clip-finetuned-csu-p14-336-e4l58-l

This model is a fine-tuned version of openai/clip-vit-large-patch14-336 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.8028

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: 5e-08
• train_batch_size: 128
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.4667	0.0533	500	1.4426
0.4532	0.1067	1000	1.3816
0.3749	0.1600	1500	1.3311
0.336	0.2133	2000	1.2891
0.3585	0.2666	2500	1.2536
0.303	0.3200	3000	1.2203
0.3242	0.3733	3500	1.1956
0.2427	0.4266	4000	1.1694
0.2993	0.4799	4500	1.1456
0.3183	0.5333	5000	1.1201
0.307	0.5866	5500	1.0982
0.2638	0.6399	6000	1.0780
0.2226	0.6933	6500	1.0613
0.2453	0.7466	7000	1.0444
0.272	0.7999	7500	1.0301
0.283	0.8532	8000	1.0167
0.2331	0.9066	8500	1.0035
0.2362	0.9599	9000	0.9925
0.2396	1.0132	9500	0.9830
0.2013	1.0666	10000	0.9736
0.2082	1.1199	10500	0.9639
0.2023	1.1732	11000	0.9558
0.2331	1.2265	11500	0.9465
0.1784	1.2799	12000	0.9392
0.1953	1.3332	12500	0.9316
0.1867	1.3865	13000	0.9270
0.22	1.4398	13500	0.9197
0.1656	1.4932	14000	0.9148
0.1968	1.5465	14500	0.9096
0.1676	1.5998	15000	0.9057
0.2074	1.6532	15500	0.8994
0.1847	1.7065	16000	0.8954
0.1845	1.7598	16500	0.8900
0.1721	1.8131	17000	0.8873
0.2627	1.8665	17500	0.8810
0.1623	1.9198	18000	0.8774
0.2162	1.9731	18500	0.8713
0.1802	2.0265	19000	0.8679
0.179	2.0798	19500	0.8633
0.1549	2.1331	20000	0.8606
0.1742	2.1864	20500	0.8585
0.1448	2.2398	21000	0.8546
0.2066	2.2931	21500	0.8513
0.1483	2.3464	22000	0.8481
0.1813	2.3997	22500	0.8447
0.1617	2.4531	23000	0.8411
0.1664	2.5064	23500	0.8394
0.1786	2.5597	24000	0.8358
0.1465	2.6131	24500	0.8330
0.1289	2.6664	25000	0.8314
0.1662	2.7197	25500	0.8296
0.1463	2.7730	26000	0.8262
0.1471	2.8264	26500	0.8249
0.167	2.8797	27000	0.8219
0.1268	2.9330	27500	0.8204
0.177	2.9863	28000	0.8177
0.1206	3.0397	28500	0.8166
0.1345	3.0930	29000	0.8156
0.1907	3.1463	29500	0.8144
0.1395	3.1997	30000	0.8126
0.1511	3.2530	30500	0.8112
0.1334	3.3063	31000	0.8102
0.1799	3.3596	31500	0.8090
0.1289	3.4130	32000	0.8081
0.1545	3.4663	32500	0.8072
0.1705	3.5196	33000	0.8064
0.1424	3.5730	33500	0.8055
0.1873	3.6263	34000	0.8048
0.1432	3.6796	34500	0.8043
0.1485	3.7329	35000	0.8037
0.1286	3.7863	35500	0.8033
0.1469	3.8396	36000	0.8030
0.1708	3.8929	36500	0.8029
0.1226	3.9462	37000	0.8028
0.1549	3.9996	37500	0.8028

Framework versions

• Transformers 4.45.0.dev0
• Pytorch 1.12.1
• Datasets 2.21.0
• Tokenizers 0.19.1