detr_finetuned_trashify_box_detector

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

• Loss: 1.2291

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: 0.0001
• 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
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
137.9231	1.0	17	125.8028
84.2349	2.0	34	40.2665
19.7645	3.0	51	7.3174
5.1905	4.0	68	3.5275
3.2078	5.0	85	2.6258
2.5905	6.0	102	2.2457
2.3692	7.0	119	2.3092
2.2272	8.0	136	1.9838
2.1079	9.0	153	1.8935
2.0051	10.0	170	1.7939
1.8845	11.0	187	1.7864
1.8544	12.0	204	1.6802
1.7695	13.0	221	1.5136
1.6797	14.0	238	1.5392
1.5638	15.0	255	1.4511
1.5424	16.0	272	1.4155
1.4715	17.0	289	1.4013
1.4396	18.0	306	1.3514
1.4471	19.0	323	1.3247
1.3963	20.0	340	1.2975
1.3261	21.0	357	1.2779
1.2633	22.0	374	1.3002
1.2494	23.0	391	1.2980
1.2512	24.0	408	1.2877
1.2433	25.0	425	1.2777
1.2116	26.0	442	1.2703
1.2489	27.0	459	1.2643
1.2316	28.0	476	1.2370
1.1575	29.0	493	1.2619
1.1525	30.0	510	1.1912
1.1134	31.0	527	1.2454
1.1722	32.0	544	1.2335
1.1395	33.0	561	1.2095
1.1626	34.0	578	1.2022
1.0886	35.0	595	1.1997
1.0653	36.0	612	1.2023
1.0653	37.0	629	1.1955
1.0421	38.0	646	1.2127
1.0476	39.0	663	1.1780
1.0415	40.0	680	1.2002
1.0107	41.0	697	1.1691
0.9861	42.0	714	1.2002
1.0084	43.0	731	1.1759
1.0171	44.0	748	1.1726
0.9977	45.0	765	1.1668
0.9553	46.0	782	1.2050
0.9872	47.0	799	1.1835
0.9529	48.0	816	1.2004
0.9669	49.0	833	1.1595
0.9669	50.0	850	1.1474
0.9218	51.0	867	1.1900
0.9517	52.0	884	1.1845
0.9665	53.0	901	1.1831
0.8924	54.0	918	1.2106
0.9008	55.0	935	1.1995
0.8584	56.0	952	1.2195
0.9453	57.0	969	1.2342
0.915	58.0	986	1.2210
0.9046	59.0	1003	1.2073
0.8851	60.0	1020	1.1895
0.9139	61.0	1037	1.2091
0.8388	62.0	1054	1.2165
0.8799	63.0	1071	1.2143
0.8534	64.0	1088	1.2154
0.8412	65.0	1105	1.2134
0.8305	66.0	1122	1.2277
0.8485	67.0	1139	1.2750
0.8177	68.0	1156	1.2791
0.8266	69.0	1173	1.2348
0.8448	70.0	1190	1.2564
0.8392	71.0	1207	1.2211
0.8007	72.0	1224	1.2272
0.8609	73.0	1241	1.2467
0.838	74.0	1258	1.2389
0.8095	75.0	1275	1.2609
0.7726	76.0	1292	1.2399
0.7956	77.0	1309	1.2718
0.8253	78.0	1326	1.2352
0.7387	79.0	1343	1.2423
0.8324	80.0	1360	1.2692
0.77	81.0	1377	1.2384
0.8133	82.0	1394	1.2354
0.7822	83.0	1411	1.2231
0.7512	84.0	1428	1.2178
0.7264	85.0	1445	1.2305
0.7814	86.0	1462	1.2202
0.7954	87.0	1479	1.2102
0.7679	88.0	1496	1.2158
0.7577	89.0	1513	1.2429
0.7122	90.0	1530	1.2462
0.7859	91.0	1547	1.2340
0.7276	92.0	1564	1.2447
0.7651	93.0	1581	1.2286
0.743	94.0	1598	1.2402
0.7813	95.0	1615	1.2276
0.6978	96.0	1632	1.2364
0.6845	97.0	1649	1.2392
0.7037	98.0	1666	1.2367
0.7468	99.0	1683	1.2323
0.7241	100.0	1700	1.2291

Framework versions

• Transformers 4.44.0
• Pytorch 2.2.0+cu121
• Datasets 2.20.0
• Tokenizers 0.19.1