteau-50-2024_09_23-batch-size32_freeze

This model is a fine-tuned version of microsoft/resnet-50 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1906
F1 Micro: 0.6954
F1 Macro: 0.4462
Accuracy: 0.1827
Learning Rate: 0.0001

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 400
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	F1 Macro	Accuracy	Rate
No log	1.0	273	0.2460	0.5802	0.2267	0.0877	0.001
0.2786	2.0	546	0.2217	0.6412	0.3160	0.1369	0.001
0.2786	3.0	819	0.2117	0.6596	0.3581	0.1486	0.001
0.231	4.0	1092	0.2049	0.6674	0.3831	0.1618	0.001
0.231	5.0	1365	0.2016	0.6707	0.3965	0.1677	0.001
0.2206	6.0	1638	0.2002	0.6720	0.4076	0.1677	0.001
0.2206	7.0	1911	0.1976	0.6752	0.4142	0.1746	0.001
0.2157	8.0	2184	0.1971	0.6824	0.4281	0.1764	0.001
0.2157	9.0	2457	0.1961	0.6845	0.4300	0.1764	0.001
0.2127	10.0	2730	0.1944	0.6763	0.4264	0.1805	0.001
0.2117	11.0	3003	0.1940	0.6902	0.4391	0.1781	0.001
0.2117	12.0	3276	0.1945	0.6939	0.4523	0.1729	0.001
0.2107	13.0	3549	0.1936	0.6908	0.4461	0.1795	0.001
0.2107	14.0	3822	0.1931	0.6916	0.4424	0.1781	0.001
0.2105	15.0	4095	0.1935	0.6936	0.4431	0.1809	0.001
0.2105	16.0	4368	0.1931	0.6896	0.4429	0.1805	0.001
0.2086	17.0	4641	0.1931	0.6953	0.4411	0.1819	0.001
0.2086	18.0	4914	0.1908	0.6984	0.4490	0.1857	0.001
0.2101	19.0	5187	0.1925	0.6879	0.4428	0.1812	0.001
0.2101	20.0	5460	0.1913	0.6797	0.4357	0.1774	0.001
0.2088	21.0	5733	0.1915	0.6958	0.4381	0.1823	0.001
0.2084	22.0	6006	0.1919	0.7039	0.4535	0.1826	0.001
0.2084	23.0	6279	0.1926	0.6907	0.4363	0.1798	0.001
0.2083	24.0	6552	0.1919	0.6953	0.4544	0.1805	0.001
0.2083	25.0	6825	0.1919	0.6962	0.4466	0.1781	0.0001
0.2076	26.0	7098	0.1912	0.6943	0.4418	0.1823	0.0001
0.2076	27.0	7371	0.1912	0.6972	0.4500	0.1809	0.0001
0.2081	28.0	7644	0.1915	0.6944	0.4454	0.1857	0.0001

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.0
Tokenizers 0.19.1