README.md

metadata

license: apache-2.0
base_model: facebook/dinov2-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: dinov2-large-2024_05_23-drone_batch-size512_epochs50_freeze
    results: []

dinov2-large-2024_05_23-drone_batch-size512_epochs50_freeze

This model is a fine-tuned version of facebook/dinov2-large on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2361
• F1 Micro: 0.7694
• F1 Macro: 0.4048
• Roc Auc: 0.8448
• Accuracy: 0.1449
• 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: 512
• eval_batch_size: 512
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	F1 Macro	Roc Auc	Accuracy	Rate
No log	1.0	28	0.5952	0.5739	0.4067	0.7528	0.0124	0.001
No log	2.0	56	0.4730	0.7307	0.4368	0.8401	0.0698	0.001
No log	3.0	84	0.3240	0.7499	0.3770	0.8378	0.1074	0.001
No log	4.0	112	0.2770	0.7521	0.3710	0.8372	0.1180	0.001
No log	5.0	140	0.2588	0.7507	0.3715	0.8353	0.1196	0.001
No log	6.0	168	0.2533	0.7520	0.3630	0.8354	0.1218	0.001
No log	7.0	196	0.2513	0.7517	0.3646	0.8347	0.1153	0.001
No log	8.0	224	0.2508	0.7576	0.3894	0.8407	0.1228	0.001
No log	9.0	252	0.2479	0.7550	0.3829	0.8360	0.1275	0.001
No log	10.0	280	0.2481	0.7583	0.3797	0.8407	0.1265	0.001
No log	11.0	308	0.2467	0.7601	0.3964	0.8431	0.1243	0.001
No log	12.0	336	0.2460	0.7565	0.3733	0.8362	0.1251	0.001
No log	13.0	364	0.2456	0.7582	0.3862	0.8399	0.1298	0.001
No log	14.0	392	0.2465	0.7526	0.3708	0.8323	0.1371	0.001
No log	15.0	420	0.2452	0.7541	0.3795	0.8344	0.1271	0.001
No log	16.0	448	0.2437	0.7597	0.3904	0.8409	0.1293	0.001
No log	17.0	476	0.2447	0.7526	0.3854	0.8317	0.1316	0.001
0.3126	18.0	504	0.2454	0.7534	0.3578	0.8326	0.1332	0.001
0.3126	19.0	532	0.2441	0.7568	0.3694	0.8367	0.1324	0.001
0.3126	20.0	560	0.2454	0.7509	0.3768	0.8288	0.1361	0.001
0.3126	21.0	588	0.2438	0.7602	0.3896	0.8416	0.1249	0.001
0.3126	22.0	616	0.2419	0.7576	0.3716	0.8368	0.1302	0.001
0.3126	23.0	644	0.2435	0.7629	0.3880	0.8454	0.1265	0.001
0.3126	24.0	672	0.2413	0.7561	0.3897	0.8344	0.1342	0.001
0.3126	25.0	700	0.2419	0.7599	0.3827	0.8415	0.1298	0.001
0.3126	26.0	728	0.2438	0.7593	0.3971	0.8401	0.1267	0.001
0.3126	27.0	756	0.2418	0.7614	0.3838	0.8422	0.1310	0.001
0.3126	28.0	784	0.2432	0.7498	0.3793	0.8275	0.1334	0.001
0.3126	29.0	812	0.2420	0.7622	0.3960	0.8436	0.1367	0.001
0.3126	30.0	840	0.2407	0.7620	0.3860	0.8404	0.1424	0.001
0.3126	31.0	868	0.2422	0.7612	0.3929	0.8429	0.1328	0.001
0.3126	32.0	896	0.2430	0.7516	0.3912	0.8298	0.1312	0.001
0.3126	33.0	924	0.2414	0.7589	0.3884	0.8388	0.1302	0.001
0.3126	34.0	952	0.2404	0.7625	0.4037	0.8419	0.1354	0.001
0.3126	35.0	980	0.2413	0.7602	0.3973	0.8400	0.1300	0.001
0.2465	36.0	1008	0.2419	0.7622	0.3876	0.8436	0.1357	0.001
0.2465	37.0	1036	0.2399	0.7598	0.3992	0.8381	0.1342	0.001
0.2465	38.0	1064	0.2400	0.7607	0.3933	0.8397	0.1330	0.001
0.2465	39.0	1092	0.2409	0.7619	0.4008	0.8412	0.1389	0.001
0.2465	40.0	1120	0.2399	0.76	0.3925	0.8378	0.1354	0.001
0.2465	41.0	1148	0.2423	0.7640	0.4061	0.8464	0.1249	0.001
0.2465	42.0	1176	0.2426	0.7569	0.4005	0.8378	0.1310	0.001
0.2465	43.0	1204	0.2392	0.7594	0.4008	0.8369	0.1336	0.001
0.2465	44.0	1232	0.2418	0.7577	0.4064	0.8365	0.1304	0.001
0.2465	45.0	1260	0.2411	0.7591	0.3906	0.8384	0.1379	0.001
0.2465	46.0	1288	0.2396	0.7654	0.4106	0.8457	0.1363	0.001
0.2465	47.0	1316	0.2396	0.7575	0.3968	0.8349	0.1326	0.001
0.2465	48.0	1344	0.2423	0.7564	0.3878	0.8373	0.1287	0.001
0.2465	49.0	1372	0.2398	0.7608	0.4027	0.8390	0.1330	0.001
0.2465	50.0	1400	0.2367	0.7652	0.4087	0.8436	0.1424	0.0001

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu118
• Datasets 2.19.1
• Tokenizers 0.19.1