🧠
Detr Resnet 50 Finetuned Cppe5 model by Elisa
⭐ 27.6
🧠
Model

Detr Resnet 50 Finetuned Cppe5

by Elisa elisa/detr-resnet-50_finetuned_cppe5
Free2AITools Nexus Index
27.6
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 3
R: Recency 16
Q: Quality 65
Tech Context
0.04B Params
4.096K Ctx
Vital Performance
25 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 27.6 FNI Score
Tiny 0.04B Params
4k Context
25 Downloads
8G GPU ~2GB Est. VRAM
Dense DETRFOROBJECTDETECTION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID elisa/detr-resnet-50_finetuned_cppe5
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.3GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{elisa_detr_resnet_50_finetuned_cppe5,
  author = {Elisa},
  title = {Detr Resnet 50 Finetuned Cppe5 Model},
  year = {2024},
  howpublished = {\url{https://huggingface.co/Elisa/detr-resnet-50_finetuned_cppe5}},
  note = {Accessed via Free2AITools.}
}
APA Style
Elisa. (2024). Detr Resnet 50 Finetuned Cppe5 [Model]. Free2AITools. https://huggingface.co/Elisa/detr-resnet-50_finetuned_cppe5

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run detr-resnet-50_finetuned_cppe5
πŸ€— HF Download
huggingface-cli download elisa/detr-resnet-50_finetuned_cppe5
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 3
Recency (R) 16
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Detr Resnet 50 Finetuned Cppe5: Authority (A:0), Popularity (P:3), Recency (R:16), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Deployment reference

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Technical Deep Dive

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.

Social Proof

HuggingFace Hub
25Downloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--elisa--detr-resnet-50_finetuned_cppe5
slug
elisa--detr-resnet-50_finetuned_cppe5
source
huggingface
author
Elisa
license
Apache-2.0
tags
transformers, tensorboard, safetensors, detr, object-detection, generated_from_trainer, base_model:facebook/detr-resnet-50, base_model:finetune:facebook/detr-resnet-50, license:apache-2.0, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
DetrForObjectDetection
params billions
0.04
context length
4,096
pipeline tag
object-detection
vram gb
1.3
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
25
stars
0
forks
0

Data indexed from public sources. Updated daily.