README.md

metadata

license: apache-2.0
base_model: facebook/dinov2-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: >-
      drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_freeze
    results: []

drone-DinoVdeau-produttoria_binary-binary-large-2024_11_03-batch-size64_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: 0.2854
• F1 Micro: 0.8468
• F1 Macro: 0.6351
• Accuracy: 0.2786
• Learning Rate: 0.0000

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 150
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Micro	F1 Macro	Accuracy	Rate
No log	1.0	181	0.3236	0.8262	0.5774	0.2630	0.001
No log	2.0	362	0.3146	0.8379	0.6199	0.2412	0.001
0.3995	3.0	543	0.3090	0.8398	0.6044	0.2555	0.001
0.3995	4.0	724	0.3074	0.8349	0.6003	0.2562	0.001
0.3995	5.0	905	0.3039	0.8406	0.6248	0.2516	0.001
0.3299	6.0	1086	0.3060	0.8420	0.6225	0.2596	0.001
0.3299	7.0	1267	0.3014	0.8387	0.5955	0.2820	0.001
0.3299	8.0	1448	0.3013	0.8391	0.5975	0.2703	0.001
0.3216	9.0	1629	0.3010	0.8407	0.5974	0.2841	0.001
0.3216	10.0	1810	0.3007	0.8376	0.5938	0.2711	0.001
0.3216	11.0	1991	0.3036	0.8349	0.5762	0.2773	0.001
0.3167	12.0	2172	0.3013	0.8385	0.6115	0.2674	0.001
0.3167	13.0	2353	0.2978	0.8421	0.6146	0.2648	0.001
0.315	14.0	2534	0.2977	0.8400	0.6059	0.2734	0.001
0.315	15.0	2715	0.2981	0.8434	0.6075	0.2666	0.001
0.315	16.0	2896	0.2974	0.8394	0.5933	0.2747	0.001
0.3147	17.0	3077	0.2984	0.8438	0.6147	0.2664	0.001
0.3147	18.0	3258	0.3023	0.8356	0.5804	0.2763	0.001
0.3147	19.0	3439	0.2985	0.8424	0.6159	0.2739	0.001
0.3122	20.0	3620	0.2968	0.8412	0.5984	0.2807	0.001
0.3122	21.0	3801	0.3005	0.8419	0.6060	0.2703	0.001
0.3122	22.0	3982	0.2982	0.8375	0.5804	0.2747	0.001
0.3149	23.0	4163	0.2939	0.8436	0.6152	0.2781	0.001
0.3149	24.0	4344	0.2948	0.8453	0.6229	0.2760	0.001
0.3118	25.0	4525	0.2968	0.8427	0.6103	0.2737	0.001
0.3118	26.0	4706	0.2956	0.8421	0.6045	0.2755	0.001
0.3118	27.0	4887	0.2959	0.8438	0.6115	0.2765	0.001
0.3126	28.0	5068	0.2955	0.8447	0.6191	0.2693	0.001
0.3126	29.0	5249	0.3011	0.8438	0.6216	0.2664	0.001
0.3126	30.0	5430	0.2921	0.8437	0.6025	0.2810	0.0001
0.3093	31.0	5611	0.2904	0.8439	0.6072	0.2812	0.0001
0.3093	32.0	5792	0.2903	0.8437	0.6112	0.2810	0.0001
0.3093	33.0	5973	0.2889	0.8462	0.6202	0.2854	0.0001
0.3049	34.0	6154	0.2896	0.8446	0.6151	0.2862	0.0001
0.3049	35.0	6335	0.2887	0.8449	0.6112	0.2867	0.0001
0.3012	36.0	6516	0.2889	0.8447	0.6120	0.2836	0.0001
0.3012	37.0	6697	0.2883	0.8476	0.6256	0.2867	0.0001
0.3012	38.0	6878	0.2905	0.8453	0.6057	0.2825	0.0001
0.299	39.0	7059	0.2878	0.8471	0.6254	0.2854	0.0001
0.299	40.0	7240	0.2886	0.8468	0.6223	0.2810	0.0001
0.299	41.0	7421	0.2877	0.8473	0.6261	0.2843	0.0001
0.2989	42.0	7602	0.2878	0.8477	0.6199	0.2856	0.0001
0.2989	43.0	7783	0.2872	0.8479	0.6288	0.2830	0.0001
0.2989	44.0	7964	0.2868	0.8464	0.6190	0.2841	0.0001
0.2983	45.0	8145	0.2870	0.8463	0.6236	0.2838	0.0001
0.2983	46.0	8326	0.2868	0.8460	0.6151	0.2825	0.0001
0.298	47.0	8507	0.2872	0.8462	0.6211	0.2846	0.0001
0.298	48.0	8688	0.2866	0.8467	0.6231	0.2836	0.0001
0.298	49.0	8869	0.2863	0.8460	0.6161	0.2859	0.0001
0.2965	50.0	9050	0.2864	0.8483	0.6255	0.2846	0.0001
0.2965	51.0	9231	0.2891	0.8486	0.6278	0.2849	0.0001
0.2965	52.0	9412	0.2856	0.8464	0.6255	0.2851	0.0001
0.2956	53.0	9593	0.2872	0.8490	0.6458	0.2789	0.0001
0.2956	54.0	9774	0.2856	0.8477	0.6244	0.2903	0.0001
0.2956	55.0	9955	0.2857	0.8475	0.6340	0.2846	0.0001
0.2958	56.0	10136	0.2862	0.8466	0.6241	0.2867	0.0001
0.2958	57.0	10317	0.2871	0.8454	0.6249	0.2862	0.0001
0.2958	58.0	10498	0.2858	0.8492	0.6334	0.2812	0.0001
0.2954	59.0	10679	0.2862	0.8468	0.6178	0.2888	1e-05
0.2954	60.0	10860	0.2847	0.8485	0.6276	0.2854	1e-05
0.2923	61.0	11041	0.2849	0.8480	0.6224	0.2830	1e-05
0.2923	62.0	11222	0.2855	0.8469	0.6248	0.2843	1e-05
0.2923	63.0	11403	0.2849	0.8489	0.6275	0.2828	1e-05
0.2918	64.0	11584	0.2846	0.8475	0.6371	0.2823	1e-05
0.2918	65.0	11765	0.2860	0.8468	0.6241	0.2869	1e-05
0.2918	66.0	11946	0.2847	0.8481	0.6347	0.2841	1e-05
0.2906	67.0	12127	0.2853	0.8488	0.6287	0.2854	1e-05
0.2906	68.0	12308	0.2853	0.8480	0.6321	0.2867	1e-05
0.2906	69.0	12489	0.2848	0.8477	0.6397	0.2836	1e-05
0.2918	70.0	12670	0.2853	0.8492	0.6381	0.2823	1e-05
0.2918	71.0	12851	0.2851	0.8476	0.6325	0.2882	0.0000
0.2918	72.0	13032	0.2845	0.8474	0.6236	0.2849	0.0000
0.2918	73.0	13213	0.2845	0.8476	0.6333	0.2812	0.0000
0.2918	74.0	13394	0.2845	0.8466	0.6300	0.2828	0.0000
0.2913	75.0	13575	0.2851	0.8474	0.6235	0.2820	0.0000
0.2913	76.0	13756	0.2860	0.8473	0.6186	0.2880	0.0000
0.2913	77.0	13937	0.2858	0.8459	0.6173	0.2856	0.0000
0.2913	78.0	14118	0.2844	0.8481	0.6326	0.2843	0.0000
0.2913	79.0	14299	0.2871	0.8472	0.6179	0.2875	0.0000
0.2913	80.0	14480	0.2848	0.8477	0.6287	0.2838	0.0000
0.2915	81.0	14661	0.2848	0.8490	0.6305	0.2854	0.0000
0.2915	82.0	14842	0.2851	0.8480	0.6394	0.2859	0.0000
0.2913	83.0	15023	0.2846	0.8488	0.6255	0.2856	0.0000
0.2913	84.0	15204	0.2857	0.8482	0.6458	0.2833	0.0000
0.2913	85.0	15385	0.2855	0.8488	0.6340	0.2812	0.0000
0.2922	86.0	15566	0.2849	0.8480	0.6363	0.2859	0.0000
0.2922	87.0	15747	0.2845	0.8474	0.6328	0.2851	0.0000
0.2922	88.0	15928	0.2854	0.8478	0.6371	0.2812	0.0000

Framework versions

• Transformers 4.41.0
• Pytorch 2.5.0+cu124
• Datasets 3.0.2
• Tokenizers 0.19.1