This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 100 - mixedprecisiontraining: Native AMP - Transformers 4.57.6 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.22.2
Open-weight model · Object detection
furniture_data_finetuning_1
by KIM MINSEO kumumin/furniture_data_finetuning_1
furniture_data_finetuning_1 is an open-weight model for object detection from KIM MINSEO, released under Apache License 2.0. It has 42M parameters and a 1,024-token context. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset.
Parameters42M
Context1,024
Weights166.5 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve furniture_data_finetuning_1 (42M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
furniture_data_finetuning_1 on every accelerator the SAVRN Index prices, at every precision
Model Card
By KIM MINSEO, published under apache-2.0, revision 39dd3c8b1fb4.
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 100 - mixedprecisiontraining: Native AMP - Transformers 4.57.6 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.22.2
Read KIM MINSEO's full model card
This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset.
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: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2
Configuration
- Architecture
- DetrForObjectDetection
- Context length (tokens)
- 1,024
- Layers
- 6
- Model type
- detr
Identity and Version
- Repository
- kumumin/furniture_data_finetuning_1
- Publisher
- KIM MINSEO
- Task
- Object detection
- Modality
- Image
- Library
- transformers
- Parameters
- 42M parameters
- Languages
- Not stated by the source
- Revision
- 39dd3c8b1fb43e0c5c81412c01830a92bd1a5fe6
- First published
- 2026-10-11
- Last updated
- 2026-10-11
Files and Weights
6 files, 166.5 MB in total. The weights are 2 files totalling 166.5 MB in bin, safetensors.
Weights2 files · 166.5 MB
Configuration2 files · 1.9 KB
Documentation1 file · 1.2 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 166.5 MB | dd14d8f223cf |
| training_args.bin | Weights | 5.8 KB | 5fa0d91233fa |
| config.json | Configuration | 1.4 KB | — |
| preprocessor_config.json | Configuration | 455 B | — |
| README.md | Documentation | 1.2 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 166.5 MB
Download from KIM MINSEO
Released by KIM MINSEO through its official repository on Hugging Face. Read the license.
Built From
- Derived from facebook/detr-resnet-50
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 166.5 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About furniture_data_finetuning_1
How much GPU memory does furniture_data_finetuning_1 need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (42M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run furniture_data_finetuning_1 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use furniture_data_finetuning_1 commercially?
Yes. furniture_data_finetuning_1 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
What is furniture_data_finetuning_1's context length?
1,024 tokens, from the maximum position embeddings in its published configuration.
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